Intrusion Detection System using Bayesian Network and Hidden Markov Model

Devarakonda, Nagaraju; Srinivasulu, P.; Kumari, V. Valli; Govardhan, A.

doi:10.1016/j.protcy.2012.05.081

Cited by 29 publications

(11 citation statements)

References 5 publications

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“…The work of Devarakonda et al in [27] focuses on detecting and preventing the multi-step attack at its onset (before it poses a severe risk). The proposed detection approach was adapted using a hybrid HMM.…”

Section: Hidden Markov Models (Hmms) Against Multi-stepmentioning

confidence: 99%

Fuzzy Automaton as a Detection Mechanism for the Multi-Step Attack

Almseidin¹,

Piller²,

Alkasassbeh³

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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The integration of a fuzzy system and automaton theory can form the concept of fuzzy automaton. This integration allows a discretely defined state-machine to act on continuous universes and handle uncertainty in applications like Intrusion Detection Systems (IDS). The typical IDS detection mechanisms are targeted to detect and prevent single-stage attacks. These types of attacks can be detected using either a common convincing threshold or by pre-defined rules. However, attack techniques have changed in recent years. Currently, the largest proportion of attacks performed, are multi-step attacks. The goal of this paper is to introduce a novel detection mechanism for multi-step attacks built upon Fuzzy Rule Interpolation (FRI) based fuzzy automaton. In that respect, the FRI method instruments the fuzzy automaton to be able to act on a not fully defined state transition rule-base, by offering interpolated conclusion even for situations which are not explicitly defined. In the suggested model, the intrusion definition state transition rule-base is defined using an open source fuzzy declarative language. On the multi-step attack benchmark dataset introduced in this paper, the proposed detection mechanism was able to achieve 97.836% detection rate. Furthermore, in the studied examples, the suggested method was able not only to detect but also early detect the multi-step attack in stages, where the planned attack is not fully elaborated and hence less harmful. According to these results, the IDS built upon the FRI based fuzzy automaton could be a useful device for detecting multi-step attacks, even in cases when the intrusion state transition rule-based is incomplete. The early detection of multi-step attacks also allows the administrator to take the necessary actions in time, to mitigate the potential threats.

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Section: Hidden Markov Models (Hmms) Against Multi-stepmentioning

confidence: 99%

Fuzzy Automaton as a Detection Mechanism for the Multi-Step Attack

Almseidin¹,

Piller²,

Alkasassbeh³

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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show abstract

“…The study showed the BNMA classifier to have a significantly better predictive capability compared to the BN and Naive Bayes classifier which was built based on heuristic method. Even classifiers trained with smaller dataset has better performances compared to the other two classifiers trained with a larger dataset.The use of the Hidden Markov Model (HMM) for IDS has been suggested by [21]. The KDD Cup 1999 dataset for IDS was trained and tested using HMM for the applicator.…”

Section: Ids Based On Bayesian Classifiermentioning

confidence: 99%

Review on Intrusion Detection System Based on The Goal of The Detection System

Khaleel

Ismail

Yunan³

et al. 2018

IJIE

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“…Choosing the positive method as in misuse detection needs categorizing of the network traffic. Devarakonda et al [24] proposed a study based on Bayesian network and the Hidden Markov Model with help of the KDDcup dataset as intrusion detection dataset. e model for IDS has been developed with different stages like learning the model with the training dataset and construction of Bayesian network, and this arrangement is used as the HMM stage diagram.…”

Section: Bayesian Network (Bn)mentioning

confidence: 99%

Role of Machine Learning and Data Mining in Internet Security: Standing State with Future Directions

Borjali

Wang

Ali

2018

Journal of Computer Networks and Communications

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As time progresses with vast development of information technology, a large number of industries are more dependent on network connections for sensitive business trading and security matters. Communications and networks are highly vulnerable to threats because of increase in hacking. Personnel, governments, and armed classified networks are more exposed to difficulties, so the need of the hour is to install safety measures for network to prevent illegal modification, damage, or leakage of serious information. Intrusion detection, an important entity towards network security, has the ability to observe network activity as well as detect intrusions/attacks. This study highlights the developing research about the application of machine learning and data mining in Internet security. We provide background, enthusiasm, discussion of challenges, and recommendations for the application of ML/DM in the field of intrusion detection.

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Intrusion Detection System using Bayesian Network and Hidden Markov Model

Cited by 29 publications

References 5 publications

Fuzzy Automaton as a Detection Mechanism for the Multi-Step Attack

Fuzzy Automaton as a Detection Mechanism for the Multi-Step Attack

Review on Intrusion Detection System Based on The Goal of The Detection System

Role of Machine Learning and Data Mining in Internet Security: Standing State with Future Directions

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