Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convol...Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural Networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low accuracy.Therefore,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 dataset.The hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 dataset.The evaluation accuracy of the new dataset GINKS2023 created in this paper is 0.9386.Compared to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.展开更多
Nowadays,abnormal traffic detection for Software-Defined Networking(SDN)faces the challenges of large data volume and high dimensionality.Since traditional machine learning-based detection methods have the problem of ...Nowadays,abnormal traffic detection for Software-Defined Networking(SDN)faces the challenges of large data volume and high dimensionality.Since traditional machine learning-based detection methods have the problem of data redundancy,the Metaheuristic Algorithm(MA)is introduced to select features beforemachine learning to reduce the dimensionality of data.Since a Tyrannosaurus Optimization Algorithm(TROA)has the advantages of few parameters,simple implementation,and fast convergence,and it shows better results in feature selection,TROA can be applied to abnormal traffic detection for SDN.However,TROA suffers frominsufficient global search capability,is easily trapped in local optimums,and has poor search accuracy.Then,this paper tries to improve TROA,namely the Improved Tyrannosaurus Optimization Algorithm(ITROA).It proposes a metaheuristic-driven abnormal traffic detection model for SDN based on ITROA.Finally,the validity of the ITROA is verified by the benchmark function and the UCI dataset,and the feature selection optimization operation is performed on the InSDN dataset by ITROA and other MAs to obtain the optimized feature subset for SDN abnormal traffic detection.The experiment shows that the performance of the proposed ITROA outperforms compared MAs in terms of the metaheuristic-driven model for SDN,achieving an accuracy of 99.37%on binary classification and 96.73%on multiclassification.展开更多
Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensi...Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensive applications in law enforcement and the commercial domain,and the rapid advancement of practical technologies.Despite the significant advancements,modern recognition algorithms still struggle in real-world conditions such as varying lighting conditions,occlusion,and diverse facial postures.In such scenarios,human perception is still well above the capabilities of present technology.Using the systematic mapping study,this paper presents an in-depth review of face detection algorithms and face recognition algorithms,presenting a detailed survey of advancements made between 2015 and 2024.We analyze key methodologies,highlighting their strengths and restrictions in the application context.Additionally,we examine various datasets used for face detection/recognition datasets focusing on the task-specific applications,size,diversity,and complexity.By analyzing these algorithms and datasets,this survey works as a valuable resource for researchers,identifying the research gap in the field of face detection and recognition and outlining potential directions for future research.展开更多
In smart driving for rail transit,a reliable obstacle detection system is an important guarantee for the safety of trains.Therein,the detection of the rail area directly affects the accuracy of the system to identify ...In smart driving for rail transit,a reliable obstacle detection system is an important guarantee for the safety of trains.Therein,the detection of the rail area directly affects the accuracy of the system to identify dangerous targets.Both the rail line and the lane are presented as thin line shapes in the image,but the rail scene is more complex,and the color of the rail line is more difficult to distinguish from the background.By comparison,there are already many deep learning-based lane detection algorithms,but there is a lack of public datasets and targeted deep learning detection algorithms for rail line detection.To address this,this paper constructs a rail image dataset RailwayLine and labels the rail line for the training and testing of models.This dataset contains rich rail images including single-rail,multi-rail,straight rail,curved rail,crossing rails,occlusion,blur,and different lighting conditions.To address the problem of the lack of deep learning-based rail line detection algorithms,we improve the CLRNet algorithm which has an excellent performance in lane detection,and propose the CLRNet-R algorithm for rail line detection.To address the problem of the rail line being thin and occupying fewer pixels in the image,making it difficult to distinguish from complex backgrounds,we introduce an attention mechanism to enhance global feature extraction ability and add a semantic segmentation head to enhance the features of the rail region by the binary probability of rail lines.To address the poor curve recognition performance and unsmooth output lines in the original CLRNet algorithm,we improve the weight allocation for line intersection-over-union calculation in the original framework and propose two loss functions based on local slopes to optimize the model’s local sampling point training constraints,improving the model’s fitting performance on curved rails and obtaining smooth and stable rail line detection results.Through experiments,this paper demonstrates that compared with other mainstream lane detection algorithms,the algorithm proposed in this paper has a better performance for rail line detection.展开更多
The excessive use of artificial intelligence(AI)algorithms has caused the problem of errors in AI algorithms,which has challenged the fairness of decision-making,and has intensified people’s inequality.Therefore,it i...The excessive use of artificial intelligence(AI)algorithms has caused the problem of errors in AI algorithms,which has challenged the fairness of decision-making,and has intensified people’s inequality.Therefore,it is necessary to conduct in-depth research and propose corresponding error detection and error elimination methods.This paper first proposes the root causes and threats of bias in AI algorithms,then summarizes the existing bias detection and error elimination methods,and proposes a bias processing framework in three-level dimensions of data,models,and conclusions,aiming to provide a framework for a comprehensive solution to errors in algorithms.At the same time,it also summarizes the problems and challenges in existing research and makes a prospect for future research trends.It is hoped that it will be helpful for us to build fairer AI.展开更多
Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of t...Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of the searched point to determine the next search point during the search process,reducing the uncertainty in the random search process.Due to the ability of the Bayesian algorithm to reduce uncertainty,a Bayesian ACO algorithm is proposed in this paper to increase the convergence speed of the conventional ACO algorithm for image edge detection.In addition,this paper has the following two innovations on the basis of the classical algorithm,one of which is to add random perturbations after completing the pheromone update.The second is the use of adaptive pheromone heuristics.Experimental results illustrate that the proposed Bayesian ACO algorithm has faster convergence and higher precision and recall than the traditional ant colony algorithm,due to the improvement of the pheromone utilization rate.Moreover,Bayesian ACO algorithm outperforms the other comparative methods in edge detection task.展开更多
Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is propose...Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is proposed based on the improved fully convolutional one-stage object detection(FCOS)algorithm.The regression performance of bounding boxes was optimized by introducing the complete intersection over union loss function into the improved algorithm.The feature fusion network structure is modified by adding adaptive fusion paths to the feature fusion network structure,which makes full use of the features of accurate localization and semantics of multi-scale feature fusion networks.Finally,the network structure was trained and validated by using the X-ray image dataset of damages in conveyor belts with steel rope cores provided by a flaw detection equipment manufacturer.In addition,the data enhancement methods such as rotating,mirroring,and scaling,were employed to enrich the image dataset so that the model is adequately trained.Experimental results showed that the improved FCOS algorithm promoted the precision rate and the recall rate by 20.9%and 14.8%respectively,compared with the original algorithm.Meanwhile,compared with Fast R-CNN,Faster R-CNN,SSD,and YOLOv3,the improved FCOS algorithm has obvious advantages;detection precision rate and recall rate of the modified network reached 95.8%and 97.0%respectively.Furthermore,it demonstrated a higher detection accuracy without affecting the speed.The results of this work have some reference significance for the automatic identification and detection of steel core conveyor belt damage.展开更多
Domain Generation Algorithms(DGAs)continue to pose a significant threat inmodernmalware infrastructures by enabling resilient and evasive communication with Command and Control(C&C)servers.Traditional detection me...Domain Generation Algorithms(DGAs)continue to pose a significant threat inmodernmalware infrastructures by enabling resilient and evasive communication with Command and Control(C&C)servers.Traditional detection methods-rooted in statistical heuristics,feature engineering,and shallow machine learning-struggle to adapt to the increasing sophistication,linguistic mimicry,and adversarial variability of DGA variants.The emergence of Large Language Models(LLMs)marks a transformative shift in this landscape.Leveraging deep contextual understanding,semantic generalization,and few-shot learning capabilities,LLMs such as BERT,GPT,and T5 have shown promising results in detecting both character-based and dictionary-based DGAs,including previously unseen(zeroday)variants.This paper provides a comprehensive and critical review of LLM-driven DGA detection,introducing a structured taxonomy of LLM architectures,evaluating the linguistic and behavioral properties of benchmark datasets,and comparing recent detection frameworks across accuracy,latency,robustness,and multilingual performance.We also highlight key limitations,including challenges in adversarial resilience,model interpretability,deployment scalability,and privacy risks.To address these gaps,we present a forward-looking research roadmap encompassing adversarial training,model compression,cross-lingual benchmarking,and real-time integration with SIEM/SOAR platforms.This survey aims to serve as a foundational resource for advancing the development of scalable,explainable,and operationally viable LLM-based DGA detection systems.展开更多
The complexity of cloud environments challenges secure resource management,especially for intrusion detection systems(IDS).Existing strategies struggle to balance efficiency,cost fairness,and threat resilience.This pa...The complexity of cloud environments challenges secure resource management,especially for intrusion detection systems(IDS).Existing strategies struggle to balance efficiency,cost fairness,and threat resilience.This paper proposes an innovative approach to managing cloud resources through the integration of a genetic algorithm(GA)with a“double auction”method.This approach seeks to enhance security and efficiency by aligning buyers and sellers within an intelligent market framework.It guarantees equitable pricing while utilizing resources efficiently and optimizing advantages for all stakeholders.The GA functions as an intelligent search mechanism that identifies optimal combinations of bids from users and suppliers,addressing issues arising from the intricacies of cloud systems.Analyses proved that our method surpasses previous strategies,particularly in terms of price accuracy,speed,and the capacity to manage large-scale activities,critical factors for real-time cybersecurity systems,such as IDS.Our research integrates artificial intelligence-inspired evolutionary algorithms with market-driven methods to develop intelligent resource management systems that are secure,scalable,and adaptable to evolving risks,such as process innovation.展开更多
With the birth of Software-Defined Networking(SDN),integration of both SDN and traditional architectures becomes the development trend of computer networks.Network intrusion detection faces challenges in dealing with ...With the birth of Software-Defined Networking(SDN),integration of both SDN and traditional architectures becomes the development trend of computer networks.Network intrusion detection faces challenges in dealing with complex attacks in SDN environments,thus to address the network security issues from the viewpoint of Artificial Intelligence(AI),this paper introduces the Crayfish Optimization Algorithm(COA)to the field of intrusion detection for both SDN and traditional network architectures,and based on the characteristics of the original COA,an Improved Crayfish Optimization Algorithm(ICOA)is proposed by integrating strategies of elite reverse learning,Levy flight,crowding factor and parameter modification.The ICOA is then utilized for AI-integrated feature selection of intrusion detection for both SDN and traditional network architectures,to reduce the dimensionality of the data and improve the performance of network intrusion detection.Finally,the performance evaluation is performed by testing not only the NSL-KDD dataset and the UNSW-NB 15 dataset for traditional networks but also the InSDN dataset for SDN-based networks.Experimental results show that ICOA improves the accuracy by 0.532%and 2.928%respectively compared with GWO and COA in traditional networks.In SDN networks,the accuracy of ICOA is 0.25%and 0.3%higher than COA and PSO.These findings collectively indicate that AI-integrated feature selection based on the proposed ICOA can promote network intrusion detection for both SDN and traditional architectures.展开更多
