Abstract

Intrusion Detection Systems (IDS) are crucial for maintaining the security and integrity of network infrastructures. This review paper comprehensively examines the evolution and advancements in IDS technologies, focusing on both traditional methods and contemporary machine learning and deep learning approaches. Traditional IDS methods, including signature-based and anomaly-based detection, laid the groundwork for current systems but faced challenges such as high false-positive rates and limited adaptability. Recent advancements in machine learning, specifically supervised and unsupervised learning algorithms, have significantly enhanced the accuracy and efficiency of IDS. Deep learning techniques, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), offer promising solutions for complex and high-volume network traffic analysis. This review also explores federated learning for IDS, emphasizing its potential for data privacy preservation and reduced computational load. Furthermore, hybrid models combining various algorithms are discussed for their capability to leverage the strengths of multiple techniques. The paper synthesizes current research, highlighting key methodologies, datasets, evaluation metrics, and the future direction of IDS research. By providing a thorough analysis of existing literature and identifying gaps, this review aims to guide future research efforts and practical implementations in the field of network security.

Keyword

Intrusion detection system (IDS), Machine learning, Deep learning, Network security, Federated learning.

Cite this article

Ranjan AK, Dubey AK.Evolution and advancements in intrusion detection systems: from traditional methods to deep learning and federated learning approaches. ACCENTS Transactions on Information Security. 2024;9(36):15-19. DOI:10.19101/TIS.2024.935002

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