A Survey of Intrusion Detection Systems Using Evolutionary Computation

Şen, Sevil

doi:10.1016/b978-0-12-801538-4.00004-5

Cited by 35 publications

(10 citation statements)

References 47 publications

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“…Evolutionary computation (EC) is one of the most promising approaches in intrusion detection. A recent survey [49] showed that evolutionary computation techniques allow to obtain more readable outputs by security experts, lightweight solutions using fewer features, and a set of solutions providing different trade-offs between conflict objectives. Furthermore, EC provides ease of representation, and it does not require assumptions about the solution space [60].…”

Section: The Modelmentioning

confidence: 99%

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GenTrust: A genetic trust management model for peer-to-peer systems

Tahta

Şen

Can

2015

Applied Soft Computing

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Section: The Modelmentioning

confidence: 99%

“…The evolutionary computation techniques are mainly applied to intrusion detection in the security domain [48] [49]. Most of these studies focus on developing effective and efficient detection methods.…”

Section: Related Workmentioning

confidence: 99%

GenTrust: A genetic trust management model for peer-to-peer systems

Tahta

Şen

Can

2015

Applied Soft Computing

Self Cite

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“…Similarly to natural evolution, the optimization process consists of choosing amongst a set of solutions -the population -the fittest ones to breed or to continue into a newer set of solutions, which constitutes a new generation. This process is repeated until a certain convergence criteria is reached, which can be set as a minimum performance required from the individuals or a maximum number of generations reached during execution [9] (Figure 1). 1) Fitness Function: The process of selecting the best individuals from a given generation is done by evaluating each one of them using a fitness function, which can be described as a way of measuring how good the solutions presented are at solving the given problem [10].…”

Section: A Genetic Algorithmsmentioning

confidence: 99%

Evolutionary Convolutional Neural Network: a case study

Pereira¹,

Kohler²,

Pacheco³

2021

Anais Do 15. Congresso Brasileiro De Inteligência Computacional

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Most of the state-of-the-art Convolutional Neural Network (CNN) architectures are manually crafted by experts, usually with background knowledge from extent working experience in this research field. Therefore, this manner of designing CNNs is highly limited and many approaches have been developed to try to make this procedure more automatic. This paper presents a case study in tackling the architecture search problem by using a Genetic Algorithm (GA) to optimize an existing CNN Architecture. The proposed methodology uses VGG-16 convolutional blocks as its building blocks and each individual from the GA corresponds to a possible model built from these blocks with varying filter sizes, keeping fixed the original network architecture connections. The selection of the fittest individuals are done according to their weighted F1-Score when training from scratch on the available data. To evaluate the best individual found from the proposed methodology, the performance is compared to a VGG-16 model trained from scratch on the same data.

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“…The traffic information is moved in bulk and then analyzed for any unusual behavior in the data. The team explores threats by using known attack signatures, as well as trends [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Effectiveness Evaluation of Different IDSs Using Integrated Fuzzy MCDM Model

et al. 2022

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Cyber-attacks are becoming progressively complicated; hence, the functional issues of intrusion-detection systems (IDSs) present ever-growing challenges. Failing to detect intrusions may jeopardize the trustworthiness of security services, such as privacy preservation, authenticity, and accessibility. To fight these risks, different organizations nowadays use a variety of approaches, techniques, and technologies to safeguard the systems’ credibility. Establishing policies and procedures, raising user awareness, implementing firewall and verification systems, controlling system access, and building computer-issue management groups are all examples of safeguarding methods. There is a lack of sufficient emphasis on the effectiveness of intrusion-detection systems. In enterprises, IDS is used to analyze the potentially dangerous activities taking place within the technological settings. The selection of efficient IDS is a challenging task for organizations. This research evaluates the impact of five popular IDSs for their efficiency and effectiveness in information security. The authors used the fuzzy analytical hierarchy process (AHP) and fuzzy technique for order performance by similarity to ideal solution (TOPSIS)-based integrated multi-criteria decision-making (MCDM) methodology to evaluate the efficacy of the popular IDSs. The findings of this research suggest that most of the IDSs appear to be highly potential tools. Even though Snort is extensively deployed, Suricata has a substantial advantage over Snort. Suricata uses multi-threading functionality in comparison to Snort to boost the processing performance.

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A Survey of Intrusion Detection Systems Using Evolutionary Computation

Cited by 35 publications

References 47 publications

GenTrust: A genetic trust management model for peer-to-peer systems

GenTrust: A genetic trust management model for peer-to-peer systems

Evolutionary Convolutional Neural Network: a case study

Effectiveness Evaluation of Different IDSs Using Integrated Fuzzy MCDM Model

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