Blockchain merges technology with the Internet of Things(IoT)for addressing security and privacy-related issues.However,conventional blockchain suffers from scalability issues due to its linear structure,which increas...Blockchain merges technology with the Internet of Things(IoT)for addressing security and privacy-related issues.However,conventional blockchain suffers from scalability issues due to its linear structure,which increases the storage overhead,and Intrusion detection performed was limited with attack severity,leading to performance degradation.To overcome these issues,we proposed MZWB(Multi-Zone-Wise Blockchain)model.Initially,all the authenticated IoT nodes in the network ensure their legitimacy by using the Enhanced Blowfish Algorithm(EBA),considering several metrics.Then,the legitimately considered nodes for network construction for managing the network using Bayesian-Direct Acyclic Graph(B-DAG),which considers several metrics.The intrusion detection is performed based on two tiers.In the first tier,a Deep Convolution Neural Network(DCNN)analyzes the data packets by extracting packet flow features to classify the packets as normal,malicious,and suspicious.In the second tier,the suspicious packets are classified as normal or malicious using the Generative Adversarial Network(GAN).Finally,intrusion scenario performed reconstruction to reduce the severity of attacks in which Improved Monkey Optimization(IMO)is used for attack path discovery by considering several metrics,and the Graph cut utilized algorithm for attack scenario reconstruction(ASR).UNSW-NB15 and BoT-IoT utilized datasets for the MZWB method simulated using a Network simulator(NS-3.26).Compared with previous performance metrics such as energy consumption,storage overhead accuracy,response time,attack detection rate,precision,recall,and F-measure.The simulation result shows that the proposed MZWB method achieves high performance than existing works.展开更多
This paper introduces the middleman attack methods which are against the remote desktop protocol(RDP),discusses advantages and disadvantages of several current mainstream prevention strategies,and puts forward a new p...This paper introduces the middleman attack methods which are against the remote desktop protocol(RDP),discusses advantages and disadvantages of several current mainstream prevention strategies,and puts forward a new prevention strategy.The strategy,taking advantage of the original key agreement process of the RDP,designs a piecewise authentication scheme of the key agreement.Using the strategy can achieve the purpose of prevention and detection of middleman attacks.Finally,the security of the strategy is analyzed.展开更多
While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),...While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),given that these techniques are increasingly being used by malicious actors to compromise IoT systems.Although an ample body of research focusing on conventional AI methods exists,there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures.To contribute to this nascent research stream,a novel AI-driven security system denoted as“AI2AI”is presented in this work.AI2AI employs AI techniques to enhance the performance and optimize security mechanisms within the IoT framework.We also introduce the Genetic Algorithm Anomaly Detection and Prevention Deep Neural Networks(GAADPSDNN)sys-tem that can be implemented to effectively identify,detect,and prevent cyberattacks targeting IoT devices.Notably,this system demonstrates adaptability to both federated and centralized learning environments,accommodating a wide array of IoT devices.Our evaluation of the GAADPSDNN system using the recently complied WUSTL-IIoT and Edge-IIoT datasets underscores its efficacy.Achieving an impressive overall accuracy of 98.18%on the Edge-IIoT dataset,the GAADPSDNN outperforms the standard deep neural network(DNN)classifier with 94.11%accuracy.Furthermore,with the proposed enhancements,the accuracy of the unoptimized random forest classifier(80.89%)is improved to 93.51%,while the overall accuracy(98.18%)surpasses the results(93.91%,94.67%,94.94%,and 94.96%)achieved when alternative systems based on diverse optimization techniques and the same dataset are employed.The proposed optimization techniques increase the effectiveness of the anomaly detection system by efficiently achieving high accuracy and reducing the computational load on IoT devices through the adaptive selection of active features.展开更多
