Performance evaluation of different intrusion detection system: An empirical approach

Beigh, Bilal Maqbool; Peer, M. A.

doi:10.1109/iccci.2014.6921740

Cited by 12 publications

(3 citation statements)

References 6 publications

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“…(7). False Negative Rate (FNR): FNR defined as the ratio of malicious behavior that have been designated as non-intrusive [6], as shown in 19 [48].…”

Section: Evaluated Metricsmentioning

confidence: 99%

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A Comparative Study of AI-Based Intrusion Detection Techniques in Critical Infrastructures

Otoum

Kantarcı

Mouftah

2021

ACM Trans. Internet Technol.

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Volunteer computing uses Internet-connected devices (laptops, PCs, smart devices, etc.), in which their owners volunteer them as storage and computing power resources, has become an essential mechanism for resource management in numerous applications. The growth of the volume and variety of data traffic on the Internet leads to concerns on the robustness of cyberphysical systems especially for critical infrastructures. Therefore, the implementation of an efficient Intrusion Detection System for gathering such sensory data has gained vital importance. In this article, we present a comparative study of Artificial Intelligence (AI)-driven intrusion detection systems for wirelessly connected sensors that track crucial applications. Specifically, we present an in-depth analysis of the use of machine learning, deep learning and reinforcement learning solutions to recognise intrusive behavior in the collected traffic. We evaluate the proposed mechanisms by using KDD’99 as real attack dataset in our simulations. Results present the performance metrics for three different IDSs, namely the Adaptively Supervised and Clustered Hybrid IDS (ASCH-IDS), Restricted Boltzmann Machine-based Clustered IDS (RBC-IDS), and Q-learning based IDS (Q-IDS), to detect malicious behaviors. We also present the performance of different reinforcement learning techniques such as State-Action-Reward-State-Action Learning (SARSA) and the Temporal Difference learning (TD). Through simulations, we show that Q-IDS performs with detection rate while SARSA-IDS and TD-IDS perform at the order of .

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“…(7). False Negative Rate (FNR): FNR defined as the ratio of malicious behavior that have been designated as non-intrusive [6], as shown in 19 [48].…”

Section: Evaluated Metricsmentioning

confidence: 99%

“…It signifies the (𝑇 𝑃) as displayed in Eq. ( 18) [48]. 𝐷𝑅 for ASCH-IDS, RBC-IDS, TD-IDS, SARSA-IDS and QL-IDS are shown in Fig.…”

Section: Evaluated Metricsmentioning

confidence: 99%

A Comparative Study of AI-Based Intrusion Detection Techniques in Critical Infrastructures

Otoum

Kantarcı

Mouftah

2021

ACM Trans. Internet Technol.

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“…Penetration detection systems have different types based on what part of the information they use or what method they use to detect. [2]…”

Section: Introductionmentioning

confidence: 99%

Types and Methods of Detecting the Penetration of MaliciousCargoes

M. J. Alfouadi,

Marwah Nafea Saeea,

Ali Fahem Neamah

2023

WJCMS

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Intrusion detection systems are management programs that detect possible attacks on networks andcomputers, and usually do so by identifying information in the header of packages. But the cargo of packagescontaining the main information can help detect abnormal traffic. This article examines the types of malicious cargoand the different types of penetration detection systems and the methods offered to detect based on cargo. At theend of this article, we will also introduce the Metasploit Framework, which is a framework used by testers and evenattackers and contains a variety of ready-made cargoes for different systems

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Analyzing threat flow over network using ensemble-based dense network model

Harita,

Mohammed

2024

Soft Comput

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Performance evaluation of different intrusion detection system: An empirical approach

Cited by 12 publications

References 6 publications

A Comparative Study of AI-Based Intrusion Detection Techniques in Critical Infrastructures

A Comparative Study of AI-Based Intrusion Detection Techniques in Critical Infrastructures

Types and Methods of Detecting the Penetration of MaliciousCargoes

Analyzing threat flow over network using ensemble-based dense network model

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