Intrusion detection systems in the cloud computing: A comprehensive and deep literature review

Liu, Zhiqiang; Xu, Bo; Cheng, Bo; Hu, Xiaomei; Darbandi, Mehdi

doi:10.1002/cpe.6646

Cited by 20 publications

(10 citation statements)

References 204 publications

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“…The necessity of a more reliable cyber security system than IDSs is cemented by the fact that encrypted packets [119] are unseen by IDSs, hence these violations will not be registered earlier on in the process of intrusion, only to be realized when they have penetrated the network much deeply [120]. Subsequently, attackers mainly use them as the pathway to enter systems and networks [121].…”

Section: Common Non-artificial Intelligence Based Techniquesmentioning

confidence: 99%

Assessment of existing cyber-attack detection models for web-based systems

Awuor¹

2023

Global J. Eng. Technol. Adv.

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In the current technological environment, different entities engage in intricate cyber security approaches in order to counter damages and disruptions in web-based systems. The design of the security protocols relies on the guarantee that attacks are prevented in the web-based systems. Prevention and detection using techniques such as access control tools, encryption and firewalls present limitations in the full protection of web-based systems. Furthermore, despite the sophistication of current systems, there are still shortfalls in high false positive and false negative threat detection rates, which is attributed to poor adaptation by systems and networks to the changing threats and behavior of cyber-criminals. In this perspective, this survey paper discusses the existing cyber-attack detection models, and recommends the cyber-attack detection models and techniques that are appropriate for web-based systems. It is evident that deep learning techniques offer better performance and robustness compared to traditional machine learning techniques and other non-artificial intelligence-based techniques. Deep learning techniques learn and extract features automatically without human intervention and can also handle big and multidimensional data more conventionally than the other techniques.

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Section: Common Non-artificial Intelligence Based Techniquesmentioning

confidence: 99%

Assessment of existing cyber-attack detection models for web-based systems

Awuor¹

2023

Global J. Eng. Technol. Adv.

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“…During the SLR presented here, the search strings returned seven surveys [Ahmad et al 2022;Al-Dhaqm et al 2020b;Kanta et al 2020;Liu et al 2022;Mahapatra and Khan 2012;Prajapati and Shah 2020;Sikos 2020]. However, their main focus is different, as they address: cyber security in IoT-based cloud computing [Ahmad et al 2022], database forensic investigation process models [Al-Dhaqm et al 2020b], open source intelligence for smarter password cracking [Kanta et al 2020], intrusion detection systems in the cloud computing [Liu et al 2022], existing techniques against SQL injection attacks • Seufitelli, Brandão and Moro [Mahapatra and Khan 2012], secure data deduplication [Prajapati and Shah 2020], and packet for network forensics [Sikos 2020].…”

Section: Related Workmentioning

confidence: 99%

Exploring the Intersection between Databases and Digital Forensics

Seufitelli

Brandão²,

Moro³

2022

jidm

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Digital forensics has attracted attention from assorted researchers, who primarily work on predicting and solving digital hacks and crimes. In turn, the number and types of digital crimes have increased considerably, mainly due to the growing use of digital media to perform daily personal and professional tasks. Like most computer-related activities, data is at the center of such hacks and crimes. Hence, this work presents a systematic literature review of publications at the intersection between Digital Forensics and Databases. We discuss problems and trends of two main categories: Data Building and Database Management Systems. Overall, this research opens the doors for the communication between databases and an area with several exciting and concrete challenges, with great potential for social, economic, and technical-scientific contributions.

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“…This critical infrastructure demands meticulous protection to prevent any adverse consequences that could potentially impact society at large. When considering network security, it's crucial to acknowledge that real-world data is often transmitted across long distances [4], [5]. This approach is commonly employed in cloud technologies as well.…”

Section: Introductionmentioning

confidence: 99%

A hybrid deep learning approach for enhanced network intrusion detection

Prabu,

Sudhakar

2024

IJEECS

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The contemporary era places paramount importance on network security and cloud environments, driven by increased data transmission demands, the flexibility of cloud services, and the prevalence of global resources. Addressing the escalating threat of computer malware, the development of efficient intrusion detection systems (IDS) is imperative. This research focuses on the challenges posed by imbalanced datasets and the necessity for unsupervised learning to enhance network security. The proposed hybrid deep learning method utilizes raw data from the CSE-CIC-IDS-2018 dataset, integrating imbalanced and unsupervised learning techniques. After preprocessing and normalization, feature extraction through principal component analysis (PCA) reduces dimensionality from seventy-eight fields to ten essential features. Clustering, employing the density-based spatial clustering of applications with noise (DBSCAN) algorithm optimized with particle swarm optimization (PSO), is applied to the extracted features, distinguishing between attack and non-attack packets. Addressing dataset imbalances, imbalanced learning techniques are employed, and unsupervised learning is exemplified through the AutoEncoder (AE) algorithm. The attack cluster’s data is input into AE, a deep learning-based approach, yielding outputs for attack classification. The proposed technique (PCA+DBSCANPSO+AE) achieves an impressive 99.19% accuracy in intrusion detection, surpassing contemporary methodologies and five existing techniques. This research not only enhances accuracy but also addresses imbalanced learning challenges, utilizing the power of unsupervised learning for robust network security.

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Intrusion detection systems in the cloud computing: A comprehensive and deep literature review

Cited by 20 publications

References 204 publications

Assessment of existing cyber-attack detection models for web-based systems

Assessment of existing cyber-attack detection models for web-based systems

Exploring the Intersection between Databases and Digital Forensics

A hybrid deep learning approach for enhanced network intrusion detection

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