Optical fiber vibration is detected by the coherent optical time domain reflection technique. In addition to the vibration signals, the reflected signals include clutters and noises, which lead to a high false alarm r...Optical fiber vibration is detected by the coherent optical time domain reflection technique. In addition to the vibration signals, the reflected signals include clutters and noises, which lead to a high false alarm rate. The ＂cell averaging＂ constant false alarm rate algorithm has a high computing speed, but its detection performance will be declined in nonhomogeneous environments such as multiple targets. The ＂order statistics＂ constant false alarm rate algorithm has a distinct advantage in multiple target environments, but it has a lower computing speed. An intelligent two-level detection algorithm is presented based on ＂cell averaging＂ constant false alarm rate and ＂order statistics＂ constant false alarm rate which work in serial way, and the detection speed of ＂cell averaging＂ constant false alarm rate and performance of ＂order statistics＂ constant false alarm rate are conserved, respectively. Through the adaptive selection, the ＂cell averaging＂ is applied in homogeneous environments, and the two-level detection algorithm is employed in nonhomogeneous environments. Our Monte Carlo simulation results demonstrate that considering different signal noise ratios, the proposed algorithm gives better detection probability than that of ＂order statistics＂.展开更多
In order to solve the problem that the global navigation satellite system（GNSS） receivers can hardly detect the GNSS spoofing when they are deceived by a spoofer,a model-based approach for the identification of the ...In order to solve the problem that the global navigation satellite system（GNSS） receivers can hardly detect the GNSS spoofing when they are deceived by a spoofer,a model-based approach for the identification of the GNSS spoofing is proposed.First,a Hammerstein model is applied to model the spoofer/GNSS transmitter and the wireless channel.Then,a novel method based on the uncultivated wolf pack algorithm（UWPA） is proposed to estimate the model parameters.Taking the estimated model parameters as a feature vector,the identification of the spoofing is realized by comparing the Euclidean distance between the feature vectors.Simulations verify the effectiveness and the robustness of the proposed method.The results show that,compared with the other identification algorithms,such as least square（LS）,the iterative method and the bat-inspired algorithm（BA）,although the UWPA has a little more time-eomplexity than the LS and the BA algorithm,it has better estimation precision of the model parameters and higher identification rate of the GNSS spoofing,even for relative low signal-to-noise ratios.展开更多
With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory data.In the field of data mining for moving objects,the pr...With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory data.In the field of data mining for moving objects,the problem of anomaly detection is a hot topic.Based on the development of anomalous trajectory detection of moving objects,this paper introduces the classical trajectory outlier detection（TRAOD） algorithm,and then proposes a density-based trajectory outlier detection（DBTOD） algorithm,which compensates the disadvantages of the TRAOD algorithm that it is unable to detect anomalous defects when the trajectory is local and dense.The results of employing the proposed algorithm to Elk1993 and Deer1995 datasets are also presented,which show the effectiveness of the algorithm.展开更多
To aim at the multimode character of the data from the airplane detecting system, the paper combines Dempster- Shafer evidence theory and subjective Bayesian algorithm and makes to propose a mixed structure multimode ...To aim at the multimode character of the data from the airplane detecting system, the paper combines Dempster- Shafer evidence theory and subjective Bayesian algorithm and makes to propose a mixed structure multimode data fusion algorithm. The algorithm adopts a prorated algorithm relate to the incertitude evaluation to convert the probability evaluation into the precognition probability in an identity frame, and ensures the adaptability of different data from different source to the mixed system. To guarantee real time fusion, a combination of time domain fusion and space domain fusion is established, this not only assure the fusion of data chain in different time of the same sensor, but also the data fusion from different sensors distributed in different platforms and the data fusion among different modes. The feasibility and practicability are approved through computer simulation.展开更多
Genetic algorithm(GA) has received significant attention for the design and implementation of intrusion detection systems. In this paper, it is proposed to use variable length chromosomes(VLCs) in a GA-based network i...Genetic algorithm(GA) has received significant attention for the design and implementation of intrusion detection systems. In this paper, it is proposed to use variable length chromosomes(VLCs) in a GA-based network intrusion detection system.Fewer chromosomes with relevant features are used for rule generation. An effective fitness function is used to define the fitness of each rule. Each chromosome will have one or more rules in it. As each chromosome is a complete solution to the problem, fewer chromosomes are sufficient for effective intrusion detection. This reduces the computational time. The proposed approach is tested using Defense Advanced Research Project Agency(DARPA) 1998 data. The experimental results show that the proposed approach is efficient in network intrusion detection.展开更多
The leakage control is an important task, because it is associated with some problems such as economic loss, safety concerns, and environmental damages. The pervious methods which have already been devised for leakage...The leakage control is an important task, because it is associated with some problems such as economic loss, safety concerns, and environmental damages. The pervious methods which have already been devised for leakage detection are not only expensive and time consuming, but also have a low efficient. As a result, the global leakage detection methods such as leak detection based on simulation and calibration of the network have been considered recently. In this research, leak detection based on calibration in two hypothetical and a laboratorial networks is considered. Additionally a novel optimization method called step-by-step elimination method (SSEM) combining with a genetic algorithm (GA) is introduced to calibration and leakage detection in networks. This method step-by-step detects and eliminates the nodes that provide no contribution in leakage among uncertain parameters of calibration of a network. The proposed method initiates with an ordinary calibration for a studied network, follow by elimination of suspicious nodes among adjusted parameters, then, the network is re-calibrated. Finally the process is repeated until the numbers of unknown demands are equal to the desired numbers or the exact leakage locations and values are determined. These investigations illustrate the capability of this method for detecting the locations and sizes of leakages.展开更多
