scholarly journals Intrusion Detection Method Based on Adaptive Clonal Genetic Algorithm and Backpropagation Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yi Lu ◽  
Menghan Liu ◽  
Jie Zhou ◽  
Zhigang Li
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Detection Rate ◽  
Detection System ◽  
Poor Performance ◽  
Data Sets ◽  
Backpropagation Neural Network ◽  
External Threats ◽  
Kdd Cup 99

Intrusion Detection System (IDS) is an important part of ensuring network security. When the system faces network attacks, it can identify the source of threats in a timely and accurate manner and adjust strategies to prevent hackers from intruding. Efficient IDS can identify external threats well, but traditional IDS has poor performance and low recognition accuracy. To improve the detection rate and accuracy of IDS, this paper proposes a novel ACGA-BPNN method based on adaptive clonal genetic algorithm (ACGA) and backpropagation neural network (BPNN). ACGA-BPNN is simulated on the KDD-CUP’99 and UNSW-NB15 data sets. The simulation results indicate that, in contrast to the methods based on simulated annealing (SA) and genetic algorithm (GA), the detection rate and accuracy of ACGA-BPNN are much higher than of GA-BPNN and SA-BPNN. In the classification results of KDD-CUP’99, the classification accuracy of ACGA-BPNN is 11% higher than GA-BPNN and 24.2% higher than SA-BPNN, and F-score reaches 99.0%. In addition, ACGA-BPNN has good global searchability and its convergence speed is higher than that of GA-BPNN and SA-BPNN. Furthermore, ACGA-BPNN significantly improves the overall detection performance of IDS.

scholarly journals Cascaded Neural Network Based Data Mining Strategy for Cloud Intrusion Detection

2018 ◽  
Vol 11 (3) ◽  
pp. 1094
Author(s):  
P Purniemaa ◽  
R Jagadeesh Kannan
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Intrusion Detection ◽  
Anomaly Detection ◽  
Knowledge Discovery ◽  
Detection System ◽  
Data Sets ◽  
And Performance ◽  
Performance Results ◽  
Data Mining Strategy

In recent years data mining has acquired huge popularity in the field of knowledge discovery. Thus, this approach has inspired several researches for anomaly detection, fraud detection and intrusion detection with higher accuracy, all round generalization of the problem and its sub cases; all giving higher performance in conditions subjected to continuous alteration. Though there remain quite a few challenging problems in design and implementation of a data mining based cloud intrusion detection system, as deception tactics and modeling of behavior remains a daunting problem to compute for anomaly owing to massive size of data to process in reasonable time. In this study we present a cascaded neural network based data mining strategy for cloud intrusion detection systems (IDSs) and presents the comparison and performance results tested on DARPA Intrusion Detection (ID) Data Sets, Knowledge Discovery and Data Mining Cup, NSL-KDD dataset. The study exhibits numerous advantages offered by the presented method and give reliable results of anomaly detection in real time scenario.

Intrusion Detection Model Based on Improved Genetic Algorithm Neural Network in Computer Integrated Process System

2013 ◽  
Vol 380-384 ◽  
pp. 2708-2711
Author(s):  
Li Kun Zou ◽  
Shao Kun Liu ◽  
Guo Fu Ma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Detection System ◽  
Linear Mapping ◽  
Integrated Process ◽  
Detection Model ◽  
Model Based ◽  
Fitness Value ◽  
Process System

In order to solve the problems of high false alarm rate and fail rate in intrusion detection system of Computer Integrated Process System (CIPS) network, this paper takes advantage that Genetic Algorithm (GA) possesses overall optimization seeking ability and neural network has formidable approaching ability to the non-linear mapping to propose an intrusion detection model based on Genetic Algorithm Neural Network (GANN) with self-learning and adaptive capacity, which includes data collection module, data preprocessing module, neural network analysis module and intrusion alarm module. To overcome the shortcomings that GA is easy to fall into the extreme value and searches slowly, it improves the adjusting method of GANN fitness value and optimizes the parameter settings of GA. The improved GA is used to optimize BP neural network. Simulation results show that the model makes the detection rate of the system enhance to 97.11%.

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 A Hybrid Genetic-Neuro Algorithm for Cloud Intrusion Detection System

2020 ◽  
Vol 1 (2) ◽  
pp. 15-25
Author(s):  
Suresh Adithya Nallamuthu ◽  
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Performance Analysis ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Intrusion Detection System ◽  
Detection Rate ◽  
Detection System ◽  
Attack Detection ◽  
Network Systems

The security for cloud network systems is essential and significant to secure the data source from intruders and attacks. Implementing an intrusion detection system (IDS) for securing from those intruders and attacks is the best option. Many IDS models are presently based on different techniques and algorithms like machine learning and deep learning. In this research, IDS for the cloud computing environment is proposed. Here in this model, the genetic algorithm (GA) and back propagation neural network (BPNN) is used for attack detection and classification. The Canadian Institute for Cyber-security CIC-IDS 2017 dataset is used for the evaluation of performance analysis. Initially, from the dataset, the data are preprocessed, and by using the genetic algorithm, the attack was detected. The detected attacks are classified using the BPNN classifier for identifying the types of attacks. The performance analysis was executed, and the results are obtained and compared with the existing machine learning-based classifiers like FC-ANN, NB-RF, KDBN, and FCM-SVM techniques. The proposed GA-BPNN model outperforms all these classifying techniques in every performance metric, like accuracy, precision, recall, and detection rate. Overall, from the performance analysis, the best classification accuracy is achieved for Web attack detection with 97.90%, and the best detection rate is achieved for Brute force attack detection with 97.89%.

The Research of Network Intrusion Detection Technology Based on Genetic Algorithm and BP Neural Network

2014 ◽  
Vol 599-601 ◽  
pp. 726-730 ◽  
Author(s):  
Gang Ke ◽  
Ying Han Hong
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Bp Neural Network ◽  
Detection System ◽  
Training Sample ◽  
Detection Algorithm ◽  
Network Intrusion Detection ◽  
Detection Accuracy ◽  
Network Intrusion

The traditional BP neural network algorithm is applied to intrusion detection system, detection speed slow and low detection accuracy. In order to solve the above problems, this paper proposes a network intrusion detection algorithm using genetic algorithms to optimize neural network weights. which find the most suitable weights of BP neural network by the genetic algorithm, and uses the optimized BP neural network to learn and detect the network intrusion detection data. Matlab simulation results show that the training sample time of the algorithm is shorter, has good intrusion recognition and detection effect, compared with the traditional network intrusion detection algorithm.

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