The Smart Grid is an enhancement of the traditional grid system and employs new technologies and sophisticated communication techniques for electrical power transmission and distribution. The Smart Grid’s communicati...The Smart Grid is an enhancement of the traditional grid system and employs new technologies and sophisticated communication techniques for electrical power transmission and distribution. The Smart Grid’s communication network shares information about status of its several integrated IEDs (Intelligent Electronic Devices). However, the IEDs connected throughout the Smart Grid, open opportunities for attackers to interfere with the communications and utilities resources or take clients’ private data. This development has introduced new cyber-security challenges for the Smart Grid and is a very concerning issue because of emerging cyber-threats and security incidents that have occurred recently all over the world. The purpose of this research is to detect and mitigate Distributed Denial of Service [DDoS] with application to the Electrical Smart Grid System by deploying an optimized Stealthwatch Secure Network analytics tool. In this paper, the DDoS attack in the Smart Grid communication networks was modeled using Stealthwatch tool. The simulated network consisted of Secure Network Analytic tools virtual machines (VMs), electrical Grid network communication topology, attackers and Target VMs. Finally, the experiments and simulations were performed, and the research results showed that Stealthwatch analytic tool is very effective in detecting and mitigating DDoS attacks in the Smart Grid System without causing any blackout or shutdown of any internal systems as compared to other tools such as GNS3, NeSSi2, NISST Framework, OMNeT++, INET Framework, ReaSE, NS2, NS3, M5 Simulator, OPNET, PLC & TIA Portal management Software which do not have the capability to do so. Also, using Stealthwatch tool to create a security baseline for Smart Grid environment, contributes to risk mitigation and sound security hygiene.展开更多
This study presents a comprehensive and secure architectural framework for the Internet of Medical Things(IoMT),integrating the foundational principles of the Confidentiality,Integrity,and Availability(CIA)triad along...This study presents a comprehensive and secure architectural framework for the Internet of Medical Things(IoMT),integrating the foundational principles of the Confidentiality,Integrity,and Availability(CIA)triad along with authentication mechanisms.Leveraging advanced Machine Learning(ML)and Deep Learning(DL)techniques,the proposed system is designed to safeguard Patient-Generated Health Data(PGHD)across interconnected medical devices.Given the increasing complexity and scale of cyber threats in IoMT environments,the integration of Intrusion Detection and Prevention Systems(IDPS)with intelligent analytics is critical.Our methodology employs both standalone and hybrid ML&DL models to automate threat detection and enable real-time analysis,while ensuring rapid and accurate responses to a diverse array of attacks.Emphasis is placed on systematic model evaluation using detection metrics such as accuracy,False Alarm Rate(FAR),and False Discovery Rate(FDR),with performance validation through cross-validation and statistical significance testing.Experimental results based on the Edge-IIoTset dataset demonstrate the superior performance of ensemble-based ML models such as Extreme Gradient Boosting(XGB)and hybrid DL models such as Convolutional Neural Networks with Autoencoders(CNN+AE),which achieved detection accuracies of 96%and 98%,respectively,with notably low FARs.These findings underscore the effectiveness of combining traditional security principles with advanced AI-driven methodologies to ensure secure,resilient,and trustworthy healthcare systems within the IoMT ecosystem.展开更多
Fog computing(FC)is a networking paradigm where wireless devices known as fog nodes are placed at the edge of the network(close to the Internet of Things(IoT)devices).Fog nodes provide services in lieu of the cloud.Th...Fog computing(FC)is a networking paradigm where wireless devices known as fog nodes are placed at the edge of the network(close to the Internet of Things(IoT)devices).Fog nodes provide services in lieu of the cloud.Thus,improving the performance of the network and making it attractive to social media-based systems.Security issues are one of the most challenges encountered in FC.In this paper,we propose an anomalybased Intrusion Detection and Prevention System(IDPS)against Man-in-theMiddle(MITM)attack in the fog layer.The system uses special nodes known as Intrusion Detection System(IDS)nodes to detect intrusion in the network.They periodically monitor the behavior of the fog nodes in the network.Any deviation from normal network activity is categorized as malicious,and the suspected node is isolated.ExponentiallyWeighted Moving Average(EWMA)is added to the system to smooth out the noise that is typically found in social media communications.Our results(with 95%confidence)show that the accuracy of the proposed system increases from 80%to 95%after EWMA is added.Also,with EWMA,the proposed system can detect the intrusion from 0.25–0.5 s seconds faster than that without EWMA.However,it affects the latency of services provided by the fog nodes by at least 0.75–1.3 s.Finally,EWMA has not increased the energy overhead of the system,due to its lightweight.展开更多