With the development of data age,data quality has become one of the problems that people pay much attention to.As a field of data mining,outlier detection is related to the quality of data.The isolated forest algorith...With the development of data age,data quality has become one of the problems that people pay much attention to.As a field of data mining,outlier detection is related to the quality of data.The isolated forest algorithm is one of the more prominent numerical data outlier detection algorithms in recent years.In the process of constructing the isolation tree by the isolated forest algorithm,as the isolation tree is continuously generated,the difference of isolation trees will gradually decrease or even no difference,which will result in the waste of memory and reduced efficiency of outlier detection.And in the constructed isolation trees,some isolation trees cannot detect outlier.In this paper,an improved iForest-based method GA-iForest is proposed.This method optimizes the isolated forest by selecting some better isolation trees according to the detection accuracy and the difference of isolation trees,thereby reducing some duplicate,similar and poor detection isolation trees and improving the accuracy and stability of outlier detection.In the experiment,Ubuntu system and Spark platform are used to build the experiment environment.The outlier datasets provided by ODDS are used as test.According to indicators such as the accuracy,recall rate,ROC curves,AUC and execution time,the performance of the proposed method is evaluated.Experimental results show that the proposed method can not only improve the accuracy and stability of outlier detection,but also reduce the number of isolation trees by 20%-40%compared with the original iForest method.展开更多
The purpose of community detection in complex networks is to identify the structural location of nodes. Complex network methods are usually graphical, with graph nodes representing objects and edges representing conne...The purpose of community detection in complex networks is to identify the structural location of nodes. Complex network methods are usually graphical, with graph nodes representing objects and edges representing connections between things. Communities are node clusters with many internal links but minimal intergroup connections. Although community detection has attracted much attention in social media research, most face functional weaknesses because the structure of society is unclear or the characteristics of nodes in society are not the same. Also, many existing algorithms have complex and costly calculations. This paper proposes different Harris Hawk Optimization (HHO) algorithm methods (such as Improved HHO Opposition-Based Learning(OBL) (IHHOOBL), Improved HHO Lévy Flight (IHHOLF), and Improved HHO Chaotic Map (IHHOCM)) were designed to balance exploitation and exploration in this algorithm for community detection in the social network. The proposed methods are evaluated on 12 different datasets based on NMI and modularity criteria. The findings reveal that the IHHOOBL method has better detection accuracy than IHHOLF and IHHOCM. Also, to offer the efficiency of the , state-of-the-art algorithms have been used as comparisons. The improvement percentage of IHHOOBL compared to the state-of-the-art algorithm is about 7.18%.展开更多
： This paper proposes a new sequential similarity detection algorithm （SSDA）, which can overcome matching error caused by grayscale distortion; meanwhile, time consumption is much less than that of regular algorith...： This paper proposes a new sequential similarity detection algorithm （SSDA）, which can overcome matching error caused by grayscale distortion; meanwhile, time consumption is much less than that of regular algorithms based on image feature. The algorithm adopts Sobel operator to deal with subgraph and template image, and regards the region which has maximum relevance as final result. In order to solve time-consuming problem existing in original algorithm, a coarse-to-fine matching method is put forward. Besides, the location correlation keeps updating and remains the minimum value in the whole scanning process, which can significantly decrease time consumption. Experiments show that the algorithm proposed in this article can not only overcome gray distortion, but also ensure accuracy. Time consumption is at least one time orders of magnitude shorter than that of primal algorithm.展开更多
We present an ameliorated arctangent algorithm based on phase-locked loop for digital Doppler signal processing,utilized within the heterodyne detection system. We define the error gain factor given by the approximati...We present an ameliorated arctangent algorithm based on phase-locked loop for digital Doppler signal processing,utilized within the heterodyne detection system. We define the error gain factor given by the approximation of Taylor expansion by means of a comparison of the measured values and true values. Exact expressions are derived for the amplitude error of two in-phase & quadrature signals and the frequency error of the acousto-optic modulator. Numerical simulation results and experimental results make it clear that the dynamic instability of the intermediate frequency signals leads to cumulative errors, which will spiral upward. An improved arctangent algorithm for the heterodyne detection is proposed to eliminate the cumulative errors and harmonic components. Depending on the narrow-band filter, our experiments were performed to realize the detectable displacement of 20 nm at a detection distance of 20 m. The aim of this paper is the demonstration of the optimized arctangent algorithm as a powerful approach to the demodulation algorithm, which will advance the signal-to-noise ratio and measurement accuracy of the heterodyne detection system.展开更多
基金supported by Science and Technology Innovation Programfor Postgraduate Students in IDP Subsidized by Fundamental Research Funds for the Central Universities(Project No.ZY20240335)support of the Research Project of the Key Technology of Malicious Code Detection Based on Data Mining in APT Attack(Project No.2022IT173)the Research Project of the Big Data Sensitive Information Supervision Technology Based on Convolutional Neural Network(Project No.2022011033).
