scholarly journals Fusion of Heterogeneous Intrusion Detection Systems for Network Attack Detection

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Jayakumar Kaliappan ◽  
Revathi Thiagarajan ◽  
Karpagam Sundararajan
Keyword(s):  
Intrusion Detection ◽  
Detection Rate ◽  
Detection System ◽  
Attack Detection ◽  
Majority Voting ◽  
Final Decision ◽  
Network Attack ◽  
Voting Rule ◽  
Specific Category ◽  
Local Decision

An intrusion detection system (IDS) helps to identify different types of attacks in general, and the detection rate will be higher for some specific category of attacks. This paper is designed on the idea that each IDS is efficient in detecting a specific type of attack. In proposed Multiple IDS Unit (MIU), there are five IDS units, and each IDS follows a unique algorithm to detect attacks. The feature selection is done with the help of genetic algorithm. The selected features of the input traffic are passed on to the MIU for processing. The decision from each IDS is termed as local decision. The fusion unit inside the MIU processes all the local decisions with the help of majority voting rule and makes the final decision. The proposed system shows a very good improvement in detection rate and reduces the false alarm rate.

scholarly journals Intrusion Detection System using Hybrid SVM-RF and SVM-DT in Wireless Sensor Networks

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1926-1931
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Hybrid Algorithm ◽  
Detection Rate ◽  
Detection System ◽  
Denial Of Service ◽  
Attack Detection ◽  
Support Vector ◽  
Second Stage ◽  
Network Intrusion

Intrusion detection system (IDS) is one of the essential security mechanisms against attacks in WSN. Network intrusion detection system (NIDS) generally uses the classification techniques in order to obtain the best possible accuracy and attack detection rate. In this paper, Intrusion Detection System is designed which uses two-stage hybrid classification method. In the first stage it uses Support Vector Machine (SVM) as anomaly detection, and in the second stage it uses Random Forest (RF)/Decision Tree (DT) as misuse. The abnormal activities are detected in the first stage. These abnormal activities are further analyzed and the known attacks are identified in the second stage and are classified as Denial of Service (DoS) attack, Probe attack, Remote to Local (R2L) attack and User to Root (U2R) attack. Simulation results reveal that the proposed hybrid algorithm obtains better accuracy and detection rate than the single classifier namely, SVM, RF and DT algorithm. The experimental results also shows that hybrid algorithm can detect anomaly activity in a reliable way. Proposed technique uses the standard NSL KDD dataset to evaluate/calculate the performance of the proposed approach. Here the results show that the proposed Hybrid SVM-RF/DT IDS technique performs better in terms of detection rate, accuracy and recall than the existing SVM, RF and DT approaches.

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%.

An effective intrusion detection system using homogeneous ensemble techniques

2022 ◽  
Vol 16 (1) ◽  
pp. 0-0
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection Rate ◽  
Detection System ◽  
Majority Voting ◽  
The Other ◽  
Recursive Feature Elimination ◽  
Ensemble Techniques ◽  
Proposed Model ◽  
Homogeneous Ensemble

In this work, homogeneous ensemble techniques, namely bagging and boosting were employed for intrusion detection to determine the intrusive activities in network by monitoring the network traffic. Simultaneously, model diversity was enhanced as numerous algorithms were taken into account, thereby leading to an increase in the detection rate Several classifiers, i.e., SVM, KNN, RF, ETC and MLP) were used in case of bagging approach. Likewise, tree-based classifiers have been employed for boosting. The proposed model was tested on NSL-KDD dataset that was initially subjected to preprocessing. Accordingly, ten most significant features were identified using decision tree and recursive feature elimination method. Furthermore, the dataset was divided into five subsets, each one them being subjected to training, and the final results were obtained based on majority voting. Experimental results proved that the model was effective for detecting intrusive activities. Bagged ETC and boosted RF outperformed all the other classifiers with an accuracy of 99.123% and 99.309%, respectively.

Structural Design of Intrusion Detection System

2014 ◽  
Vol 530-531 ◽  
pp. 705-708
Author(s):  
Yao Meng
Keyword(s):  
Intrusion Detection ◽  
Structural Design ◽  
Intrusion Detection System ◽  
Detection System ◽  
Algorithm Design ◽  
Detection Algorithm ◽  
Attack Detection ◽  
Detection Algorithms ◽  
Defense Systems ◽  
Ddos Attack Detection

This paper first engine starting defense from Intrusion Detection, Intrusion detection engine analyzes the hardware platform, the overall structure of the technology and the design of the overall structure of the plug, which on the whole structure from intrusion defense systems were designed; then described in detail improved DDOS attack detection algorithm design thesis, and the design of anomaly detection algorithms.

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 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.

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.

Network Attack Detection With SNMP-MIB Using Deep Neural Network

2020 ◽  
pp. 66-76
Author(s):  
Mouhammd Sharari Alkasassbeh ◽  
Mohannad Zead Khairallah
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Information Technologies ◽  
Deep Neural Network ◽  
Detection System ◽  
Attack Detection ◽  
Machine Learning Techniques ◽  
Network Attack ◽  
Security Breaches ◽  
Security Issues

Over the past decades, the Internet and information technologies have elevated security issues due to the huge use of networks. Because of this advance information and communication and sharing information, the threats of cybersecurity have been increasing daily. Intrusion Detection System (IDS) is considered one of the most critical security components which detects network security breaches in organizations. However, a lot of challenges raise while implementing dynamics and effective NIDS for unknown and unpredictable attacks. Consider the machine learning approach to developing an effective and flexible IDS. A deep neural network model is proposed to increase the effectiveness of intrusions detection system. This chapter presents an efficient mechanism for network attacks detection and attack classification using the Management Information Base (MIB) variables with machine learning techniques. During the evaluation test, the proposed model seems highly effective with deep neural network implementation with a precision of 99.6% accuracy rate.

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