Due to polymorphic nature of malware attack,a signature-based analysis is no longer sufficient to solve polymorphic and stealth nature ofmalware attacks.On the other hand,state-of-the-art methods like deep learning re...Due to polymorphic nature of malware attack,a signature-based analysis is no longer sufficient to solve polymorphic and stealth nature ofmalware attacks.On the other hand,state-of-the-art methods like deep learning require labelled dataset as a target to train a supervised model.This is unlikely to be the case in production network as the dataset is unstructured and has no label.Hence an unsupervised learning is recommended.Behavioral study is one of the techniques to elicit traffic pattern.However,studies have shown that existing behavioral intrusion detection model had a few issues which had been parameterized into its common characteristics,namely lack of prior information(p(θ)),and reduced parameters(θ).Therefore,this study aims to utilize the previously built Feature Selection Model subsequently to design a Predictive Analytics Model based on Bayesian Network used to improve the analysis prediction.Feature Selection Model is used to learn significant label as a target and Bayesian Network is a sophisticated probabilistic approach to predict intrusion.Finally,the results are extended to evaluate detection,accuracy and false alarm rate of the model against the subject matter expert model,Support Vector Machine(SVM),k nearest neighbor(k-NN)using simulated and ground-truth dataset.The ground-truth dataset from the production traffic of one of the largest healthcare provider in Malaysia is used to promote realism on the real use case scenario.Results have shown that the proposed model consistently outperformed other models.展开更多
Zhao Fanghui is a researcher,doctoral supervisor,distinguished professor at Peking Union Medical College and director of the Department of Cancer Epidemiology,National Cancer Center and Cancer Hospital,Chinese Academy...Zhao Fanghui is a researcher,doctoral supervisor,distinguished professor at Peking Union Medical College and director of the Department of Cancer Epidemiology,National Cancer Center and Cancer Hospital,Chinese Academy of Medical Sciences.Her research focuses on the prevention and early detection of cancer;more specifically,research into ways to prevent cervical and breast cancers through comprehensive approaches.She is committed to improving health equity and accessibility for underserved regions,and especially vulnerable populations,and to advancing China,s cancer prevention initiatives.She received the WHO/IARC Senior Visiting Scientist Award in 2015,and the APEC Healthy Women,Healthy Economies Research Prize in 2020.In 2025,she was honored as a National March 8th Red-Banner Holder.展开更多
Understanding the cellular origins and early evolutionary dynamics that drive the initiation of carcinogenesis is critical to advancing early detection and prevention strategies.By characterizing key molecular,cellula...Understanding the cellular origins and early evolutionary dynamics that drive the initiation of carcinogenesis is critical to advancing early detection and prevention strategies.By characterizing key molecular,cellular and niche events at the precancerous tipping point of early gastric cancer(EGC),we aimed to develop more precise screening tools and design targeted interventions to prevent malignant transformation at this stage.We utilized our AI models to integrate spatial multimodal data from nine EGC endoscopic submucosal dissection(ESD)samples(covering sequential stages from normal to cancer),construct a spatial-temporal profile of disease progression,and identify a critical tipping point(PMC_P)characterized by an immune-suppressive microenvironment during early cancer development.At this stage,inflammatory pit mucous cells with stemness(PMC_2)interact with fibroblasts via NAMPT→ITGA5/ITGB1 and with macrophages via AREG→EGFR/ERBB2 signaling,fostering cancer initiation.We established gastric precancerous cell lines and organoids to demonstrate that NAMPT and AREG promote cellular proliferation in vitro.Furthermore,in the transgenic CEA-SV40 mouse model,targeting AREG and/or NAMPT disrupted key cell interactions,inhibited the JAK-STAT,MAPK,and NFκB pathways,and reduced PD-L1 expression,which was also confirmed by western blot in vitro.These interventions delayed disease progression,reversed the immunosuppressive microenvironment,and prevented malignant transformation.Clinical validation was conducted using endoscopically resected EGC specimens.Our study provides a precise spatiotemporal depiction of EGC development and identifies novel diagnostic markers and therapeutic targets for early intervention.展开更多