文摘Previous studies have shown that deep learning is very effective in detecting known attacks.However,when facing unknown attacks,models such as Deep Neural Networks(DNN)combined with Long Short-Term Memory(LSTM),Convolutional Neural Networks(CNN)combined with LSTM,and so on are built by simple stacking,which has the problems of feature loss,low efficiency,and low accuracy.Therefore,this paper proposes an autonomous detectionmodel for Distributed Denial of Service attacks,Multi-Scale Convolutional Neural Network-Bidirectional Gated Recurrent Units-Single Headed Attention(MSCNN-BiGRU-SHA),which is based on a Multistrategy Integrated Zebra Optimization Algorithm(MI-ZOA).The model undergoes training and testing with the CICDDoS2019 dataset,and its performance is evaluated on a new GINKS2023 dataset.The hyperparameters for Conv_filter and GRU_unit are optimized using the Multi-strategy Integrated Zebra Optimization Algorithm(MIZOA).The experimental results show that the test accuracy of the MSCNN-BiGRU-SHA model based on the MIZOA proposed in this paper is as high as 0.9971 in the CICDDoS 2019 dataset.The evaluation accuracy of the new dataset GINKS2023 created in this paper is 0.9386.Compared to the MSCNN-BiGRU-SHA model based on the Zebra Optimization Algorithm(ZOA),the detection accuracy on the GINKS2023 dataset has improved by 5.81%,precisionhas increasedby 1.35%,the recallhas improvedby 9%,and theF1scorehas increasedby 5.55%.Compared to the MSCNN-BiGRU-SHA models developed using Grid Search,Random Search,and Bayesian Optimization,the MSCNN-BiGRU-SHA model optimized with the MI-ZOA exhibits better performance in terms of accuracy,precision,recall,and F1 score.
基金supported by the National Natural Science Foundation of China under Grant 61602162the Hubei Provincial Science and Technology Plan Project under Grant 2023BCB041.
文摘Nowadays,abnormal traffic detection for Software-Defined Networking(SDN)faces the challenges of large data volume and high dimensionality.Since traditional machine learning-based detection methods have the problem of data redundancy,the Metaheuristic Algorithm(MA)is introduced to select features beforemachine learning to reduce the dimensionality of data.Since a Tyrannosaurus Optimization Algorithm(TROA)has the advantages of few parameters,simple implementation,and fast convergence,and it shows better results in feature selection,TROA can be applied to abnormal traffic detection for SDN.However,TROA suffers frominsufficient global search capability,is easily trapped in local optimums,and has poor search accuracy.Then,this paper tries to improve TROA,namely the Improved Tyrannosaurus Optimization Algorithm(ITROA).It proposes a metaheuristic-driven abnormal traffic detection model for SDN based on ITROA.Finally,the validity of the ITROA is verified by the benchmark function and the UCI dataset,and the feature selection optimization operation is performed on the InSDN dataset by ITROA and other MAs to obtain the optimized feature subset for SDN abnormal traffic detection.The experiment shows that the performance of the proposed ITROA outperforms compared MAs in terms of the metaheuristic-driven model for SDN,achieving an accuracy of 99.37%on binary classification and 96.73%on multiclassification.
文摘Face recognition has emerged as one of the most prominent applications of image analysis and under-standing,gaining considerable attention in recent years.This growing interest is driven by two key factors:its extensive applications in law enforcement and the commercial domain,and the rapid advancement of practical technologies.Despite the significant advancements,modern recognition algorithms still struggle in real-world conditions such as varying lighting conditions,occlusion,and diverse facial postures.In such scenarios,human perception is still well above the capabilities of present technology.Using the systematic mapping study,this paper presents an in-depth review of face detection algorithms and face recognition algorithms,presenting a detailed survey of advancements made between 2015 and 2024.We analyze key methodologies,highlighting their strengths and restrictions in the application context.Additionally,we examine various datasets used for face detection/recognition datasets focusing on the task-specific applications,size,diversity,and complexity.By analyzing these algorithms and datasets,this survey works as a valuable resource for researchers,identifying the research gap in the field of face detection and recognition and outlining potential directions for future research.
基金the Sichuan Science and Technology Program(No.2022YFS0557)the National Natural Science Foundation of China(No.61972271)。
文摘In smart driving for rail transit,a reliable obstacle detection system is an important guarantee for the safety of trains.Therein,the detection of the rail area directly affects the accuracy of the system to identify dangerous targets.Both the rail line and the lane are presented as thin line shapes in the image,but the rail scene is more complex,and the color of the rail line is more difficult to distinguish from the background.By comparison,there are already many deep learning-based lane detection algorithms,but there is a lack of public datasets and targeted deep learning detection algorithms for rail line detection.To address this,this paper constructs a rail image dataset RailwayLine and labels the rail line for the training and testing of models.This dataset contains rich rail images including single-rail,multi-rail,straight rail,curved rail,crossing rails,occlusion,blur,and different lighting conditions.To address the problem of the lack of deep learning-based rail line detection algorithms,we improve the CLRNet algorithm which has an excellent performance in lane detection,and propose the CLRNet-R algorithm for rail line detection.To address the problem of the rail line being thin and occupying fewer pixels in the image,making it difficult to distinguish from complex backgrounds,we introduce an attention mechanism to enhance global feature extraction ability and add a semantic segmentation head to enhance the features of the rail region by the binary probability of rail lines.To address the poor curve recognition performance and unsmooth output lines in the original CLRNet algorithm,we improve the weight allocation for line intersection-over-union calculation in the original framework and propose two loss functions based on local slopes to optimize the model’s local sampling point training constraints,improving the model’s fitting performance on curved rails and obtaining smooth and stable rail line detection results.Through experiments,this paper demonstrates that compared with other mainstream lane detection algorithms,the algorithm proposed in this paper has a better performance for rail line detection.
