scholarly journals Fuzzy Based Advanced Hybrid Intrusion Detection System to Detect Malicious Nodes in Wireless Sensor Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Rupinder Singh ◽  
Jatinder Singh ◽  
Ravinder Singh
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection Rate ◽  
Detection System ◽  
Hybrid Technique ◽  
Sybil Attack ◽  
Wormhole Attack ◽  
Positive Rate ◽  
Hello Flood

In this paper, an Advanced Hybrid Intrusion Detection System (AHIDS) that automatically detects the WSNs attacks is proposed. AHIDS makes use of cluster-based architecture with enhanced LEACH protocol that intends to reduce the level of energy consumption by the sensor nodes. AHIDS uses anomaly detection and misuse detection based on fuzzy rule sets along with the Multilayer Perceptron Neural Network. The Feed Forward Neural Network along with the Backpropagation Neural Network are utilized to integrate the detection results and indicate the different types of attackers (i.e., Sybil attack, wormhole attack, and hello flood attack). For detection of Sybil attack, Advanced Sybil Attack Detection Algorithm is developed while the detection of wormhole attack is done by Wormhole Resistant Hybrid Technique. The detection of hello flood attack is done by using signal strength and distance. An experimental analysis is carried out in a set of nodes; 13.33% of the nodes are determined as misbehaving nodes, which classified attackers along with a detection rate of the true positive rate and false positive rate. Sybil attack is detected at a rate of 99,40%; hello flood attack has a detection rate of 98, 20%; and wormhole attack has a detection rate of 99, 20%.

scholarly journals Identifying Nodes with Maladaptive Behavior in WSN’s by Using Hybrid Incursion Identification Approach for Secure Wireless

2021 ◽  
Author(s):  
G. Amudha
Keyword(s):  
Neural Network ◽  
Detection Rate ◽  
Maladaptive Behavior ◽  
Sensor Nodes ◽  
Hybrid Technique ◽  
Exploratory Research ◽  
Sybil Attack ◽  
Feed Forward Neural Network ◽  
Wormhole Attack ◽  
Identification Approach

Abstract In this study, to detect attacks of WSNs, a Hybrid Incursion Identification Approach (HIIA) is proposed. To reduce the amount of Energy Consumption (EC) of the sensor nodes, the HIIA mechanism utilizes a cluster-oriented approach with the LEACH protocol. For misuse observation and anomaly recognition, with MPNN (Multilayer Perceptron Neural Network) depended on fuzzy rule sets, HIIA structure is utilized. To refer to various varieties of attackers and to harmonize the identification results, with appendicle NN, FFNN (Feed Forward Neural Network) is utilized, that means Sybil Attack (SA), Hello Flood Attack (HFA) and Wormhole Attack (WA). To detect a SA, Improved SA Algorithm developed. Similarly, to detect a WA, that particular method is developed by Wormhole Anti-Hybrid Technique. Using the distance and power of the signal, HFA is detected. An exploratory research is conveyed out in a group of nodes. The nodes that misbehave in them are all determined. This proposed method, detects the performance of the accuracy, precision-recall and EC. This proposed method also finds the WA Detection Rate, HFA detection rate and the SA Detection Rate, respectively.

scholarly journals Anomaly-Based Intrusion Detection System using Supervised Learning Algorithm Artificial Neural Network and Ant Colony Optimization with Feature Selection

2020 ◽  
Vol 9 (3) ◽  
pp. 2475-2481
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Ant Colony Optimization ◽  
Supervised Learning ◽  
Intrusion Detection System ◽  
Detection Rate ◽  
Learning Algorithm ◽  
Detection System

In the advent of the cyber world, all know that cyber security is randomly used research area for researchers to secure host, network, and data because of increasingly complex attacks. In the advent of anomaly-based intrusion detection system, various techniques are applied to detect intrusion on system or network. This approach attains an extreme detection rate and accuracy but there may be overhead acquired to build and training them. The objective of this paper is to detect the intrusion of a system by proposing a Data mining technique which is based on supervised learning algorithm for training dataset. Artificial neural network (ANN) and Ant Colony Optimization (ACO) with feature selection are the basics of the proposed scheme. ACO work on a population-based algorithm and is motivated by the pheromone trail laying behavior of real ants, in which NSL-KDD Cup99 Dataset is used. Empirical Results clearly explain that the proposed system can attain an overall detection rate of 88% and time complexity of 0.343 sec, which is satisfactory when compared to other anomaly-based schemes.

scholarly journals HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 834
Author(s):  
Muhammad Ashfaq Khan
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Recurrent Neural Network ◽  
Intrusion Detection System ◽  
Detection System ◽  
Attack Detection ◽  
Network Intrusion Detection ◽  
Complex Nature ◽  
Network Intrusion ◽  
Network Intrusion Detection System

Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks. Currently, deep learning (DL) and machine learning (ML) are being applied in different domains, especially information security, for developing effective ID systems. These ID systems are capable of detecting malicious threats automatically and on time. However, malicious threats are occurring and changing continuously, so the network requires a very advanced security solution. Thus, creating an effective and smart ID system is a massive research problem. Various ID datasets are publicly available for ID research. Due to the complex nature of malicious attacks with a constantly changing attack detection mechanism, publicly existing ID datasets must be modified systematically on a regular basis. So, in this paper, a convolutional recurrent neural network (CRNN) is used to create a DL-based hybrid ID framework that predicts and classifies malicious cyberattacks in the network. In the HCRNNIDS, the convolutional neural network (CNN) performs convolution to capture local features, and the recurrent neural network (RNN) captures temporal features to improve the ID system’s performance and prediction. To assess the efficacy of the hybrid convolutional recurrent neural network intrusion detection system (HCRNNIDS), experiments were done on publicly available ID data, specifically the modern and realistic CSE-CIC-DS2018 data. The simulation outcomes prove that the proposed HCRNNIDS substantially outperforms current ID methodologies, attaining a high malicious attack detection rate accuracy of up to 97.75% for CSE-CIC-IDS2018 data with 10-fold cross-validation.

scholarly journals An Intrusion Detection System for Smart Grid Neighborhood Area Network

2021 ◽  
Author(s):  
Nasim Beigi Mohammadi
Keyword(s):  
Intrusion Detection ◽  
Smart Grid ◽  
Intrusion Detection System ◽  
Detection System ◽  
Area Network ◽  
Wormhole Attack ◽  
Detection Systems ◽  
Wireless Mesh ◽  
First Time ◽  
Neighborhood Area Network

Smart grid is expected to improve the efficiency, reliability and economics of current energy systems. Using two-way flow of electricity and information, smart grid builds an automated, highly distributed energy delivery network. In this thesis, we present the requirements for intrusion detection systems in smart grid, neighborhood area network (NAN) in particular. We propose an intrusion detection system (IDS) that considers the constraints and requirements of the NAN. It captures the communication and computation overhead constraints as well as the lack of a central point to install the IDS. The IDS is distributed on some nodes which are powerful in terms of memory, computation and the degree of connectivity. Our IDS uses an analytical approach for detecting Wormhole attack. We simulate wireless mesh NANs in OPNET Modeler and for the first time, we integrate our analytical model in Maple from MapleSoft with our OPNET simulation model.

scholarly journals DCNN-IDS : Deep Convolutional Neural Network based Intrusion Detection System

2020 ◽  
Author(s):  
Sriram Srinivasan ◽  
Shashank A ◽  
vinayakumar R ◽  
Soman KP
Keyword(s):  
Neural Network ◽  
Intrusion Detection ◽  
Convolutional Neural Network ◽  
Intrusion Detection System ◽  
Input Data ◽  
Detection System ◽  
Deep Convolutional Neural Network ◽  
Traffic Data ◽  
Data Set ◽  
Research Problems

In the present era, cyberspace is growing tremendously and the intrusion detection system (IDS) plays a key role in it to ensure information security. The IDS, which works in network and host level, should be capable of identifying various malicious attacks. The job of network-based IDS is to differentiate between normal and malicious traffic data and raise an alert in case of an attack. Apart from the traditional signature and anomaly-based approaches, many researchers have employed various deep learning (DL) techniques for detecting intrusion as DL models are capable of extracting salient features automatically from the input data. The application of deep convolutional neural network (DCNN), which is utilized quite often for solving research problems in image processing and vision fields, is not explored much for IDS. In this paper, a DCNN architecture for IDS which is trained on KDDCUP 99 data set is proposed. This work also shows that the DCNN-IDS model performs superior when compared with other existing works.

scholarly journals A feature selection algorithm combining information gain and multi-objective genetic search for intrusion detection system

2021 ◽  
Vol 336 ◽  
pp. 08008
Author(s):  
Tao Xie
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection Rate ◽  
Information Gain ◽  
Detection System ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Genetic Search ◽  
Multi Objective

In order to improve the detection rate and speed of intrusion detection system, this paper proposes a feature selection algorithm. The algorithm uses information gain to rank the features in descending order, and then uses a multi-objective genetic algorithm to gradually search the ranking features to find the optimal feature combination. We classified the Kddcup98 dataset into five classes, DOS, PROBE, R2L, and U2R, and conducted numerous experiments on each class. Experimental results show that for each class of attack, the proposed algorithm can not only speed up the feature selection, but also significantly improve the detection rate of the algorithm.

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