A Comprehensive Review on Automated Visualization Techniques for Intrusion Detection Systems

In supervised learning-based classification, ensembles have been successfully employed to different application domains. In the literature, many researchers have proposed different ensembles by considering different combination methods, training datasets, base classifiers, and many other factors. Artificial-intelligence-(AI-) based techniques play prominent role in development of ensemble for intrusion detection (ID) and have many benefits over other techniques. However, there is no comprehensive review of ensembles in general and AI-based ensembles for ID to examine and understand their current research status to solve the ID problem. Here, an updated review of ensembles and their taxonomies has been presented in general. The paper also presents the updated review of various AI-based ensembles for ID (in particular) during last decade. The related studies of AI-based ensembles are compared by set of evaluation metrics driven from (1) architecture & approach followed; (2) different methods utilized in different phases of ensemble learning; (3) other measures used to evaluate classification performance of the ensembles. The paper also provides the future directions of the research in this area. The paper will help the better understanding of different directions in which research of ensembles has been done in general and specifically: field of intrusion detection systems (IDSs).

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Network Intrusion Visualization with NIVA, an Intrusion Detection Visual and Haptic Analyzer

Information Visualization ◽

10.1057/palgrave.ivs.9500044 ◽

2003 ◽

Vol 2 (2) ◽

pp. 82-94 ◽

Cited By ~ 4

Author(s):

Craig Scott ◽

Kofi Nyarko ◽

Tanya Capers ◽

Jumoke Ladeji-Osias

Keyword(s):

Intrusion Detection ◽

Communication Networks ◽

Large Scale ◽

Three Dimensional ◽

Relevant Information ◽

Gravitational Theory ◽

Intrusion Detection Systems ◽

Detection Systems ◽

Network Intrusion ◽

Visualization Techniques

The rapid growth of malicious activities on worldwide communication networks, such as the Internet, has highlighted the need for efficient intrusion detection systems. The efficiency of traditional intrusion detection systems is limited, in part, by their inability to relay effectively relevant information due to their lack of interactive/immersive technologies. In this paper, we explore several network visualization techniques geared toward intrusion detection on small- and large-scale networks. We also examine the use of haptics in network intrusion visualization. By incorporating concepts from electromagnetics, fluid dynamics, and gravitational theory, we show that haptic technologies can provide another dimension of information critical to the efficient visualization of network intrusion data. Furthermore, we explore the applicability of these visualization techniques in conjunction with commercial network intrusion detectors. Finally, we present a network intrusion visualization application with haptic integration, NIVA, which allows the analyst to interactively investigate as well as efficiently detect structured attacks across time and space using advanced interactive three-dimensional displays.

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