文摘Blockchain merges technology with the Internet of Things(IoT)for addressing security and privacy-related issues.However,conventional blockchain suffers from scalability issues due to its linear structure,which increases the storage overhead,and Intrusion detection performed was limited with attack severity,leading to performance degradation.To overcome these issues,we proposed MZWB(Multi-Zone-Wise Blockchain)model.Initially,all the authenticated IoT nodes in the network ensure their legitimacy by using the Enhanced Blowfish Algorithm(EBA),considering several metrics.Then,the legitimately considered nodes for network construction for managing the network using Bayesian-Direct Acyclic Graph(B-DAG),which considers several metrics.The intrusion detection is performed based on two tiers.In the first tier,a Deep Convolution Neural Network(DCNN)analyzes the data packets by extracting packet flow features to classify the packets as normal,malicious,and suspicious.In the second tier,the suspicious packets are classified as normal or malicious using the Generative Adversarial Network(GAN).Finally,intrusion scenario performed reconstruction to reduce the severity of attacks in which Improved Monkey Optimization(IMO)is used for attack path discovery by considering several metrics,and the Graph cut utilized algorithm for attack scenario reconstruction(ASR).UNSW-NB15 and BoT-IoT utilized datasets for the MZWB method simulated using a Network simulator(NS-3.26).Compared with previous performance metrics such as energy consumption,storage overhead accuracy,response time,attack detection rate,precision,recall,and F-measure.The simulation result shows that the proposed MZWB method achieves high performance than existing works.
基金the National Natural Science Foundation of China(No.61272500)the Beijing Natural Science Foundation(No.4142008)the Pre-launch of Beijing City Government Key Tasks and District Government Emergency Projects(No.Z131100005613030)
文摘This paper introduces the middleman attack methods which are against the remote desktop protocol(RDP),discusses advantages and disadvantages of several current mainstream prevention strategies,and puts forward a new prevention strategy.The strategy,taking advantage of the original key agreement process of the RDP,designs a piecewise authentication scheme of the key agreement.Using the strategy can achieve the purpose of prevention and detection of middleman attacks.Finally,the security of the strategy is analyzed.
文摘While emerging technologies such as the Internet of Things(IoT)have many benefits,they also pose considerable security challenges that require innovative solutions,including those based on artificial intelligence(AI),given that these techniques are increasingly being used by malicious actors to compromise IoT systems.Although an ample body of research focusing on conventional AI methods exists,there is a paucity of studies related to advanced statistical and optimization approaches aimed at enhancing security measures.To contribute to this nascent research stream,a novel AI-driven security system denoted as“AI2AI”is presented in this work.AI2AI employs AI techniques to enhance the performance and optimize security mechanisms within the IoT framework.We also introduce the Genetic Algorithm Anomaly Detection and Prevention Deep Neural Networks(GAADPSDNN)sys-tem that can be implemented to effectively identify,detect,and prevent cyberattacks targeting IoT devices.Notably,this system demonstrates adaptability to both federated and centralized learning environments,accommodating a wide array of IoT devices.Our evaluation of the GAADPSDNN system using the recently complied WUSTL-IIoT and Edge-IIoT datasets underscores its efficacy.Achieving an impressive overall accuracy of 98.18%on the Edge-IIoT dataset,the GAADPSDNN outperforms the standard deep neural network(DNN)classifier with 94.11%accuracy.Furthermore,with the proposed enhancements,the accuracy of the unoptimized random forest classifier(80.89%)is improved to 93.51%,while the overall accuracy(98.18%)surpasses the results(93.91%,94.67%,94.94%,and 94.96%)achieved when alternative systems based on diverse optimization techniques and the same dataset are employed.The proposed optimization techniques increase the effectiveness of the anomaly detection system by efficiently achieving high accuracy and reducing the computational load on IoT devices through the adaptive selection of active features.
