Intrusion detection in cloud computing based attacks patterns and risk assessment

Youssef, Ben Charhi; Nada, Mannane; Bendriss, Elmehdi; Regragui, Boubker

doi:10.1109/sysco.2016.7831341

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…Security researchers need to have a seamless mechanism to integrate and analyze various information generated by heterogeneous sources implemented in cloud environment with the aim to detect intrusion and reduce false positive alerts. [1] Kleber, schulter et al [2] have proposed an IDS service at cloud middleware layer with an audit system designed to cover attacks that Network IDS and Host IDS cannot detect. In [3] authors presented an approach of detection using five major classifiers to characterize the nature of an attack, classification by attack vector, attack target, operational impact, informational impact and by defense, which provide the network administrator with information of how to mitigate an attack.…”

Section: Related Workmentioning

confidence: 99%

Intrusion detection in cloud computing based attack patterns and risk assessment

Youssef¹,

Nada²,

Bendriss³

et al. 2017

Adv. sci. technol. eng. syst. j.

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This paper is an extension of work originally presented in SYSCO CONF.We extend our previous work by presenting the initial results of the implementation of intrusion detection based on risk assessment on cloud computing. The idea focuses on a novel approach for detecting cyber-attacks on the cloud environment by analyzing attacks pattern using risk assessment methodologies. The aim of our solution is to combine evidences obtained from Intrusion Detection Systems (IDS) deployed in a cloud with risk assessment related to each attack pattern. Our approach presents a new qualitative solution for analyzing each symptom, indicator and vulnerability analyzing impact and likelihood of distributed and multi-steps attacks directed to cloud environments. The implementation of this approach will reduce the number of false alerts and will improve the performance of the IDS.

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Section: Related Workmentioning

confidence: 99%

Intrusion detection in cloud computing based attack patterns and risk assessment

Youssef¹,

Nada²,

Bendriss³

et al. 2017

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

show abstract

“…For example, the authors of [4], [5], and [6] introduced solutions to detect DoS attacks. Alternatively, one can also rely on attacks' patterns and risk assessment [9], game theory [10], and supervised learning [11] to detect and counter cyber threats. The common limitation of these methods is the relatively low accuracy in detecting cyberattacks, and they are unable to work effectively in real-time cloud systems with different types of attacks.…”

Section: Introductionmentioning

confidence: 99%

Cyberattack detection in mobile cloud computing: A deep learning approach

Nguyen

Hoang

Niyato

et al. 2018

2018 IEEE Wireless Communications and Networking Conference (WCNC)

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With the rapid growth of mobile applications and cloud computing, mobile cloud computing has attracted great interest from both academia and industry. However, mobile cloud applications are facing security issues such as data integrity, users' confidentiality, and service availability. A preventive approach to such problems is to detect and isolate cyber threats before they can cause serious impacts to the mobile cloud computing system. In this paper, we propose a novel framework that leverages a deep learning approach to detect cyberattacks in mobile cloud environment. Through experimental results, we show that our proposed framework not only recognizes diverse cyberattacks, but also achieves a high accuracy (up to 97.11%) in detecting the attacks. Furthermore, we present the comparisons with current machine learning-based approaches to demonstrate the effectiveness of our proposed solution.

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Assessing Cloud Computing Security Threats in Malaysian Organization Using Fuzzy Delphi Method

Zainuddin

Yusuff

Samy

2022

Recent Advances in Soft Computing and Data Mining

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Intrusion detection in cloud computing based attacks patterns and risk assessment

Cited by 7 publications

References 6 publications

Intrusion detection in cloud computing based attack patterns and risk assessment

Intrusion detection in cloud computing based attack patterns and risk assessment

Cyberattack detection in mobile cloud computing: A deep learning approach

Assessing Cloud Computing Security Threats in Malaysian Organization Using Fuzzy Delphi Method

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