文摘The excessive use of artificial intelligence(AI)algorithms has caused the problem of errors in AI algorithms,which has challenged the fairness of decision-making,and has intensified people’s inequality.Therefore,it is necessary to conduct in-depth research and propose corresponding error detection and error elimination methods.This paper first proposes the root causes and threats of bias in AI algorithms,then summarizes the existing bias detection and error elimination methods,and proposes a bias processing framework in three-level dimensions of data,models,and conclusions,aiming to provide a framework for a comprehensive solution to errors in algorithms.At the same time,it also summarizes the problems and challenges in existing research and makes a prospect for future research trends.It is hoped that it will be helpful for us to build fairer AI.
基金supported by the National Natural Science Foundation of China(62276055).
文摘Ant colony optimization(ACO)is a random search algorithm based on probability calculation.However,the uninformed search strategy has a slow convergence speed.The Bayesian algorithm uses the historical information of the searched point to determine the next search point during the search process,reducing the uncertainty in the random search process.Due to the ability of the Bayesian algorithm to reduce uncertainty,a Bayesian ACO algorithm is proposed in this paper to increase the convergence speed of the conventional ACO algorithm for image edge detection.In addition,this paper has the following two innovations on the basis of the classical algorithm,one of which is to add random perturbations after completing the pheromone update.The second is the use of adaptive pheromone heuristics.Experimental results illustrate that the proposed Bayesian ACO algorithm has faster convergence and higher precision and recall than the traditional ant colony algorithm,due to the improvement of the pheromone utilization rate.Moreover,Bayesian ACO algorithm outperforms the other comparative methods in edge detection task.
文摘Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is proposed based on the improved fully convolutional one-stage object detection(FCOS)algorithm.The regression performance of bounding boxes was optimized by introducing the complete intersection over union loss function into the improved algorithm.The feature fusion network structure is modified by adding adaptive fusion paths to the feature fusion network structure,which makes full use of the features of accurate localization and semantics of multi-scale feature fusion networks.Finally,the network structure was trained and validated by using the X-ray image dataset of damages in conveyor belts with steel rope cores provided by a flaw detection equipment manufacturer.In addition,the data enhancement methods such as rotating,mirroring,and scaling,were employed to enrich the image dataset so that the model is adequately trained.Experimental results showed that the improved FCOS algorithm promoted the precision rate and the recall rate by 20.9%and 14.8%respectively,compared with the original algorithm.Meanwhile,compared with Fast R-CNN,Faster R-CNN,SSD,and YOLOv3,the improved FCOS algorithm has obvious advantages;detection precision rate and recall rate of the modified network reached 95.8%and 97.0%respectively.Furthermore,it demonstrated a higher detection accuracy without affecting the speed.The results of this work have some reference significance for the automatic identification and detection of steel core conveyor belt damage.
基金the Deanship of Scientific Research at King Khalid University for funding this work through large group under grant number(GRP.2/663/46).
文摘Domain Generation Algorithms(DGAs)continue to pose a significant threat inmodernmalware infrastructures by enabling resilient and evasive communication with Command and Control(C&C)servers.Traditional detection methods-rooted in statistical heuristics,feature engineering,and shallow machine learning-struggle to adapt to the increasing sophistication,linguistic mimicry,and adversarial variability of DGA variants.The emergence of Large Language Models(LLMs)marks a transformative shift in this landscape.Leveraging deep contextual understanding,semantic generalization,and few-shot learning capabilities,LLMs such as BERT,GPT,and T5 have shown promising results in detecting both character-based and dictionary-based DGAs,including previously unseen(zeroday)variants.This paper provides a comprehensive and critical review of LLM-driven DGA detection,introducing a structured taxonomy of LLM architectures,evaluating the linguistic and behavioral properties of benchmark datasets,and comparing recent detection frameworks across accuracy,latency,robustness,and multilingual performance.We also highlight key limitations,including challenges in adversarial resilience,model interpretability,deployment scalability,and privacy risks.To address these gaps,we present a forward-looking research roadmap encompassing adversarial training,model compression,cross-lingual benchmarking,and real-time integration with SIEM/SOAR platforms.This survey aims to serve as a foundational resource for advancing the development of scalable,explainable,and operationally viable LLM-based DGA detection systems.
文摘The complexity of cloud environments challenges secure resource management,especially for intrusion detection systems(IDS).Existing strategies struggle to balance efficiency,cost fairness,and threat resilience.This paper proposes an innovative approach to managing cloud resources through the integration of a genetic algorithm(GA)with a“double auction”method.This approach seeks to enhance security and efficiency by aligning buyers and sellers within an intelligent market framework.It guarantees equitable pricing while utilizing resources efficiently and optimizing advantages for all stakeholders.The GA functions as an intelligent search mechanism that identifies optimal combinations of bids from users and suppliers,addressing issues arising from the intricacies of cloud systems.Analyses proved that our method surpasses previous strategies,particularly in terms of price accuracy,speed,and the capacity to manage large-scale activities,critical factors for real-time cybersecurity systems,such as IDS.Our research integrates artificial intelligence-inspired evolutionary algorithms with market-driven methods to develop intelligent resource management systems that are secure,scalable,and adaptable to evolving risks,such as process innovation.
基金supported by the National Natural Science Foundation of China under Grant 61602162the Hubei Provincial Science and Technology Plan Project under Grant 2023BCB041.
