scholarly journals Preventing Man-in-The-Middle (MiTM) Attack of GSM Calls

2021 ◽  
Vol 5 (4) ◽  
pp. 63-68
Author(s):  
B. I. Bakare ◽  
S. M. Ekolama
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Attack ◽  
Network Intrusion ◽  
Cellular Telephony ◽  
Depth Analysis ◽  
Man In The Middle ◽  
Internet Presence ◽  
Artificial Neural ◽  
Mitm Attack

Preventing man-in-the-middle (MiTM) attack using Artificial Neural Network refers to an in depth analysis of how calls are made vis-a-viz the structure of the inter-related operations that binds the respective subsystems within the GSM Architecture during calls. Calls in the GSM network is a request from aMobile Station (MS). This request has faced severe attacks due to the network’s access to Internet presence that has made its way into cellular telephony, creating a vulnerable and susceptible network attack such as Man-in-the-middle. This paper proffer solution to Man-in-the-middle attack during GSM calls by using Artificial Neural Network which can be embedded into the Protocol Stack to detect network intrusion and prevent Man-in-the-middle attack to obtain hitch-free local and international calls.

scholarly journals Classification of Intrusion using Artificial Neural Network with GWO

2020 ◽  
Vol 9 (4) ◽  
pp. 599-606
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Detection System ◽  
Denial Of Service ◽  
Data Set ◽  
Detection Systems ◽  
Network Intrusion ◽  
Network Intrusion Detection Systems ◽  
Artificial Neural

In the present milieu of connected world, where security is the major concern, Intrusion Detection System is the prominent area of research to deal with various types of attacks in network. Intrusion detection systems (IDS) finds the dynamic and malicious traffic of network, in accordance to the aspect of network. Various form of IDS has been developed working on distinctive approaches. One popular approach is machine learning in which various algorithms like ANN, SVM etc. have been used. But the most prominent method used is ANN. The performance of the ANN can significantly be improved by combining it with different metaheuristic algorithms. In present work, GWO is used to optimize ANN. For this KDD-99 data-set is used to classify various types of attacks i.e. denial of service (DOS), normal and other form of attack. The present paper provides detailed analysis of the performance of Artificial Neural Network and optimized Artificial Neural Network with GA, PSO and GWO. The research shows that ANN with GWO outperform as compared to others (ANN, ANN with PSO and ANN with GA).

Hybrid Weighted K-Means Clustering and Artificial Neural Network for an Anomaly-Based Network Intrusion Detection System

2018 ◽  
Vol 27 (2) ◽  
pp. 135-147 ◽  
Author(s):  
Rafath Samrin ◽  
Devara Vasumathi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Clustering Algorithm ◽  
Detection System ◽  
Similarity Measures ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Benchmark Database ◽  
Artificial Neural

AbstractDespite the rapid developments in data technology, intruders are among the most revealed threats to security. Network intrusion detection systems are now a typical constituent of network security structures. In this paper, we present a combined weighted K-means clustering algorithm with artificial neural network (WKMC+ANN)-based intrusion identification scheme. This paper comprises two modules: clustering and intrusion detection. The input dataset is gathered into clusters with the usage of WKMC in clustering module. In the intrusion detection module, the clustered information is trained with the utilization of ANN and its structure is stored. In the testing process, the data are tested by choosing the most suitable ANN classifier, which corresponds to the closest cluster to the test data, according to distance or similarity measures. For experimental evaluation, we used the benchmark database, and the results clearly demonstrated that the proposed technique outperformed the existing technique by having better accuracy.

Hacker Intrusion Detection System Based on Artificial Neural Network

2012 ◽  
Vol 263-266 ◽  
pp. 2924-2928
Author(s):  
Jing Huang ◽  
Hai Bin Chen ◽  
Jiang Zhang ◽  
Han Bo Zhang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
System Model ◽  
Data Set ◽  
Advantages And Disadvantages ◽  
Network Intrusion ◽  
Artificial Neural

In this paper, some scholars’ idea of applying neural network technology in the design of hacker intrusion detection system model and making a hacker intrusion detection system model based on artificial neural network is adopted. This study selects KDDCup’99 for network intrusion detection data set to learn the characteristics of the intrusion accurately; completes the normalization of all characteristics to achieve rapid convergence of the artificial neural network; analyses the advantages and disadvantages of different neural network training functions; achieves a high accuracy rate for intrusion detection successfully.

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