文摘The Smart Grid is an enhancement of the traditional grid system and employs new technologies and sophisticated communication techniques for electrical power transmission and distribution. The Smart Grid’s communication network shares information about status of its several integrated IEDs (Intelligent Electronic Devices). However, the IEDs connected throughout the Smart Grid, open opportunities for attackers to interfere with the communications and utilities resources or take clients’ private data. This development has introduced new cyber-security challenges for the Smart Grid and is a very concerning issue because of emerging cyber-threats and security incidents that have occurred recently all over the world. The purpose of this research is to detect and mitigate Distributed Denial of Service [DDoS] with application to the Electrical Smart Grid System by deploying an optimized Stealthwatch Secure Network analytics tool. In this paper, the DDoS attack in the Smart Grid communication networks was modeled using Stealthwatch tool. The simulated network consisted of Secure Network Analytic tools virtual machines (VMs), electrical Grid network communication topology, attackers and Target VMs. Finally, the experiments and simulations were performed, and the research results showed that Stealthwatch analytic tool is very effective in detecting and mitigating DDoS attacks in the Smart Grid System without causing any blackout or shutdown of any internal systems as compared to other tools such as GNS3, NeSSi2, NISST Framework, OMNeT++, INET Framework, ReaSE, NS2, NS3, M5 Simulator, OPNET, PLC & TIA Portal management Software which do not have the capability to do so. Also, using Stealthwatch tool to create a security baseline for Smart Grid environment, contributes to risk mitigation and sound security hygiene.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under Grant Number(DGSSR-2023-02-02516).
文摘This study presents a comprehensive and secure architectural framework for the Internet of Medical Things(IoMT),integrating the foundational principles of the Confidentiality,Integrity,and Availability(CIA)triad along with authentication mechanisms.Leveraging advanced Machine Learning(ML)and Deep Learning(DL)techniques,the proposed system is designed to safeguard Patient-Generated Health Data(PGHD)across interconnected medical devices.Given the increasing complexity and scale of cyber threats in IoMT environments,the integration of Intrusion Detection and Prevention Systems(IDPS)with intelligent analytics is critical.Our methodology employs both standalone and hybrid ML&DL models to automate threat detection and enable real-time analysis,while ensuring rapid and accurate responses to a diverse array of attacks.Emphasis is placed on systematic model evaluation using detection metrics such as accuracy,False Alarm Rate(FAR),and False Discovery Rate(FDR),with performance validation through cross-validation and statistical significance testing.Experimental results based on the Edge-IIoTset dataset demonstrate the superior performance of ensemble-based ML models such as Extreme Gradient Boosting(XGB)and hybrid DL models such as Convolutional Neural Networks with Autoencoders(CNN+AE),which achieved detection accuracies of 96%and 98%,respectively,with notably low FARs.These findings underscore the effectiveness of combining traditional security principles with advanced AI-driven methodologies to ensure secure,resilient,and trustworthy healthcare systems within the IoMT ecosystem.
基金The Authors would like to acknowledge the support of King Fahd University of Petroleum and Minerals for this research.
文摘Fog computing(FC)is a networking paradigm where wireless devices known as fog nodes are placed at the edge of the network(close to the Internet of Things(IoT)devices).Fog nodes provide services in lieu of the cloud.Thus,improving the performance of the network and making it attractive to social media-based systems.Security issues are one of the most challenges encountered in FC.In this paper,we propose an anomalybased Intrusion Detection and Prevention System(IDPS)against Man-in-theMiddle(MITM)attack in the fog layer.The system uses special nodes known as Intrusion Detection System(IDS)nodes to detect intrusion in the network.They periodically monitor the behavior of the fog nodes in the network.Any deviation from normal network activity is categorized as malicious,and the suspected node is isolated.ExponentiallyWeighted Moving Average(EWMA)is added to the system to smooth out the noise that is typically found in social media communications.Our results(with 95%confidence)show that the accuracy of the proposed system increases from 80%to 95%after EWMA is added.Also,with EWMA,the proposed system can detect the intrusion from 0.25–0.5 s seconds faster than that without EWMA.However,it affects the latency of services provided by the fog nodes by at least 0.75–1.3 s.Finally,EWMA has not increased the energy overhead of the system,due to its lightweight.
