Intrusion detection system for cloud forensics using bayesian fuzzy clustering and optimization based SVNN

Tummalapalli, Siva Rama Krishna; Chakravarthy, A. S. N.

doi:10.1007/s12065-020-00410-y

Cited by 8 publications

(6 citation statements)

References 14 publications

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“…They defined evaluation criteria for digital forensic techniques in IaaS, PaaS, and SaaS models, identifying gaps that require further research. Tummalapalli and Chakravarthy [ 26 ] proposed an intrusion detection framework for cloud forensics based on a two-level gravitational group search-based support vector neural network classifier with clustering and a low false-positive rate. Purnaye and Kulkarni [ 27 ] proposed a more generic level taxonomy of cloud forensics solutions and strategies that would help researchers gain more knowledge in this field of study.…”

Section: Related Surveysmentioning

confidence: 99%

Cloud Digital Forensics: Beyond Tools, Techniques, and Challenges

Malik,

Bhatti,

Park

et al. 2024

Sensors

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Cloud computing technology is rapidly becoming ubiquitous and indispensable. However, its widespread adoption also exposes organizations and individuals to a broad spectrum of potential threats. Despite the multiple advantages the cloud offers, organizations remain cautious about migrating their data and applications to the cloud due to fears of data breaches and security compromises. In light of these concerns, this study has conducted an in-depth examination of a variety of articles to enhance the comprehension of the challenges related to safeguarding and fortifying data within the cloud environment. Furthermore, the research has scrutinized several well-documented data breaches, analyzing the financial consequences they inflicted. Additionally, it scrutinizes the distinctions between conventional digital forensics and the forensic procedures specific to cloud computing. As a result of this investigation, the study has concluded by proposing potential opportunities for further research in this critical domain. By doing so, it contributes to our collective understanding of the complex panorama of cloud data protection and security, while acknowledging the evolving nature of technology and the need for ongoing exploration and innovation in this field. This study also helps in understanding the compound annual growth rate (CAGR) of cloud digital forensics, which is found to be quite high at ≈16.53% from 2023 to 2031. Moreover, its market is expected to reach ≈USD 36.9 billion by the year 2031; presently, it is ≈USD 11.21 billion, which shows that there are great opportunities for investment in this area. This study also strategically addresses emerging challenges in cloud digital forensics, providing a comprehensive approach to navigating and overcoming the complexities associated with the evolving landscape of cloud computing.

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Section: Related Surveysmentioning

confidence: 99%

Cloud Digital Forensics: Beyond Tools, Techniques, and Challenges

Malik,

Bhatti,

Park

et al. 2024

Sensors

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“…A progressing Cloud Security Alliance (CSA) research indicated that DDoS attacks are the common attacks on cloud security [4]. To illustrate, distinguished scientific works presented in [5][6][7][8][9] focused on creating a more distinguished phase to combat DDoS attacks.…”

Section: Figure 1: Classes Of Ddos Attackmentioning

confidence: 99%

DDoS Attack Detection in Cloud Computing Based on Ensemble Feature Selection and Deep Learning

Sanjalawe¹,

Althobaiti²

2023

Computers, Materials &Amp; Continua

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Intrusion Detection System (IDS) in the cloud Computing (CC) environment has received paramount interest over the last few years. Among the latest approaches, Deep Learning (DL)-based IDS methods allow the discovery of attacks with the highest performance. In the CC environment, Distributed Denial of Service (DDoS) attacks are widespread. The cloud services will be rendered unavailable to legitimate end-users as a consequence of the overwhelming network traffic, resulting in financial losses. Although various researchers have proposed many detection techniques, there are possible obstacles in terms of detection performance due to the use of insignificant traffic features. Therefore, in this paper, a hybrid deep learning mode based on hybridizing Convolutional Neural Network (CNN) with Long-Short-Term Memory (LSTM) is used due to its robustness and efficiency in detecting normal and attack traffic. Besides, the ensemble feature selection, mutualization aggregation between Particle Swarm Optimizer (PSO), Grey Wolf Optimizer (PSO), Krill Hird (KH), and Whale Optimization Algorithm (WOA), is used to select the most important features that would influence the detection performance in detecting DDoS attack in CC. A benchmark dataset proposed by the Canadian Institute of Cybersecurity (CIC), called CICIDS 2017 is used to evaluate the proposed IDS. The results revealed that the proposed IDS outperforms the state-of-the-art IDSs, as it achieved 97.9%, 98.3%, 97.9%, 98.1%, respectively. As a result, the proposed IDS achieves the requirements of getting high security, automatic, efficient, and self-decision detection of DDoS attacks.

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“…Siva Rama Krishna Tummalapalli [15] develops and Bayesian fuzzy clustering and cluster search laid on support vector neural network based intrusion detection mechanism simulator for clustering and two-level classifiers works on cloud environment. "A cloud forensics tamperproof framework for cloud forensics is developed by author that is available in a cloud environment that is untrusted and multi-tenancy.…”

Section: Introductionmentioning

confidence: 99%

“…A model for improving security, which can be used in a cloud environment. Forensic preparedness is a way of maximizing the potential of an organization to respond to violations [15]. The below mention graph and table shows that number of paper published in various digital libraries like ACM,IEEE, ScienceDirect, Springer and Elsevier.…”

Section: Introductionmentioning

confidence: 99%

An evolutionary algorithmic framework cloud based evidence collection architecture

Rathore

Khan²,

Kumar

et al. 2023

Multimed Tools Appl

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Forensic in cloud computing is an advancement of evolutionary modern forensic science that protects against cyber criminals. Single centralize point compilation and storage of data, however, overcome the authenticity of digital evidence. In order to address this serious issue, this article suggests a evolutionary modern algorithm automated forensic platform leveraging infrastructure as a cloud service (IaaS) based on Blockchain concept. This proposed forensic structural design, evidence collection of evidence and stored on a blockchain which is circulated around several peer blocks. Secure Block Verification Mechanism (SBVM) is proposed to Safeguarding the device from unauthorised users. Using the cuckoo search optimization algorithm for strengthening of the cloud environment, secret keys are optimally generated. On the bases of level of confidentiality, all data is stored and encrypted at cloud authentication server. Confidentiality-based Algebraically Homomorphic Cryptosystems learning is presented with a fast-forwarding algorithm for encryption. A block in the SDN controller is created for every data and information is stored in the cloud service provider and the history is recorded as metadata data about data. A hash based tree is constructed in each block by Secure Hash Algorithm version -3 of 512 bits. By implementing graph theory-based graph neural networks in Smart Contracts, our framework enables users to track their data (GNNSC). Finally, the construction of a evidence graph using blockchain data enables evidence analysis. Experiments was carried out in a Python programming and blockchain integrated cloud environment with network simulator-3.30 (for Software Defined Network). As part of result our newly designed forensic architecture using blochchain (FAuB) good results in terms of evidence response time, insertion times of cloud evidence, verification time of evidence, computational overhead of evidence, hashes calculation time, keys generations times of evidence, evidence encryption time, evidence decryptions time, and total overall change rate of evidence, according to a comprehensive comparative study.

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Intrusion detection system for cloud forensics using bayesian fuzzy clustering and optimization based SVNN

Cited by 8 publications

References 14 publications

Cloud Digital Forensics: Beyond Tools, Techniques, and Challenges

Cloud Digital Forensics: Beyond Tools, Techniques, and Challenges

DDoS Attack Detection in Cloud Computing Based on Ensemble Feature Selection and Deep Learning

An evolutionary algorithmic framework cloud based evidence collection architecture

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