Turkish Journal of Physiotherapy and Rehabilitation; 32(3) ISSN 2651-4451 | e-ISSN 2651-446X www.turkjphysiotherrehabil.org 430 ADAPTIVE NEURO FUZZY INFERENCE SYSTEM FOR ATTACK PREDICTION IN THE NETWORKS Durairaj. M 1 , D. Radhika 2 1 Assistant Professor, Department of Computer Science and Engineering, Bharathidasan University, Tiruchirappalli – 620 023. Email: [email protected]2 Research Scholar, Department of Computer Science and Engineering, Bharathidasan University, Tiruchirappalli – 620 023. Email: [email protected]. ABSTRACT With the rise of the Internet, the number of attacks has skyrocketed, and Intrusion Detection Systems (IDS) have emerged as a critical component of information security. The aim of an intrusion detection system (IDS) is to assist computer systems in dealing with attacks. This anomaly detection system builds a database of regular behavior and deviations from it, which it uses to activate when intrusions occur. IDS is divided into two types depending on the data source: host-based IDS and network-based IDS. Individual packets flowing through the network are analyzed in network-based IDS, while activities on a single device or server are analyzed in host-based IDS. IDS' feature selection aids in the reduction of classification time.In this paper, a new framework is proposed with Adaptive Neuro Fuzzy Inference System (ANFIS) for an IDS, to find the risk severity of the attacks. The proposed framework is composed Pre-Processing, Classification and Risk Severity Prediction. In this research work, the proposed ANFIS network is designed to predict the risk severity of the attacks in the IDS. KEYWORDS: Intrusion Detection System, Adaptive Neuro Fuzzy Inference System (ANFIS), Classification, Feature Selection, Risk Severity Prediction I. INTRODUCTION The Internet has recently become an integral part of everyday life. Present internet-based information management systems are vulnerable to a variety of attacks, resulting in a variety of damages and substantial losses. As a result, the value of information protection is rapidly increasing. The most fundamental aim of information security is to create protective information systems that are protected against unauthorized access, usage, disclosure, disturbance, alteration, or destruction. Furthermore, information protection reduces the risks associated with the three primary security objectives of confidentiality, integrity, and availability. Various systems have previously been developed to detect and prevent Internet-based attacks. Intrusion detection systems (IDS) are the most important systems among them because they effectively resist external attacks. Furthermore, IDSs serve as a line of protection against attacks on computer systems over the Internet. IDS may be used to detect various forms of attacks on network communications and computer system use in situations where a conventional firewall would fail. Intrusion detection is built on the premise that intruders behave differently than authorized users [1]. Based on their detection methods, IDSs are generally divided into two categories: anomaly detection systems and misuse detection systems [2][3]. Anomaly intrusion detection decides whether deviations from standard use habits are intrusions. Misuse detection systems, on the other hand, efficiently detect permission breaches. Intelligent agents and classification methods may be used to build intrusion detection systems. The majority of IDSs have two phases: pre-processing and intrusion detection. The intrusions detected by IDSs can be effectively avoided by implementing an intrusion prevention scheme. 1.1 Intelligent Intrusion Detection System Intelligent IDSs are intelligent computer programs that observe the environment and function flexibly to achieve higher detection accuracy [4][5]. They can be found in either a host or a network. These programs compute the behavior that should be taken in the environment by understanding the environment and firing inference rules [6].
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Turkish Journal of Physiotherapy and Rehabilitation; 32(3)
ISSN 2651-4451 | e-ISSN 2651-446X
www.turkjphysiotherrehabil.org 430
ADAPTIVE NEURO FUZZY INFERENCE SYSTEM FOR ATTACK
PREDICTION IN THE NETWORKS
Durairaj. M1, D. Radhika2 1Assistant Professor, Department of Computer Science and Engineering, Bharathidasan
University, Tiruchirappalli – 620 023. Email: [email protected] 2Research Scholar, Department of Computer Science and Engineering, Bharathidasan
Turkish Journal of Physiotherapy and Rehabilitation; 32(3)
ISSN 2651-4451 | e-ISSN 2651-446X
www.turkjphysiotherrehabil.org 445
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