文摘With the birth of Software-Defined Networking(SDN),integration of both SDN and traditional architectures becomes the development trend of computer networks.Network intrusion detection faces challenges in dealing with complex attacks in SDN environments,thus to address the network security issues from the viewpoint of Artificial Intelligence(AI),this paper introduces the Crayfish Optimization Algorithm(COA)to the field of intrusion detection for both SDN and traditional network architectures,and based on the characteristics of the original COA,an Improved Crayfish Optimization Algorithm(ICOA)is proposed by integrating strategies of elite reverse learning,Levy flight,crowding factor and parameter modification.The ICOA is then utilized for AI-integrated feature selection of intrusion detection for both SDN and traditional network architectures,to reduce the dimensionality of the data and improve the performance of network intrusion detection.Finally,the performance evaluation is performed by testing not only the NSL-KDD dataset and the UNSW-NB 15 dataset for traditional networks but also the InSDN dataset for SDN-based networks.Experimental results show that ICOA improves the accuracy by 0.532%and 2.928%respectively compared with GWO and COA in traditional networks.In SDN networks,the accuracy of ICOA is 0.25%and 0.3%higher than COA and PSO.These findings collectively indicate that AI-integrated feature selection based on the proposed ICOA can promote network intrusion detection for both SDN and traditional architectures.
文摘Optical fiber vibration is detected by the coherent optical time domain reflection technique. In addition to the vibration signals, the reflected signals include clutters and noises, which lead to a high false alarm rate. The ＂cell averaging＂ constant false alarm rate algorithm has a high computing speed, but its detection performance will be declined in nonhomogeneous environments such as multiple targets. The ＂order statistics＂ constant false alarm rate algorithm has a distinct advantage in multiple target environments, but it has a lower computing speed. An intelligent two-level detection algorithm is presented based on ＂cell averaging＂ constant false alarm rate and ＂order statistics＂ constant false alarm rate which work in serial way, and the detection speed of ＂cell averaging＂ constant false alarm rate and performance of ＂order statistics＂ constant false alarm rate are conserved, respectively. Through the adaptive selection, the ＂cell averaging＂ is applied in homogeneous environments, and the two-level detection algorithm is employed in nonhomogeneous environments. Our Monte Carlo simulation results demonstrate that considering different signal noise ratios, the proposed algorithm gives better detection probability than that of ＂order statistics＂.
基金The National Natural Science Foundation of China(No.61271214,61471152)the Postdoctoral Science Foundation of Jiangsu Province(No.1402023C)the Natural Science Foundation of Zhejiang Province(No.LZ14F010003)
文摘In order to solve the problem that the global navigation satellite system（GNSS） receivers can hardly detect the GNSS spoofing when they are deceived by a spoofer,a model-based approach for the identification of the GNSS spoofing is proposed.First,a Hammerstein model is applied to model the spoofer/GNSS transmitter and the wireless channel.Then,a novel method based on the uncultivated wolf pack algorithm（UWPA） is proposed to estimate the model parameters.Taking the estimated model parameters as a feature vector,the identification of the spoofing is realized by comparing the Euclidean distance between the feature vectors.Simulations verify the effectiveness and the robustness of the proposed method.The results show that,compared with the other identification algorithms,such as least square（LS）,the iterative method and the bat-inspired algorithm（BA）,although the UWPA has a little more time-eomplexity than the LS and the BA algorithm,it has better estimation precision of the model parameters and higher identification rate of the GNSS spoofing,even for relative low signal-to-noise ratios.
基金supported by the Aeronautical Science Foundation of China(20111052010)the Jiangsu Graduates Innovation Project (CXZZ120163)+1 种基金the "333" Project of Jiangsu Provincethe Qing Lan Project of Jiangsu Province
文摘With the development of global position system（GPS）,wireless technology and location aware services,it is possible to collect a large quantity of trajectory data.In the field of data mining for moving objects,the problem of anomaly detection is a hot topic.Based on the development of anomalous trajectory detection of moving objects,this paper introduces the classical trajectory outlier detection（TRAOD） algorithm,and then proposes a density-based trajectory outlier detection（DBTOD） algorithm,which compensates the disadvantages of the TRAOD algorithm that it is unable to detect anomalous defects when the trajectory is local and dense.The results of employing the proposed algorithm to Elk1993 and Deer1995 datasets are also presented,which show the effectiveness of the algorithm.
文摘To aim at the multimode character of the data from the airplane detecting system, the paper combines Dempster- Shafer evidence theory and subjective Bayesian algorithm and makes to propose a mixed structure multimode data fusion algorithm. The algorithm adopts a prorated algorithm relate to the incertitude evaluation to convert the probability evaluation into the precognition probability in an identity frame, and ensures the adaptability of different data from different source to the mixed system. To guarantee real time fusion, a combination of time domain fusion and space domain fusion is established, this not only assure the fusion of data chain in different time of the same sensor, but also the data fusion from different sensors distributed in different platforms and the data fusion among different modes. The feasibility and practicability are approved through computer simulation.
文摘Genetic algorithm(GA) has received significant attention for the design and implementation of intrusion detection systems. In this paper, it is proposed to use variable length chromosomes(VLCs) in a GA-based network intrusion detection system.Fewer chromosomes with relevant features are used for rule generation. An effective fitness function is used to define the fitness of each rule. Each chromosome will have one or more rules in it. As each chromosome is a complete solution to the problem, fewer chromosomes are sufficient for effective intrusion detection. This reduces the computational time. The proposed approach is tested using Defense Advanced Research Project Agency(DARPA) 1998 data. The experimental results show that the proposed approach is efficient in network intrusion detection.
