Nowadays, the network Intrusion Detection System (IDS) is a crucial cybersecurity technique that predicts unauthorized access, and exploits malfunction of computer networks. Traditional IDS struggle with classifying m...
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The self-configured, autonomous, and framework-free modes of communication that mobile adhoc networks (MANETs) offer have revolutionized our culture. As a result, efforts have been made to explore ways to maximize the...
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ISBN:
(数字)9798331527495
ISBN:
(纸本)9798331527501
The self-configured, autonomous, and framework-free modes of communication that mobile adhoc networks (MANETs) offer have revolutionized our culture. As a result, efforts have been made to explore ways to maximize the potential of MANETs through increased and improved utilization. Standards for AI have been developed thanks to the most recent release of new machine learning technologies. Different security-related issues from malware assaults affect mobile ad hoc networks (MANETs). Any node operates as a router to move data without centralized control, making nodes more vulnerable to threats from other nodes or attackers because of their brief existence. Because of this, MANET needs particular security policies to detect the incorrect entrance of misbehaving nodes. If all nodes are self-assured and correctly collaborate, the networks function better. The paper presents a practical artificial intelligence algorithm-based security system that uses AdaBoost and DT algorithms to recognize and identify packet falling nodes, classify information packets as normal or abnormal, and detect insider threats in real-time. The results showed that DT performed better than AdaBoost, with a 98% accurate prediction rate. Consequently, DT is better able to recognize damaging attacks in MANETs.
The self-configured, autonomous, and framework-free modes of communication that mobile adhoc networks (MANETs) offer have revolutionized our culture. As a result, efforts have been made to explore ways to maximize the...
详细信息
Nowadays, the network Intrusion Detection System (IDS) is a crucial cybersecurity technique that predicts unauthorized access, and exploits malfunction of computer networks. Traditional IDS struggle with classifying m...
详细信息
ISBN:
(数字)9798350375442
ISBN:
(纸本)9798350375459
Nowadays, the network Intrusion Detection System (IDS) is a crucial cybersecurity technique that predicts unauthorized access, and exploits malfunction of computer networks. Traditional IDS struggle with classifying multiple, varied attack patterns which often lead to poor performance in detecting critical threats. In order to improve detection accuracy, identify numerous attacks, developing an efficient system becomes crucial which handles imbalanced data. Thus, this paper presents an Adaptive network IDS that employs a hybrid approach by combining the Improved Beetle Antenna Search (BAS) Algorithm and the Sparrow Search Algorithm (SSA) to enhance detection accuracy and adaptability in identifying cyber threats on NSL-KDD and CSE-CIC-IDS2018 datasets. Additionally, a Bi-directional Long Short-Term Memory (Bi-LSTM) network is utilized to reduce the False Positive Rate (FPR) and increase the entire efficiency of the system by effectively overcoming the challenges such as imbalanced data and low detection rate. Experimental evaluations on benchmark intrusion datasets such as NSL-KDD and CSE-CIC-IDS2018 using proposed IBAS-SSA + Bi-LSTM demonstrate higher detection results with accuracy of 98.12% and 98.64% respectively, when compared to other traditional IDS methods.
Based on a new efficient identification technique of active constraints introduced in this paper, a new sequential systems of linear equations （SSLE） algorithm generating feasible iterates is proposed for solving no...
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Based on a new efficient identification technique of active constraints introduced in this paper, a new sequential systems of linear equations （SSLE） algorithm generating feasible iterates is proposed for solving nonlinear optimization problems with inequality constraints. In this paper, we introduce a new technique for constructing the system of linear equations, which recurs to a perturbation for the gradients of the constraint functions. At each iteration of the new algorithm, a feasible descent direction is obtained by solving only one system of linear equations without doing convex combination. To ensure the global convergence and avoid the Maratos effect, the algorithm needs to solve two additional reduced systems of linear equations with the same coefficient matrix after finite iterations. The proposed algorithm is proved to be globally and superlinearly convergent under some mild conditions. What distinguishes this algorithm from the previous feasible SSLE algorithms is that an improving direction is obtained easily and the computation cost of generating a new iterate is reduced. Finally, a preliminary implementation has been tested.
Wireless sensor networks consist of a large number of low-power, small-scale sensors with limited processing and communication capabilities. Such networks are usually applied to gather data from interested area or spe...
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Wireless sensor networks consist of a large number of low-power, small-scale sensors with limited processing and communication capabilities. Such networks are usually applied to gather data from interested area or specific environment and deliver to remote users for analyzing or monitoring. Because of sensing devices are usually powered by batteries, it is a great challenge to meet the performance of long system lifetime required by different applications under limited power. In the densely deployed sensor networks, the area or data sensed by neighboring sensors may overlap. In recent researches, coverage preserved node scheduling has been proposed to conserve power and provide sensing reliability. By selecting appropriate sensors into sleep state, the system lifetime can be extended without losing coverage. In this paper, we propose a cluster-based coverage-preserved node scheduling scheme. We divide sensors into clusters and group cluster members into sponsor sets based on neighbor information. The proposed approach distributes the workloads among sponsor set nodes and ensures sufficient coverage as long as possible
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