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

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.

Fuzzy Rule-Based Layered Classifier and Entropy-Based Feature Selection for Intrusion Detection System

2021 ◽  
pp. 289-309
Author(s):  
Devaraju Sellappan ◽  
Ramakrishnan Srinivasan
Keyword(s):  
Feature Selection ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Fuzzy Rule ◽  
Intrusion Detection Systems ◽  
Rule Based ◽  
Detection Systems ◽  
Positive Rate ◽  
Rule Based Classifier

Intrusion detection systems must detect the vulnerability consistently in a network and also perform efficiently with the huge amount of traffic. Intrusion detection systems must be capable of detecting emerging and proactive threats in the networks. Various classifiers are used to classify the threats as normal or intrusive by supervising the system activity. In this chapter, layered fuzzy rule-based classifier is proposed to detect the various intrusions, and fuzzy entropy-based feature selection is proposed to identify the relevant features. Layered fuzzy rule-based classifier is proposed to improve the performance of the intrusion detection system. KDD dataset contains various attacks; these attacks are grouped into four classes, namely Denial-of-Service (DoS), Probe, Remote-to-Local (R2L), and User-to-Root (U2R). Real-time dataset is also considered in this research. Experimental result shows that the proposed method provides good detection rate, minimizes the false positive rate, and less computational time.

scholarly journals Genetic algorithm for intrusion detection system in computer network

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>

scholarly journals Network intrusion detection system using immune-genetic algorithm (IGA)

2020 ◽  
Vol 17 (2) ◽  
pp. 1059
Author(s):  
Hamizan Suhaimi ◽  
Saiful Izwan Suliman ◽  
Ismail Musirin ◽  
Afdallyna Harun ◽  
Roslina Mohamad ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Computer Network ◽  
Detection System ◽  
Personal Information ◽  
Genetic Operators ◽  
Network Systems ◽  
Immune Genetic Algorithm ◽  
Network Intrusion

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.

scholarly journals Network intrusion detection system by using genetic algorithm

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.

DECISION MAKING FOR NETWORK HEALTH ASSESSMENT IN AN INTELLIGENT INTRUSION DETECTION SYSTEM ARCHITECTURE

2004 ◽  
Vol 03 (02) ◽  
pp. 281-306 ◽  
Author(s):  
AMBAREEN SIRAJ ◽  
RAYFORD B. VAUGHN ◽  
SUSAN M. BRIDGES
Keyword(s):  
Intrusion Detection ◽  
Computer Security ◽  
Intrusion Detection System ◽  
Detection System ◽  
Health Assessment ◽  
Fuzzy Rule ◽  
Fuzzy Cognitive Maps ◽  
Causal Knowledge ◽  
Knowledge Reasoning ◽  
Decision Engine

This paper describes the use of artificial intelligence techniques in the creation of a network-based decision engine for decision support in an Intelligent Intrusion Detection System (IIDS). In order to assess overall network health, the decision engine fuses outputs from different intrusion detection sensors serving as "experts" and then analyzes the integrated information to present an overall security view of the system for the security administrator. This paper reports on the workings of a decision engine that has been successfully embedded into the IIDS architecture being built at the Center for Computer Security Research, Mississippi State University. The decision engine uses Fuzzy Cognitive Maps (FCM)s and fuzzy rule-bases for causal knowledge acquisition and to support the causal knowledge reasoning process.

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