An Intelligent Agent Based Intrusion Detection System Using Fuzzy Rough Set Based Outlier Detection

Jaisankar, N.; Ganapathy, Sannasi; Yogesh, P.; Kannan, A.; Anand, K.

doi:10.1007/978-3-642-25507-6_13

Cited by 18 publications

(7 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This step requires knowledge of the system and its performance, as well as the purpose of modeling. For black box modeling, the input signals of the system are important sources of information, so in this type of system, accuracy is very important at this stage (Sabzalian et al 2019 ; Failed 2012 ). The excitation signal must be such that it excites all system modes and does not damage the system.…”

Section: The Principles Of System Identificationmentioning

confidence: 99%

“…In Sabzalian, et al 2019 , the application of T2F-NNs in control problem is investigated and an adaptive controller is designed and its robustness against uncertainty is shown. In (Failed 2012 ), the application of fuzzy rough systems in intrude detection problem is studied and it is shown that by the use of fuzzy logic systems (FLSs) the accuracy is improved and the rate of false alarm is decreased. In (Kanimozhi 2019 ), a fuzzy prediction systems is designed and it is used for cancer prediction.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A review on type-2 fuzzy neural networks for system identification

2021

View full text Add to dashboard Cite

In many engineering problems, the systems dynamics are uncertain, and then, the accurate dynamic modeling is required. Type-2 fuzzy neural networks (T2F-NNs) are extensively used in system identification problems, because of their strong estimation capability. In this paper, the application of T2F-NNs is reviewed and classified. First, an introduction to the principles of system identification, including how to extract data from a system, persistency of excitation, preprocessing of information and data, removal of outlier data, and sorting of data to learn the T2F-NNs, is presented. Then, various learning methods for structure and parameters of the T2F-NNs are reviewed and analyzed. A number of different T2F-NNs that have been used to system identification are reviewed, and their disadvantages and advantages are described. Also, their efficiency in different applications is reviewed. Finally, we will look at the horizon ahead in this issue and analyze its challenges.

show abstract

Section: The Principles Of System Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A review on type-2 fuzzy neural networks for system identification

2021

View full text Add to dashboard Cite

show abstract

“…A device utilizing the system has been applied to various surely understood intrusion detection informational collections to distinguish how every segment adds to the general objectives of connection. Jaisankar, N et al, (2012) [19] proposed another wise specialist based IDS utilizing Fuzzy Rough Set based exception detection and Fuzzy Rough set based SVM. In that proposed model they presented two distinctive canny operators to be specific element determination specialist to choose the necessary list of capabilities utilizing fuzzy unpleasant sets and basic leadership operator director for settling on an official conclusion.…”

Section: Related Workmentioning

confidence: 99%

Novel Trust based ranking for Intrusion Detection and Security Using Deep Learning

Baby¹

2020

IJETER

View full text Add to dashboard Cite

Network intrusions are turning out to be increasingly more complex to distinguish. To moderate this subject, intrusion detection systems (IDSs) have been broadly deploying in distinguishing an assortment assaults. A lot of consideration has been given to profound learning during recent days, and latest profound knowledge procedures be developing by superior functionality. Numerous PC with system application effectively use such profound learn calculations and report upgraded execution throughout them. In this article, we plan and assess an IDS utilizing profound learning and trust the executive's component that enables gadgets to manage disrepute data about their neighbors. The proposed IDS method at first plays out of a positioning procedure and specifically groups the hub utilizing profound learning system. Results and correlation on execution investigation demonstrates the predominance of the proposed IDS.

show abstract

“…Intelligent IDSs are the ones considered to be intelligent computer programs situated in either a host or a network which analyzes the environment and acts flexibly to achieve higher detection accuracy [4,5]. These programs compute the actions to be performed on the environment both by learning the environment and by firing rules of inference [6].…”

Section: Intelligent Intrusion Detection Systemsmentioning

confidence: 99%

“…Then the distance between any two classes of patterns are computed from the R classes using the Minkowski distance. According this method, the distance between two points P ¼ x 1 ; x 2 ; x 3 ; …; x n ð Þand Q ¼ y 1 ; y 2 ; y 3 ; …; y n ð Þ ∈ R n ð4Þ is given by the formula given in Equation 5.…”

Section: Enhanced Multiclass Support Vector Machinementioning

confidence: 99%

Intelligent feature selection and classification techniques for intrusion detection in networks: a survey

Ganapathy

Kanagasabai

Muthurajkumar

et al. 2013

J Wireless Com Network

169

View full text Add to dashboard Cite

Rapid growth in the Internet usage and diverse military applications have led researchers to think of intelligent systems that can assist the users and applications in getting the services by delivering required quality of service in networks. Some kinds of intelligent techniques are appropriate for providing security in communication pertaining to distributed environments such as mobile computing, e-commerce, telecommunication, and network management. In this paper, a survey on intelligent techniques for feature selection and classification for intrusion detection in networks based on intelligent software agents, neural networks, genetic algorithms, neuro-genetic algorithms, fuzzy techniques, rough sets, and particle swarm intelligence has been proposed. These techniques have been useful for effectively identifying and preventing network intrusions in order to provide security to the Internet and to enhance the quality of service. In addition to the survey on existing intelligent techniques for intrusion detection systems, two new algorithms namely intelligent rule-based attribute selection algorithm for effective feature selection and intelligent rule-based enhanced multiclass support vector machine have been proposed in this paper.Keywords: Survey; Intrusion detection system; Neural networks; Fuzzy systems; Swarm intelligence; Particle swarm intelligence Review Intrusion detection systemsRecently, Internet has become a part and parcel of daily life. The current internet-based information processing systems are prone to different kinds of threats which lead to various types of damages resulting in significant losses. Therefore, the importance of information security is evolving quickly. The most basic goal of information security is to develop defensive information systems which are secure from unauthorized access, use, disclosure, disruption, modification, or destruction. Moreover, information security minimizes the risks related to the three main security goals namely confidentiality, integrity, and availability.Various systems have been designed in the past to identify and block the Internet-based attacks. The most important systems among them are intrusion detection systems (IDS) since they resist external attacks effectively. Moreover, IDSs provide a wall of defense which overcomes the attack of computer systems on the Internet. IDS could be used to detect different types of attacks on network communications and computer system usage where the traditional firewall cannot perform well. Intrusion detection is based on an assumption that the behavior of intruders differ from a legal user [1]. Generally, IDSs are broadly classified into two categories namely anomaly and misuse detection systems based on their detection approaches [2,3]. Anomaly intrusion detection determines whether deviation from the established normal usage patterns can be flagged as intrusions. On the other hand, misuse detection systems detect the violations of permissions effectively. Intrusion detection systems can be built by u...

show abstract

An Intelligent Agent Based Intrusion Detection System Using Fuzzy Rough Set Based Outlier Detection

Cited by 18 publications

References 10 publications

A review on type-2 fuzzy neural networks for system identification

A review on type-2 fuzzy neural networks for system identification

Novel Trust based ranking for Intrusion Detection and Security Using Deep Learning

Intelligent feature selection and classification techniques for intrusion detection in networks: a survey

Contact Info

Product

Resources

About