A Signature-Based Intrusion Detection System for Web Applications based on Genetic Algorithm

Bronte, Robert; Shahriar, Hossain; Haddad, Hisham M.

doi:10.1145/2947626.2951964

Cited by 11 publications

(1 citation statement)

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“…and to a human activity [55] addressed to the illegitimate exploitation of network resources. [57]. In [58], the authors proposed to face the problem related to the significant overheads in terms of memory usage and execution time related to the signature/pattern matching operation by parallelizing the process on a multi-core CPU.…”

Section: Intrusion Detection Systemsmentioning

confidence: 99%

A Local Feature Engineering Strategy to Improve Network Anomaly Detection

et al. 2020

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The dramatic increase in devices and services that has characterized modern societies in recent decades, boosted by the exponential growth of ever faster network connections and the predominant use of wireless connection technologies, has materialized a very crucial challenge in terms of security. The anomaly-based intrusion detection systems, which for a long time have represented some of the most efficient solutions to detect intrusion attempts on a network, have to face this new and more complicated scenario. Well-known problems, such as the difficulty of distinguishing legitimate activities from illegitimate ones due to their similar characteristics and their high degree of heterogeneity, today have become even more complex, considering the increase in the network activity. After providing an extensive overview of the scenario under consideration, this work proposes a Local Feature Engineering (LFE) strategy aimed to face such problems through the adoption of a data preprocessing strategy that reduces the number of possible network event patterns, increasing at the same time their characterization. Unlike the canonical feature engineering approaches, which take into account the entire dataset, it operates locally in the feature space of each single event. The experiments conducted on real-world data showed that this strategy, which is based on the introduction of new features and the discretization of their values, improves the performance of the canonical state-of-the-art solutions.

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Section: Intrusion Detection Systemsmentioning

confidence: 99%