Intrusion Detection and Prevention on Flow of Big Data Using Bacterial Foraging

Ahmad, Khaleel; Kumar, Gaurav; Wahid, Abdul; Kirmani, Mudasir M

doi:10.4018/978-1-4666-6559-0.ch018

Cited by 3 publications

(3 citation statements)

References 32 publications

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“…Moreover, there are distinct author clusters, denoted as different clusters or subgroups within the network, reflecting different collaborative patterns among researchers. For instance, authors like li [11], meng [12], ahmad [13] and "wahid a" belong to separate clusters, indicating potential isolated or distinct collaborations within their groups.…”

Section: Collaboration Networkmentioning

confidence: 99%

Mapping the Evolution of Intrusion Detection in Big Data: A Bibliometric Analysis

Yaseen,

Albahri

2023

Mesopotamian Journal of Big Data

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This study provides a comprehensive analysis of the dynamic amalgamation of intrusion detection and big data, revealing trends and patterns within cybersecurity research. The investigation reveals a notable surge in scholarly output from 2018 onwards, reflecting heightened interest and exploration within the field. Dominant themes such as "intrusion detection," "big data," and "machine learning" underscore the integration of security concerns with advanced technologies. Geographical influences showcase diverse contributions, with varying citation impacts from countries like India, China, and Saudi Arabia. Author contributions reveal a balance between prolific authors and impactful contributions from authors with fewer publications. Recommendations include fostering interdisciplinary collaborations, integrating advanced computational methods, and conducting longitudinal studies to gauge sustained impacts. This research underscores collaboration dynamics, thematic evolution, and global influences as pivotal facets within the realm of intrusion detection and big data, guiding future research to fortify digital security in an ever-evolving technological landscape.

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Section: Collaboration Networkmentioning

confidence: 99%

Mapping the Evolution of Intrusion Detection in Big Data: A Bibliometric Analysis

Yaseen,

Albahri

2023

Mesopotamian Journal of Big Data

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“…On the other hand, Bacterial Foraging Optimization is another inspiring example of SI, where bacteria's formation based on environmental parameters were inspired to develop a sophisticated algorithm for multi-agent optimization. The implementation of Swarm algorithms in cloud security can be found in the following fields: Authentications [17], [18], Forensics [19], and Virtualizations [20].…”

Section: B Swarm Algorithmsmentioning

confidence: 99%

“…[107] proposed a Firefly Swarm approach for developing new connections in social networks based on big data analysis. [20] offered Bacterial Foraging to prevent security threats on the flow of big-data information. [108] developed a novel hybrid bio-inspired algorithm using a Multilayer Perceptron (MLP) to handle big data security.…”

Section: G Virtualizationmentioning

confidence: 99%

Applications and Evaluations ofBio-InspiredApproaches in Cloud Security: A Review

et al. 2020

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Cloud computing gained much popularity in the recent past due to its many internet-based services related to data, application, operating system, and eliminating the need for central hardware access. Many of the challenges associated with cloud computing can be specified as network load, security intrusion, authentication, biometric identification, and information leakage. Numerous algorithms have been proposed and evaluated to solve those challenges. Among those, bio-inspired algorithms such as Evolutionary, Swarm, Immune, and Neural algorithms are the most prominent ones which are developed based on nature's ecosystems. Bio-inspired algorithms' adaptability allows many researchers and practitioners to utilize them to solve many security-related cloud computing issues. This paper aims to explore previous research, recent studies, challenges, and scope for further analysis of cloud security. Therefore, this study provides an overview of bio-inspired algorithms application and evaluations, taking into account cloud security challenges,

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Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

Torre-Bastida

Díaz-de-Arcaya

Osaba

et al. 2021

Neural Comput & Applic

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This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.

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Intrusion Detection and Prevention on Flow of Big Data Using Bacterial Foraging

Cited by 3 publications

References 32 publications

Mapping the Evolution of Intrusion Detection in Big Data: A Bibliometric Analysis

Mapping the Evolution of Intrusion Detection in Big Data: A Bibliometric Analysis

Applications and Evaluations ofBio-InspiredApproaches in Cloud Security: A Review

Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions

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