A set of constrained Newton methods were developed for static traffic assignment problems. The Newton formula uses the gradient of the objective function to determine an improved feasible direction scaled by the secon...A set of constrained Newton methods were developed for static traffic assignment problems. The Newton formula uses the gradient of the objective function to determine an improved feasible direction scaled by the second-order derivatives of the objective function. The column generation produces the active paths necessary for each origin-destination pair. These methods then select an optimal step size or make an orthogonal projection to achieve fast, accurate convergence. These Newton methods based on the constrained Newton formula utilize path information to explicitly implement Wardrop＇s principle in the transport network modelling and complement the traffic assignment algorithms. Numerical examples are presented to compare the performance with all possible Newton methods. The computational results show that the optimal-step Newton methods have much better convergence than the fixed-step ones, while the Newton method with the unit step size is not always efficient for traffic assignment problems. Furthermore, the optimal-step Newton methods are relatively robust for all three of the tested benchmark networks of traffic assignment problems.展开更多
Detecting the anomalous entity in real-time network traffic is a popular area of research in recent times.Very few researches have focused on creating malware that fools the intrusion detection system and this paper f...Detecting the anomalous entity in real-time network traffic is a popular area of research in recent times.Very few researches have focused on creating malware that fools the intrusion detection system and this paper focuses on this topic.We are using Deep Convolutional Generative Adversarial Networks(DCGAN)to trick the malware classifier to believe it is a normal entity.In this work,a new dataset is created to fool the Artificial Intelligence(AI)based malware detectors,and it consists of different types of attacks such as Denial of Service(DoS),scan 11,scan 44,botnet,spam,User Datagram Portal(UDP)scan,and ssh scan.The discriminator used in the DCGAN discriminates two different attack classes(anomaly and synthetic)and one normal class.The model collapse,instability,and vanishing gradient issues associated with the DCGAN are overcome using the proposed hybrid Aquila optimizer-based Mine blast harmony search algorithm(AO-MBHS).This algorithm helps the generator to create realistic malware samples to be undetected by the discriminator.The performance of the proposed methodology is evaluated using different performance metrics such as training time,detection rate,F-Score,loss function,Accuracy,False alarm rate,etc.The superiority of the hybrid AO-MBHS based DCGAN model is noticed when the detection rate is changed to 0 after the retraining method to make the defensive technique hard to be noticed by the malware detection system.The support vector machines(SVM)is used as the malicious traffic detection application and its True positive rate(TPR)goes from 80%to 0%after retraining the proposed model which shows the efficiency of the proposed model in hiding the samples.展开更多
基金Supported by the National Natural Science Foundation of China (No.50678037)the National Key Basic Research and Development (973) Program of China (No.2006CB705500)the National High-Tech Research and Development (863) Program of China (No.2007AA11Z205)
文摘A set of constrained Newton methods were developed for static traffic assignment problems. The Newton formula uses the gradient of the objective function to determine an improved feasible direction scaled by the second-order derivatives of the objective function. The column generation produces the active paths necessary for each origin-destination pair. These methods then select an optimal step size or make an orthogonal projection to achieve fast, accurate convergence. These Newton methods based on the constrained Newton formula utilize path information to explicitly implement Wardrop＇s principle in the transport network modelling and complement the traffic assignment algorithms. Numerical examples are presented to compare the performance with all possible Newton methods. The computational results show that the optimal-step Newton methods have much better convergence than the fixed-step ones, while the Newton method with the unit step size is not always efficient for traffic assignment problems. Furthermore, the optimal-step Newton methods are relatively robust for all three of the tested benchmark networks of traffic assignment problems.
基金This project was funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under Grant No.RG-91-611-42.
文摘Detecting the anomalous entity in real-time network traffic is a popular area of research in recent times.Very few researches have focused on creating malware that fools the intrusion detection system and this paper focuses on this topic.We are using Deep Convolutional Generative Adversarial Networks(DCGAN)to trick the malware classifier to believe it is a normal entity.In this work,a new dataset is created to fool the Artificial Intelligence(AI)based malware detectors,and it consists of different types of attacks such as Denial of Service(DoS),scan 11,scan 44,botnet,spam,User Datagram Portal(UDP)scan,and ssh scan.The discriminator used in the DCGAN discriminates two different attack classes(anomaly and synthetic)and one normal class.The model collapse,instability,and vanishing gradient issues associated with the DCGAN are overcome using the proposed hybrid Aquila optimizer-based Mine blast harmony search algorithm(AO-MBHS).This algorithm helps the generator to create realistic malware samples to be undetected by the discriminator.The performance of the proposed methodology is evaluated using different performance metrics such as training time,detection rate,F-Score,loss function,Accuracy,False alarm rate,etc.The superiority of the hybrid AO-MBHS based DCGAN model is noticed when the detection rate is changed to 0 after the retraining method to make the defensive technique hard to be noticed by the malware detection system.The support vector machines(SVM)is used as the malicious traffic detection application and its True positive rate(TPR)goes from 80%to 0%after retraining the proposed model which shows the efficiency of the proposed model in hiding the samples.
