On detecting distributed denial of service attacks using fuzzy inference system

Almseidin, Mohammad; Al‐Sawwa, Jamil; Alkasassbeh, Mouhammd; Alweshah, Mohammed

doi:10.1007/s10586-022-03657-5

Cited by 6 publications

(2 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Almseidin et al [20] developed an anomaly-based distributed denial-of-service (DDoS) attack detection system using fuzzy inference. Their approach aims to avoid binary decisions and provide human-interpretable outputs.…”

Section: Anomaly-basedmentioning

confidence: 99%

Botnet Detection Techniques: A Comparative Study

Alauthman

2023

Preprint

View full text Add to dashboard Cite

Botnets pose a grave cybersecurity threat, enabling widescale malicious activities through networks of compromised devices. Detecting botnets is challenging given their frequent use of evasion techniques like encryption. Traditional signature-based methods fail against modern botnets capable of zero-day attacks. This paper surveys recent advances applying machine learning for botnet detection based on analysis of network traffic payloads, flows, DNS data, and hybrid feature fusion. Core machine learning models include support vector machines, neural networks, random forests, and deep learning architectures, which extract patterns to separate benign and botnet behaviors automatically. Results demonstrate machine learning's capabilities in identifying heterogeneous botnets using artefacts in network streams. However, challenges remain around limited labeled data, real-time streaming, adversarial evasion, and model interpretability. Promising directions involve semi-supervised learning, adversarial training, scalable analytics, and explainable AI to address these gaps. Beyond the technical aspects, responsible development and deployment of botnet detection systems raise ethical considerations around privacy, transparency, and accountability. With diligent cross-disciplinary collaboration, machine learning promises enhanced, generalizable, and trustworthy techniques to combat the serious threat posed by continuously evolving botnets across the digital ecosystem.

show abstract

Section: Anomaly-basedmentioning

confidence: 99%

Botnet Detection Techniques: A Comparative Study

Alauthman

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In [33], the authors introduced a fuzzy inference-based anomaly-based intrusion detection system for detecting DDoS attacks. The utilization of a fuzzy inference system aimed to move beyond binary decisions, addressing issues associated with the limitations of IDS alert system awareness.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Fuzzy Rule Interpolation Techniques Across Various Scenarios Using a Set of Benchmarks

Alzubi,

Almseidin,

Alkasassbeh

et al. 2024

IEEE Access

Self Cite

View full text Add to dashboard Cite

This paper presents a set of benchmarks to evaluate the performance of Fuzzy Rule Interpolation (FRI) methods under various challenging conditions. FRI methods are widely used for handling sparse fuzzy rule bases and reducing decision complexity. Despite lacking overlap with the antecedents of any rule in the rule bases, FRI can still produce a conclusion. To unify the requirements of FRI methods, several conditions have been proposed. Among these, the convex and normal fuzzy set condition and the Piece-wise linearity condition are the most common. In this paper, we introduce new benchmark scenarios for testing FRI methods. These benchmarks aim to serve as a reference for evaluating and comparing the accuracy and effectiveness of FRI methods. By using these benchmarks, researchers can compare different FRI methods and identify areas for improvement in the field of fuzzy inference.

show abstract