Document Type
Review Article
Subject Areas
Mathematics, Statistics, Computer Science, Physics and Astronomy
Keywords
Intrusion Detection; Signature-based detection; Anomaly-based detection; Machine Learning; Big data, Distributed systems
Abstract
We live in a time where data stream by the second, which makes intrusion detection a more difficult and tiresome task, and in turn intrusion detection systems require an efficient and improved detection mechanism to detect the intrusive activities. Moreover, handling the size, complexity, and availability of big data requires techniques that can create beneficial knowledge from huge streams of the information, which imposes the challenges on the process of both designing and management of both Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) in terms of performance, sustainability, security, reliability, privacy, energy consumption, fault tolerance, scalability, and flexibility. IDSs and IPSs utilize various methodologies to guarantee security, accessibility and reliability of enterprise computer networks. This paper presents a comprehensive study of the Distributed Intrusion Detection Systems in Big Data, and presents intrusion detection and prevention techniques that utilize machine learning, big data analytics techniques in distributed systems of the intrusion detection.
How to Cite This Article
Hameed, Bashar; AlHabshy, AbdAllah; and ElDahshan, Kamal
(2021)
"Distributed Intrusion Detection Systems in Big Data: A Survey,"
Al-Azhar Bulletin of Science: Vol. 32:
Iss.
1, Article 14.
DOI: https://doi.org/10.21608/absb.2021.63810.1100