基金The work is fully sponsored by the research project grant FRGS/1/2021/ICT07/UITM/02/3。
文摘Due to polymorphic nature of malware attack,a signature-based analysis is no longer sufficient to solve polymorphic and stealth nature ofmalware attacks.On the other hand,state-of-the-art methods like deep learning require labelled dataset as a target to train a supervised model.This is unlikely to be the case in production network as the dataset is unstructured and has no label.Hence an unsupervised learning is recommended.Behavioral study is one of the techniques to elicit traffic pattern.However,studies have shown that existing behavioral intrusion detection model had a few issues which had been parameterized into its common characteristics,namely lack of prior information(p(θ)),and reduced parameters(θ).Therefore,this study aims to utilize the previously built Feature Selection Model subsequently to design a Predictive Analytics Model based on Bayesian Network used to improve the analysis prediction.Feature Selection Model is used to learn significant label as a target and Bayesian Network is a sophisticated probabilistic approach to predict intrusion.Finally,the results are extended to evaluate detection,accuracy and false alarm rate of the model against the subject matter expert model,Support Vector Machine(SVM),k nearest neighbor(k-NN)using simulated and ground-truth dataset.The ground-truth dataset from the production traffic of one of the largest healthcare provider in Malaysia is used to promote realism on the real use case scenario.Results have shown that the proposed model consistently outperformed other models.
文摘Zhao Fanghui is a researcher,doctoral supervisor,distinguished professor at Peking Union Medical College and director of the Department of Cancer Epidemiology,National Cancer Center and Cancer Hospital,Chinese Academy of Medical Sciences.Her research focuses on the prevention and early detection of cancer;more specifically,research into ways to prevent cervical and breast cancers through comprehensive approaches.She is committed to improving health equity and accessibility for underserved regions,and especially vulnerable populations,and to advancing China,s cancer prevention initiatives.She received the WHO/IARC Senior Visiting Scientist Award in 2015,and the APEC Healthy Women,Healthy Economies Research Prize in 2020.In 2025,she was honored as a National March 8th Red-Banner Holder.
基金supported by Shanghai Oriental Talent Youth Program(QNKJ2024006)National Natural Science Foundation of China(82170555,32300523,32570769,and 62132015)+1 种基金Shanghai Academic/Technology Research Leader(22XD1422400)Shuguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission(22SG06).
文摘Understanding the cellular origins and early evolutionary dynamics that drive the initiation of carcinogenesis is critical to advancing early detection and prevention strategies.By characterizing key molecular,cellular and niche events at the precancerous tipping point of early gastric cancer(EGC),we aimed to develop more precise screening tools and design targeted interventions to prevent malignant transformation at this stage.We utilized our AI models to integrate spatial multimodal data from nine EGC endoscopic submucosal dissection(ESD)samples(covering sequential stages from normal to cancer),construct a spatial-temporal profile of disease progression,and identify a critical tipping point(PMC_P)characterized by an immune-suppressive microenvironment during early cancer development.At this stage,inflammatory pit mucous cells with stemness(PMC_2)interact with fibroblasts via NAMPT→ITGA5/ITGB1 and with macrophages via AREG→EGFR/ERBB2 signaling,fostering cancer initiation.We established gastric precancerous cell lines and organoids to demonstrate that NAMPT and AREG promote cellular proliferation in vitro.Furthermore,in the transgenic CEA-SV40 mouse model,targeting AREG and/or NAMPT disrupted key cell interactions,inhibited the JAK-STAT,MAPK,and NFκB pathways,and reduced PD-L1 expression,which was also confirmed by western blot in vitro.These interventions delayed disease progression,reversed the immunosuppressive microenvironment,and prevented malignant transformation.Clinical validation was conducted using endoscopically resected EGC specimens.Our study provides a precise spatiotemporal depiction of EGC development and identifies novel diagnostic markers and therapeutic targets for early intervention.
