Optimization scheme for intrusion detection scheme GBDT in edge computing center

2020 ◽  
Author(s):  
Ju-fu Cui ◽  
Hui Xia ◽  
Rui Zhang ◽  
Ben-xu Hu ◽  
Xiang-guo Cheng
Keyword(s):  
Intrusion Detection ◽  
Edge Computing ◽  
Optimization Scheme ◽  
Detection Scheme ◽  
Computing Center

scholarly journals A novel two-phase cycle algorithm for effective cyber intrusion detection in edge computing

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yiguang Gong ◽  
Yunping Liu ◽  
Chuanyang Yin
Keyword(s):  
Intrusion Detection ◽  
Optimal Parameter ◽  
Edge Computing ◽  
Cloud Services ◽  
Second Phase ◽  
Pareto Optimal ◽  
Two Phase ◽  
Multi Objective ◽  
Phase Cycle ◽  
Positive Rate

AbstractEdge computing extends traditional cloud services to the edge of the network, closer to users, and is suitable for network services with low latency requirements. With the rise of edge computing, its security issues have also received increasing attention. In this paper, a novel two-phase cycle algorithm is proposed for effective cyber intrusion detection in edge computing based on a multi-objective genetic algorithm (MOGA) and modified back-propagation neural network (MBPNN), namely TPC-MOGA-MBPNN. In the first phase, the MOGA is employed to build a multi-objective optimization model that tries to find the Pareto optimal parameter set for MBPNN. The Pareto optimal parameter set is applied for simultaneous minimization of the average false positive rate (Avg FPR), mean squared error (MSE) and negative average true positive rate (Avg TPR) in the dataset. In the second phase, some MBPNNs are created based on the parameter set obtained by MOGA and are trained to search for a more optimal parameter set locally. The parameter set obtained in the second phase is used as the input of the first phase, and the training process is repeated until the termination criteria are reached. A benchmark dataset, KDD cup 1999, is used to demonstrate and validate the performance of the proposed approach for intrusion detection. The proposed approach can discover a pool of MBPNN-based solutions. Combining these MBPNN solutions can significantly improve detection performance, and a GA is used to find the optimal MBPNN combination. The results show that the proposed approach achieves an accuracy of 98.81% and a detection rate of 98.23% and outperform most systems of previous works found in the literature. In addition, the proposed approach is a generalized classification approach that is applicable to the problem of any field having multiple conflicting objectives.

scholarly journals An edge based hybrid intrusion detection framework for mobile edge computing

2021 ◽  
Author(s):  
Ashish Singh ◽  
Kakali Chatterjee ◽  
Suresh Chandra Satapathy
Keyword(s):  
Intrusion Detection ◽  
Real Time ◽  
Network Architecture ◽  
Denial Of Service ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Hybrid Detection ◽  
Game Theoretical Approach ◽  
Improved Performance ◽  
Edge Based

AbstractThe Mobile Edge Computing (MEC) model attracts more users to its services due to its characteristics and rapid delivery approach. This network architecture capability enables users to access the information from the edge of the network. But, the security of this edge network architecture is a big challenge. All the MEC services are available in a shared manner and accessed by users via the Internet. Attacks like the user to root, remote login, Denial of Service (DoS), snooping, port scanning, etc., can be possible in this computing environment due to Internet-based remote service. Intrusion detection is an approach to protect the network by detecting attacks. Existing detection models can detect only the known attacks and the efficiency for monitoring the real-time network traffic is low. The existing intrusion detection solutions cannot identify new unknown attacks. Hence, there is a need of an Edge-based Hybrid Intrusion Detection Framework (EHIDF) that not only detects known attacks but also capable of detecting unknown attacks in real time with low False Alarm Rate (FAR). This paper aims to propose an EHIDF which is mainly considered the Machine Learning (ML) approach for detecting intrusive traffics in the MEC environment. The proposed framework consists of three intrusion detection modules with three different classifiers. The Signature Detection Module (SDM) uses a C4.5 classifier, Anomaly Detection Module (ADM) uses Naive-based classifier, and Hybrid Detection Module (HDM) uses the Meta-AdaboostM1 algorithm. The developed EHIDF can solve the present detection problems by detecting new unknown attacks with low FAR. The implementation results illustrate that EHIDF accuracy is 90.25% and FAR is 1.1%. These results are compared with previous works and found improved performance. The accuracy is improved up to 10.78% and FAR is reduced up to 93%. A game-theoretical approach is also discussed to analyze the security strength of the proposed framework.

scholarly journals Enhanced replica detection scheme for efficient analysis of intrusion detection in MANET

2017 ◽  
Vol 7 (1.1) ◽  
pp. 565
Author(s):  
P. Bakeyalakshmi ◽  
S. K. Mahendran
Keyword(s):  
Intrusion Detection ◽  
Secure Communication ◽  
Mobile Ad Hoc Network ◽  
Delivery Ratio ◽  
Ratio Test ◽  
Network Simulator ◽  
Efficient Manner ◽  
Detection Scheme ◽  
Efficient Detection ◽  
Replica Detection

Nowadays, detection scheme of intrusion is placing a major role for efficient access and analysis in Mobile Ad-hoc network (MANET). In the past, the detection scheme of Intrusion was used to identify the efficiency of the network and in maximum systems it performs with huge rate of false alarm. In this paper, an Effective approach of the Enhanced Replica Detection scheme (ERDS) based on Sequential Probability Ratio Test (SPRT) is proposed to detect the malicious actions and to have a secure path without claim in an efficient manner. Also, provides strategies to avoid attacker and to provide secure communication. In order to have an efficient analysis of intrusion detection the proposed approach is implemented based on the anomaly. To achieve this, the detection scheme is established based on SPRT and demonstrated the performances of detection with less claim. The simulation results of control overhead, packet delivery ratio, efficient detection, energy consumption and average claims are carried out for the analysis of performance to show the improvement than the existing by using the network simulator tool. Also, the performance of the proposed system illustrated the detection of intrusion in the normal and attacker states of the network.

Secure and Reliable Knowledge-Based Intrusion Detection Using Mobile Base Stations in Smart Environments

2021 ◽  
pp. 500-513
Author(s):  
Ambika N.
Keyword(s):  
Intrusion Detection ◽  
Mobile Agent ◽  
Low Cost ◽  
Sensor Nodes ◽  
Base Stations ◽  
Wireless Sensor Nodes ◽  
Detection Scheme ◽  
Malicious Activity ◽  
Knowledge Based ◽  
Reliable Knowledge

Wireless sensor nodes are low cost tiny devices deployed in unsupervised environment. These devices require some kind of security mechanism to tackle different kinds of attacks. Intrusion detection is one such methodology used where the malicious activity is observed, and the target is isolated. Many authors have suggested different methodologies which are detailed in the work. The article also uses intrusion detection scheme to provide reliable network. A mobile agent and an assistance node aids in increasing security and efficiency of the work. The illegitimacy of the node is inferred using the data provided by the assistance and mobile agent. The work minimizes 12.9% of energy consumption, increases security by 3.025%, and increases reliability by 7.07% compared to the previous work.

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