A Bio-Inspired Hybrid Artificial Intelligence Framework for Cyber Security

Demertzis, Konstantinos; Iliadis, Lazaros

doi:10.1007/978-3-319-18275-9_7

Cited by 30 publications

(25 citation statements)

References 38 publications

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“…The paper on network anomaly detection based on neural network evolution written by Konstantinos Demertzis and Lazaros Iliadis [3], describes an intelligent system of machine learning, where part of the system works looking for known threats, and another part tries to detect probable threats according to abnormal activities that take place in the order. The detection system is simple, it generates a state being treated as usual, and all the signals outside the edge of that state are processed as an anomaly, so the detection algorithm learns continuously while the system is active in the network, is more and more need.…”

Section: Sql Injection Attack Identification Methodology (Model-biohamentioning

confidence: 99%

“…The methodology used in the article was the Spiking Artificial Neural Networks (SANN) [3], which uses an Evolving Connectionist System (eCOS) and Multi-Layer Feed Forward ANN approach to classify the exact type of intrusion or network abnormality with minimal computational potential. SANN is a set of modular systems based on node connections.…”

Section: Sql Injection Attack Identification Methodology (Model-biohamentioning

confidence: 99%

“…With globalization and increasing societal dependence on software systems, information and data of paramount importance to companies and individuals worldwide travel instantly via the internet, catching the attention of cybercriminals, where they seek to invade systems or intercept information for use for their benefit, or harmful to the organizations they attack, making the impacts of such attacks more and more high [3].…”

Section: Introductionmentioning

confidence: 99%

“…This article proposes the use of hybrid models based on neural networks and fuzzy systems to build systems specialized in the cybernetic invasion, based on fuzzy rules. The attack that will be the target of this In this case, the neural networks of the neural nets are similar to those proposed by Demertzis et al [3]. The system introduced in the article will be able to generate rules based on the results obtained through tests, using nebulous logic neurons.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fuzzy neural networks to create an expert system for detecting attacks by SQL Injection

Batista¹,

Betim²,

Silva³

et al. 2018

IJoFCS

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Its constant technological evolution characterizes the contemporary world, and every day the processes, once manual, become computerized. Data are stored in the cyberspace, and as a consequence, one must increase the concern with the security of this environment. Cyber-attacks are represented by a growing worldwide scale and are characterized as one of the significant challenges of the century. This article aims to propose a computational system based on intelligent hybrid models, which through fuzzy rules allows the construction of expert systems in cybernetic data attacks, focusing on the SQL Injection attack. The tests were performed with real bases of SQL Injection attacks on government computers, using fuzzy neural networks. According to the results obtained, the feasibility of constructing a system based on fuzzy rules, with the classification accuracy of cybernetic invasions within the margin of the standard deviation (compared to the state-of-the-art model in solving this type of problem) is real. The model helps countries prepare to protect their data networks and information systems, as well as create opportunities for expert systems to automate the identification of attacks in cyberspace.

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Section: Sql Injection Attack Identification Methodology (Model-biohamentioning

confidence: 99%

Section: Sql Injection Attack Identification Methodology (Model-biohamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy neural networks to create an expert system for detecting attacks by SQL Injection

Batista¹,

Betim²,

Silva³

et al. 2018

IJoFCS

View full text Add to dashboard Cite

show abstract

“…The CiantiMF system is a biologically inspired artificial intelligence computer security technique [8][9][10][11][12]. Unlike other existing approaches which are based on individual passive safety techniques, the CiantiMF is an integrated active safety system.…”

Section: Innovation Of the Proposed Methodsmentioning

confidence: 99%

Computational intelligence anti-malware framework for android OS

Demertzis¹,

Iliadis²

2017

Vietnam J Comput Sci

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It is a fact that more and more users are adopting the online digital payment systems via mobile devices for everyday use. This attracts powerful gangs of cybercriminals, which use sophisticated and highly intelligent types of malware to broaden their attacks. Malicious software is designed to run quietly and to remain unsolved for a long time. It manages to take full control of the device and to communicate (via the Tor network) with its Command & Control servers of fastflux botnets' networks to which it belongs. This is done to achieve the malicious objectives of the botmasters. This paper proposes the development of the computational intelligence anti-malware framework (CIantiMF) which is innovative, ultra-fast and has low requirements. It runs under the android operating system (OS) and its reasoning is based on advanced computational intelligence approaches. The selection of the android OS was based on its popularity and on the number of critical applications available for it. The CIantiMF uses two advanced technology extensions for the ART java virtual machine which is the default in the recent versions of android. The first is the smart anti-malware extension, which can recognize whether the java classes of an android application are benign or malicious using an optimized multi-layer perceptron. The optimization is done by the employment of the biogeography-based optimizer algorithm. The second is the Tor online traffic identification extension, which is capable of achieving malware localization, Tor traffic identification B Konstantinos Demertzis kdemertz@fmenr.duth.gr Lazaros Iliadis

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Impact of Artificial Intelligence and Machine Learning on Cybersecurity

2023

From 5G to 6G

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A Bio-Inspired Hybrid Artificial Intelligence Framework for Cyber Security

Cited by 30 publications

References 38 publications

Fuzzy neural networks to create an expert system for detecting attacks by SQL Injection

Fuzzy neural networks to create an expert system for detecting attacks by SQL Injection

Computational intelligence anti-malware framework for android OS

Impact of Artificial Intelligence and Machine Learning on Cybersecurity

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