文摘The leakage control is an important task, because it is associated with some problems such as economic loss, safety concerns, and environmental damages. The pervious methods which have already been devised for leakage detection are not only expensive and time consuming, but also have a low efficient. As a result, the global leakage detection methods such as leak detection based on simulation and calibration of the network have been considered recently. In this research, leak detection based on calibration in two hypothetical and a laboratorial networks is considered. Additionally a novel optimization method called step-by-step elimination method (SSEM) combining with a genetic algorithm (GA) is introduced to calibration and leakage detection in networks. This method step-by-step detects and eliminates the nodes that provide no contribution in leakage among uncertain parameters of calibration of a network. The proposed method initiates with an ordinary calibration for a studied network, follow by elimination of suspicious nodes among adjusted parameters, then, the network is re-calibrated. Finally the process is repeated until the numbers of unknown demands are equal to the desired numbers or the exact leakage locations and values are determined. These investigations illustrate the capability of this method for detecting the locations and sizes of leakages.
基金supported by the State Grid Liaoning Electric Power Supply CO, LTDthe financial support for the “Key Technology and Application Research of the Self-Service Grid Big Data Governance (No.SGLNXT00YJJS1800110)”
文摘With the development of data age,data quality has become one of the problems that people pay much attention to.As a field of data mining,outlier detection is related to the quality of data.The isolated forest algorithm is one of the more prominent numerical data outlier detection algorithms in recent years.In the process of constructing the isolation tree by the isolated forest algorithm,as the isolation tree is continuously generated,the difference of isolation trees will gradually decrease or even no difference,which will result in the waste of memory and reduced efficiency of outlier detection.And in the constructed isolation trees,some isolation trees cannot detect outlier.In this paper,an improved iForest-based method GA-iForest is proposed.This method optimizes the isolated forest by selecting some better isolation trees according to the detection accuracy and the difference of isolation trees,thereby reducing some duplicate,similar and poor detection isolation trees and improving the accuracy and stability of outlier detection.In the experiment,Ubuntu system and Spark platform are used to build the experiment environment.The outlier datasets provided by ODDS are used as test.According to indicators such as the accuracy,recall rate,ROC curves,AUC and execution time,the performance of the proposed method is evaluated.Experimental results show that the proposed method can not only improve the accuracy and stability of outlier detection,but also reduce the number of isolation trees by 20%-40%compared with the original iForest method.
文摘The purpose of community detection in complex networks is to identify the structural location of nodes. Complex network methods are usually graphical, with graph nodes representing objects and edges representing connections between things. Communities are node clusters with many internal links but minimal intergroup connections. Although community detection has attracted much attention in social media research, most face functional weaknesses because the structure of society is unclear or the characteristics of nodes in society are not the same. Also, many existing algorithms have complex and costly calculations. This paper proposes different Harris Hawk Optimization (HHO) algorithm methods (such as Improved HHO Opposition-Based Learning(OBL) (IHHOOBL), Improved HHO Lévy Flight (IHHOLF), and Improved HHO Chaotic Map (IHHOCM)) were designed to balance exploitation and exploration in this algorithm for community detection in the social network. The proposed methods are evaluated on 12 different datasets based on NMI and modularity criteria. The findings reveal that the IHHOOBL method has better detection accuracy than IHHOLF and IHHOCM. Also, to offer the efficiency of the , state-of-the-art algorithms have been used as comparisons. The improvement percentage of IHHOOBL compared to the state-of-the-art algorithm is about 7.18%.
基金the National Natural Science Foundation of China(No.61165008)
文摘： This paper proposes a new sequential similarity detection algorithm （SSDA）, which can overcome matching error caused by grayscale distortion; meanwhile, time consumption is much less than that of regular algorithms based on image feature. The algorithm adopts Sobel operator to deal with subgraph and template image, and regards the region which has maximum relevance as final result. In order to solve time-consuming problem existing in original algorithm, a coarse-to-fine matching method is put forward. Besides, the location correlation keeps updating and remains the minimum value in the whole scanning process, which can significantly decrease time consumption. Experiments show that the algorithm proposed in this article can not only overcome gray distortion, but also ensure accuracy. Time consumption is at least one time orders of magnitude shorter than that of primal algorithm.
基金supported by Key Research Program of Frontier Science,Chinese Academy of Sciences(Grant No.QYZDB-SSW-SLH014)the Yong Scientists Fund of the National Natural Science Foundation of China(Grant No.61205143)
文摘We present an ameliorated arctangent algorithm based on phase-locked loop for digital Doppler signal processing,utilized within the heterodyne detection system. We define the error gain factor given by the approximation of Taylor expansion by means of a comparison of the measured values and true values. Exact expressions are derived for the amplitude error of two in-phase & quadrature signals and the frequency error of the acousto-optic modulator. Numerical simulation results and experimental results make it clear that the dynamic instability of the intermediate frequency signals leads to cumulative errors, which will spiral upward. An improved arctangent algorithm for the heterodyne detection is proposed to eliminate the cumulative errors and harmonic components. Depending on the narrow-band filter, our experiments were performed to realize the detectable displacement of 20 nm at a detection distance of 20 m. The aim of this paper is the demonstration of the optimized arctangent algorithm as a powerful approach to the demodulation algorithm, which will advance the signal-to-noise ratio and measurement accuracy of the heterodyne detection system.
