Fuzzy approach for intrusion detection based on user’s commands

Kudłacik, P.; Porwik, Piotr; Wesołowski, T

doi:10.1007/s00500-015-1669-6

Cited by 27 publications

(9 citation statements)

References 43 publications

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“…The first group of approaches consists of the techniques based on attack graph analysis [17][18][19][20][21][22][23][24][25][26][27][28], hidden Markov model [29][30][31][32][33][34], and fuzzy logic [35][36][37].…”

Section: The Comparative Analysis Of the Approaches To The Attacker'smentioning

confidence: 99%

“…In [37], the authors solve the problem of constructing profiles of the normal user behaviour based on the analysis of the log events such as keyboard keys' sequences, characteristic data sequences retrieved from pointing device, chosen options, requested network resources, and so on. They apply fuzzy logic to the qualitative attributes of these events to describe a set of fuzzy profiles and identify masqueraded attacks.…”

Section: Attacker Behaviour Pattern Discovery Using Fuzzy Inferencementioning

confidence: 99%

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Attacker Behaviour Forecasting Using Methods of Intelligent Data Analysis: A Comparative Review and Prospects

2020

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Early detection of the security incidents and correct forecasting of the attack development is the basis for the efficient and timely response to cyber threats. The development of the attack depends on future steps available to the attackers, their goals, and their motivation—that is, the attacker “profile” that defines the malefactor behaviour in the system. Usually, the “attacker profile” is a set of attacker’s attributes—both inner such as motives and skills, and external such as existing financial support and tools used. The definition of the attacker’s profile allows determining the type of the malefactor and the complexity of the countermeasures, and may significantly simplify the attacker attribution process when investigating security incidents. The goal of the paper is to analyze existing techniques of the attacker’s behaviour, the attacker’ profile specifications, and their application for the forecasting of the attack future steps. The implemented analysis allowed outlining the main advantages and limitations of the approaches to attack forecasting and attacker’s profile constructing, existing challenges, and prospects in the area. The approach for attack forecasting implementation is suggested that specifies further research steps and is the basis for the development of an attacker behaviour forecasting technique.

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Section: The Comparative Analysis Of the Approaches To The Attacker'smentioning

confidence: 99%

Section: Attacker Behaviour Pattern Discovery Using Fuzzy Inferencementioning

confidence: 99%

Attacker Behaviour Forecasting Using Methods of Intelligent Data Analysis: A Comparative Review and Prospects

2020

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“…Shanmugavadivu et al [9] introduced a network intrusion detection system using fuzzy logic. Kudłacik et al [10] presented an intrusion detection method based on a fuzzy approach. Azad et al [11] introduced an intrusion detection system which is based on the fuzzy min, fuzzy max neural network and the particle swarm optimization.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy zero day exploits detector system

Shaout

Smyth²

2017

IJACR

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“…Kudłacik et al [30] have presented a method for detecting intrusions based on a fuzzy approach. By analyzing the user's activities in a short period of time, they have generated user's fuzzy profiles.…”

Section: Related Workmentioning

confidence: 99%

Predicting the behavior of attackers and the consequences of attacks against cyber‐physical systems

Orojloo

Azgomi

2016

Security Comm Networks

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One of the main challenges of the security of cyber-physical systems (CPSs) is the lack of an efficient approach to evaluate the impacts of attacks on physical processes and their probabilities of occurrence. This paper proposes a method for evaluating the security of CPSs. By using the proposed method, one can predict the attacker's preferences in attacking CPSs and study the dynamic behavior of systems under security attacks. To deal with uncertainties in attacker's decision making to conduct attacks, the attack tree structure is used and parameterized with suitable fuzzy data. In the next step, the fuzzy technique for order of preference by similarity to ideal solution method is used to evaluate the model and predict the behavior of attackers. Besides, the dynamic behavior of CPSs under attacks is investigated by using the system's process model. The output of the model is a relative estimation of the security level of system based on suitable security metrics, such as the probability of attack scenarios, the time that the process is able to operate after conducting attack before shutting down (time-to-shutdown), and security risks. We illustrate the effectiveness of the method by comparing it with another attack tree-based method. Furthermore, we present two illustrative examples and estimate the defined quantitative security measures.

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Fuzzy approach for intrusion detection based on user’s commands

Cited by 27 publications

References 43 publications

Attacker Behaviour Forecasting Using Methods of Intelligent Data Analysis: A Comparative Review and Prospects

Attacker Behaviour Forecasting Using Methods of Intelligent Data Analysis: A Comparative Review and Prospects

Fuzzy zero day exploits detector system

Predicting the behavior of attackers and the consequences of attacks against cyber‐physical systems

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