Immune-Genetic Algorithm(IGA) with Local Search For Intrusion Detection System in Computer Network

Network security is an important aspect in maintaining computer network systems and personal information from being illegally accessed by third parties. The major problem that frequently occurs in computer network systems is the failure in detecting possible network-attacks. Apart from that, the process of recognizing the type of attack that occurs is very crucial as it will determine the elimination process that should take place to counter the intrusion. This paper proposes the application of standard Genetic Algorithm (GA) that combines with immune algorithm process to enhance the computer system’s capability in recognizing possible intrusion occurrence in a computer system. Simulation was conducted numerous times to test the effectiveness of the proposed intrusion detection system by manipulating the parameter values for genetic operators utilized in GA. The effectiveness of the proposed method is shown in the gathered results and the analysis conducted further supports and proves that Immune Genetic Algorithm (IGA) has the capability to predict the occurrence of intrusion in computer network.

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Genetic algorithm for intrusion detection system in computer network

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v19.i3.pp1670-1676 ◽

2020 ◽

Vol 19 (3) ◽

pp. 1670

Author(s):

Hamizan Suhaimi ◽

Saiful Izwan Suliman ◽

Afdallyna Fathiyah Harun ◽

Roslina Mohamad ◽

Yuslinda Wati Mohamad Yusof ◽

...

Keyword(s):

Genetic Algorithm ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Computer Network ◽

Local Area Network ◽

Detection System ◽

Local Area ◽

Small Scale ◽

Area Network ◽

Network Intrusion

<span>Internet connection nowadays has become one of the essential requirements to execute our daily activities effectively. Among the major applications of wide Internet connections is local area network (LAN) which connects all internet-enabled devices in a small-scale area such as office building, computer lab etc. This connection will allow legit user to access the resources of the network anywhere as long as authorization is acquired. However, this might be seen as opportunities for some people to illegally access the network. Hence, the occurrence of network hacking and privacy breach. Therefore, it is very vital for a computer network administrator to install a very protective and effective method to detect any network intrusion and, secondly to protect the network from illegal access that can compromise the security of the resources in the network. These resources include sensitive and confidential information that could jeopardise someone’s life or sovereignty of a country if manipulated by wrong hands. In Network Intrusion Detection System (NIDS) framework, apart from detecting unauthorized access, it is equally important to recognize the type of intrusions in order for the necessary precautions and preventive measures to take place. This paper presents the application of Genetic Algorithm (GA) and its steps in performing intrusion detection process. Standard benchmark dataset known as KDD’99 cup was utilized with forty-one distinctive features representing the identity of network connections. Results presented demonstrate the effectiveness of the proposed method and warrant good research focus as it promises exciting discovery in solving similar-patent of problems. </span>

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Intrusion detection system using a new fuzzy rule-based classification system based on genetic algorithm

Intelligent Decision Technologies ◽

10.3233/idt-200036 ◽

2021 ◽

pp. 1-7

Author(s):

Zahra Asghari Varzaneh ◽

Marjan Kuchaki Rafsanjani

Keyword(s):

Genetic Algorithm ◽

Intrusion Detection ◽

Classification System ◽

Intrusion Detection System ◽

Computer Network ◽

Detection System ◽

Fuzzy Rule ◽

Classification Systems ◽

Classification Rate ◽

Rule Based

Intrusion can compromise the integrity, confidentiality, or availability of a computer system. Intrusion Detection System (IDS) is a type of security software designed to monitor network traffic and identify network intrusions. In this paper, A Fuzzy Rule – Based classification system is used to detect intrusion in a computer network. In order to improve the classification rate, a new method is proposed based on Genetic Algorithm (GA) for rule weights specification. The proposed method is tested on KDD99 dataset. Experimental results show the proposed method improves the performance of the fuzzy rule-based classification systems in terms of detection rate and false alarm rate.

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Network intrusion detection system by using genetic algorithm

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v16.i3.pp1593-1599 ◽

2019 ◽

Vol 16 (3) ◽

pp. 1593

Author(s):

Hamizan Suhaimi ◽

Saiful Izwan Suliman ◽

Ismail Musirin ◽

Afdallyna Fathiyah Harun ◽

Roslina Mohamad

Keyword(s):

Genetic Algorithm ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Computer Network ◽

Detection System ◽

Classification Rule ◽

Network Intrusion Detection ◽

Detection Systems ◽

The Past ◽

Network Intrusion

Developing a better intrusion detection systems (IDS) has attracted many researchers in the area of computer network for the past decades. In this paper, Genetic Algorithm (GA) is proposed as a tool that capable to identify harmful type of connections in a computer network. Different features of connection data such as duration and types of connection in network were analyzed to generate a set of classification rule. For this project, standard benchmark dataset known as KDD Cup 99 was investigated and utilized to study the effectiveness of the proposed method on this problem domain. The rules comprise of eight variables that were simulated during the training process to detect any malicious connection that can lead to a network intrusion. With good performance in detecting bad connections, this method can be applied in intrusion detection system to identify attack thus improving the security features of a computer network.

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Classification of intrusion detection system (IDS) based on computer network

2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE) ◽

10.1109/icitisee.2017.8285566 ◽

2017 ◽

Cited By ~ 2

Author(s):

David Ahmad Effendy ◽

Kusrini Kusrini ◽

Sudarmawan Sudarmawan

Keyword(s):

Intrusion Detection ◽

Intrusion Detection System ◽

Computer Network ◽

Detection System

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A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms

Symmetry ◽

10.3390/sym12061046 ◽

2020 ◽

Vol 12 (6) ◽

pp. 1046 ◽

Cited By ~ 3

Author(s):

Omar Almomani

Keyword(s):

Genetic Algorithm ◽

Feature Selection ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Selection Model ◽

Network Intrusion Detection ◽

Network Intrusion ◽

Proposed Model ◽

Positive Rate

The network intrusion detection system (NIDS) aims to identify virulent action in a network. It aims to do that through investigating the traffic network behavior. The approaches of data mining and machine learning (ML) are extensively used in the NIDS to discover anomalies. Regarding feature selection, it plays a significant role in improving the performance of NIDSs. That is because anomaly detection employs a great number of features that require much time. Therefore, the feature selection approach affects the time needed to investigate the traffic behavior and improve the accuracy level. The researcher of the present study aimed to propose a feature selection model for NIDSs. This model is based on the particle swarm optimization (PSO), grey wolf optimizer (GWO), firefly optimization (FFA) and genetic algorithm (GA). The proposed model aims at improving the performance of NIDSs. The proposed model deploys wrapper-based methods with the GA, PSO, GWO and FFA algorithms for selecting features using Anaconda Python Open Source, and deploys filtering-based methods for the mutual information (MI) of the GA, PSO, GWO and FFA algorithms that produced 13 sets of rules. The features derived from the proposed model are evaluated based on the support vector machine (SVM) and J48 ML classifiers and the UNSW-NB15 dataset. Based on the experiment, Rule 13 (R13) reduces the features into 30 features. Rule 12 (R12) reduces the features into 13 features. Rule 13 and Rule 12 offer the best results in terms of F-measure, accuracy and sensitivity. The genetic algorithm (GA) shows good results in terms of True Positive Rate (TPR) and False Negative Rate (FNR). As for Rules 11, 9 and 8, they show good results in terms of False Positive Rate (FPR), while PSO shows good results in terms of precision and True Negative Rate (TNR). It was found that the intrusion detection system with fewer features will increase accuracy. The proposed feature selection model for NIDS is rule-based pattern recognition to discover computer network attack which is in the scope of Symmetry journal.

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