The anonymity of the darknet makes it attractive to secure communication lines from censorship.The analysis,monitoring,and categorization of Internet network traffic are essential for detecting darknet traffic that ca...The anonymity of the darknet makes it attractive to secure communication lines from censorship.The analysis,monitoring,and categorization of Internet network traffic are essential for detecting darknet traffic that can generate a comprehensive characterization of dangerous users and assist in tracing malicious activities and reducing cybercrime.Furthermore,classifying darknet traffic is essential for real-time applications such as the timely monitoring of malware before attacks occur.This paper presents a two-stage deep network chain for detecting and classifying darknet traffic.In the first stage,anonymized darknet traffic,including VPN and Tor traffic related to hidden services provided by darknets,is detected.In the second stage,traffic related to VPNs and Tor services is classified based on their respective applications.The methodology of this paper was verified on a benchmark dataset containing VPN and Tor traffic.It achieved an accuracy of 96.8%and 94.4%in the detection and classification stages,respectively.Optimization and parameter tuning were performed in both stages to achieve more accurate results,enabling practitioners to combat alleged malicious activities and further detect such activities after outbreaks.In the classification stage,it was observed that the misclassifications were due to the audio and video streaming commonly used in shared real-time protocols.However,in cases where it is desired to distinguish between such activities accurately,the presented deep chain classifier can accommodate additional classifiers.Furthermore,additional classifiers could be added to the chain to categorize specific activities of interest further.展开更多
Parkinson’s disease(PD)is a neurodegenerative disease in the central nervous system.Recently,more researches have been conducted in the determination of PD prediction which is really a challenging task.Due to the dis...Parkinson’s disease(PD)is a neurodegenerative disease in the central nervous system.Recently,more researches have been conducted in the determination of PD prediction which is really a challenging task.Due to the disorders in the central nervous system,the syndromes like off sleep,speech disorders,olfactory and autonomic dysfunction,sensory disorder symptoms will occur.The earliest diagnosing of PD is very challenging among the doctors community.There are techniques that are available in order to predict PD using symptoms and disorder measurement.It helps to save a million lives of future by early prediction.In this article,the early diagnosing of PD using machine learning techniques with feature selection is carried out.In the first stage,the data preprocessing is used for the preparation of Parkinson’s disease data.In the second stage,MFEA is used for extracting features.In the third stage,the feature selection is performed using multiple feature input with a principal component analysis(PCA)algorithm.Finally,a Darknet Convolutional Neural Network(DNetCNN)is used to classify the PD patients.The main advantage of using PCA-DNetCNN is that,it provides the best classification in the image dataset using YOLO.In addition to that,the results of various existing methods are compared and the proposed DNetCNN proves better accuracy,performance in detecting the PD at the initial stages.DNetCNN achieves 97.5%of accuracy in detecting PD as early.Besides,the other performance metrics are compared in the result evaluation and it is proved that the proposed model outperforms all the other existing models.展开更多
The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication channels.Effective detection of clandestine darknet traffic is therefore critical yet immensely challeng...The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication channels.Effective detection of clandestine darknet traffic is therefore critical yet immensely challenging.This research demonstrates how advanced machine learning and specialized deep learning techniques can significantly enhance darknet traffic analysis to strengthen cybersecurity.Combining diverse classifiers such as random forest and naïve Bayes with a novel spiking neural network architecture provides a robust foundation for identifying concealed threats.Evaluation on the CIC-Darknet2020 dataset establishes state-of-the-art results with 98%accuracy from the random forest model and 84.31%accuracy from the spiking neural network.This pioneering application of artificial intelligence advances the frontiers in analyzing the complex characteristics and behaviours of darknet communication.The proposed techniques lay the groundwork for improved threat intelligence,real-time monitoring,and resilient cyber defense systems against the evolving landscape of cyber threats.展开更多
文摘The anonymity of the darknet makes it attractive to secure communication lines from censorship.The analysis,monitoring,and categorization of Internet network traffic are essential for detecting darknet traffic that can generate a comprehensive characterization of dangerous users and assist in tracing malicious activities and reducing cybercrime.Furthermore,classifying darknet traffic is essential for real-time applications such as the timely monitoring of malware before attacks occur.This paper presents a two-stage deep network chain for detecting and classifying darknet traffic.In the first stage,anonymized darknet traffic,including VPN and Tor traffic related to hidden services provided by darknets,is detected.In the second stage,traffic related to VPNs and Tor services is classified based on their respective applications.The methodology of this paper was verified on a benchmark dataset containing VPN and Tor traffic.It achieved an accuracy of 96.8%and 94.4%in the detection and classification stages,respectively.Optimization and parameter tuning were performed in both stages to achieve more accurate results,enabling practitioners to combat alleged malicious activities and further detect such activities after outbreaks.In the classification stage,it was observed that the misclassifications were due to the audio and video streaming commonly used in shared real-time protocols.However,in cases where it is desired to distinguish between such activities accurately,the presented deep chain classifier can accommodate additional classifiers.Furthermore,additional classifiers could be added to the chain to categorize specific activities of interest further.
文摘Parkinson’s disease(PD)is a neurodegenerative disease in the central nervous system.Recently,more researches have been conducted in the determination of PD prediction which is really a challenging task.Due to the disorders in the central nervous system,the syndromes like off sleep,speech disorders,olfactory and autonomic dysfunction,sensory disorder symptoms will occur.The earliest diagnosing of PD is very challenging among the doctors community.There are techniques that are available in order to predict PD using symptoms and disorder measurement.It helps to save a million lives of future by early prediction.In this article,the early diagnosing of PD using machine learning techniques with feature selection is carried out.In the first stage,the data preprocessing is used for the preparation of Parkinson’s disease data.In the second stage,MFEA is used for extracting features.In the third stage,the feature selection is performed using multiple feature input with a principal component analysis(PCA)algorithm.Finally,a Darknet Convolutional Neural Network(DNetCNN)is used to classify the PD patients.The main advantage of using PCA-DNetCNN is that,it provides the best classification in the image dataset using YOLO.In addition to that,the results of various existing methods are compared and the proposed DNetCNN proves better accuracy,performance in detecting the PD at the initial stages.DNetCNN achieves 97.5%of accuracy in detecting PD as early.Besides,the other performance metrics are compared in the result evaluation and it is proved that the proposed model outperforms all the other existing models.
文摘The rapidly evolving darknet enables a wide range of cybercrimes through anonymous and untraceable communication channels.Effective detection of clandestine darknet traffic is therefore critical yet immensely challenging.This research demonstrates how advanced machine learning and specialized deep learning techniques can significantly enhance darknet traffic analysis to strengthen cybersecurity.Combining diverse classifiers such as random forest and naïve Bayes with a novel spiking neural network architecture provides a robust foundation for identifying concealed threats.Evaluation on the CIC-Darknet2020 dataset establishes state-of-the-art results with 98%accuracy from the random forest model and 84.31%accuracy from the spiking neural network.This pioneering application of artificial intelligence advances the frontiers in analyzing the complex characteristics and behaviours of darknet communication.The proposed techniques lay the groundwork for improved threat intelligence,real-time monitoring,and resilient cyber defense systems against the evolving landscape of cyber threats.
