Fuzzy Data Mining-Based Framework for Forensic Analysis and Evidence Generation in Cloud Environment

Santra, Palash; Roy, Prasanna; Hazra, Debojyoti; Mahata, Puspa

doi:10.1007/978-981-10-7386-1_10

Cited by 7 publications

(6 citation statements)

References 20 publications

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“…Again, the employment of agents and the quality of the evidence are crucial points to focus on. For forensic acquisition, a fuzzy‐based data mining technique was developed 23 . For forensic monitoring, analysis, and evidence creation for cloud logs, this fuzzy‐based expert system was presented.…”

Section: Related Workmentioning

confidence: 99%

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On developing Block‐Chain based Secure Storage Model (BSSM) with auditing and integrity analysis in the cloud

Apirajitha,

Sathianesan

2023

Concurrency and Computation

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SummaryIn present scenario, Cloud Computing is developing vast, as the resources are utilized by many people and provided on‐demand. Recent works on cloud reports the success rates of cyber activities using cloud models for deceptive actions, because of its significant features and lack of apposite forensic models for cloud. For developing a cloud model for forensic support is an important and intricate issue like, directorial and technical challenges due to system distribution, virtualization, and dynamic characteristic of nodes. Considering the issues, this article develops a Block‐chain based Secure Storage Model (BSSM) incorporating Integrity and Auditing Examination Model for evaluating reliability of forensic data and auditing for validation. Moreover, this model effectively works on the domain of Forensic as a Service (FaaS) in cloud. The forensic data are formed into block‐chain for ensuring security. The model also aids in reducing the storage space effectively and the auditing and integrity analysis model incorporated in this work minimizes the dependency of trusts on Cloud Service Provider (CSP). And, the evaluation results show that the proposed model effectively achieves the objective in better way than compared works.

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Section: Related Workmentioning

confidence: 99%

“…For forensic acquisition, a fuzzy-based data mining technique was developed. 23 For forensic monitoring, analysis, and evidence creation for cloud logs, this fuzzy-based expert system was presented. CSP had complete control over the storage of all evidence.…”

Section: Related Workmentioning

confidence: 99%

On developing Block‐Chain based Secure Storage Model (BSSM) with auditing and integrity analysis in the cloud

Apirajitha,

Sathianesan

2023

Concurrency and Computation

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“…Here again, evidence integrity, use of agents are major issues to be concentrated. A fuzzy-based data mining approach was introduced for forensic acquisition [37]. This fuzzy-based expert system was proposed for forensic monitoring, analysis, and evidence generation for cloud logs.…”

Section: Related Workmentioning

confidence: 99%

Digital Forensics Architecture for Evidence Collection and Provenance Preservation in IaaS Cloud Environment Using SDN and Blockchain Technology

Pourvahab

Ekbatanifard

2019

IEEE Access

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Cloud forensics is an intelligent evolution of digital forensics that defends against cyber-crimes. However, centralized evidence collection and preservation minimizes the reliability of digital evidence. To resolve this severe problem, this paper proposes a novel digital forensic architecture using fast-growing Software-Defined Networking (SDN) and Blockchain technology for Infrastructure-as-a-Service (IaaS) cloud. In this proposed forensic architecture, the evidence is collected and preserved in the blockchain that is distributed among multiple peers. To protect the system from unauthorized users, Secure Ring Verification based Authentication (SRVA) scheme is proposed. To strengthen the cloud environment, secret keys are generated optimally by using Harmony Search Optimization (HSO) algorithm. All data are encrypted based on the sensitivity level and stored in the cloud server. For encryption, Sensitivity Aware Deep Elliptic Curve Cryptography (SA-DECC) algorithm is presented. For every data stored in the cloud, a block is created in the SDN controller and the history of data is recorded as metadata. In each block, the Merkle hash tree is built by using Secure Hashing Algorithm-3 (SHA-3). Our system allows users to trace their data by deploying Fuzzy based Smart Contracts (FCS). Finally, evidence analysis is enabled by constructing Logical Graph of Evidence (LGoE) collected from the blockchain. Experiments are conducted in an integrated environment of java (for cloud and blockchain) and network simulator-3.26 (for SDN). The extensive analysis shows that proposed forensic architecture shows promising results in Response time, Evidence insertion time, Evidence verification time, Communication overhead, Hash computation time, Key generation time, Encryption time, Decryption time and total change rate.INDEX TERMS Software-defined networking, blockchain, evidence collection, cloud forensics, security.

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“…Thus, analyzing electronic evidence in internet pyramid selling becomes very important and crucial. Many researchers analyzed the electronic evidence analysis process of internet pyramid selling, but there are few data forensics models [6,7].…”

Section: Realted Workmentioning

confidence: 99%

A Method of Mining Key Accounts from Internet Pyramid Selling Data

2019

Teh. vjesn.

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Internet pyramid selling causes great harm and has difficulty in obtaining evidence. As most of internet pyramid selling often uses virtual coins to complete cash settlement, criminals can exchange virtual coins by controlling visual accounts to transfer funds illegally. The purpose of the work is to mine the key accounts and build evidence chain of personnel and funds. In order to deal with a large number of transaction data, we characterize the virtual currency trading data, which is divided into seven transaction characteristics. The key accounts are these outliers which have too prominent trading behaviour. So we use positive samples to train One Class Support Vector Machine (OCSVM) classification to find trading behaviours' boundary and detect outliers in classification. Then, the correlation between member's information and pyramid selling hierarchical relationship can also be obtained by further linking the trading behaviour of key accounts. Finally, we can screen out the virtual accounts directly controlled by the pyramid selling organization, analyse the flow of funds, investigate and obtain evidence on amount of money involved in pyramid selling. The experimental results show that the classification model cannot only establish the normal model for account transfer behaviour, but also effectively identify abnormal transfer behaviour, thus improving efficiency of investigation and evidence collection for economic investigation departments.

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Fuzzy Data Mining-Based Framework for Forensic Analysis and Evidence Generation in Cloud Environment

Cited by 7 publications

References 20 publications

On developing Block‐Chain based Secure Storage Model (BSSM) with auditing and integrity analysis in the cloud

On developing Block‐Chain based Secure Storage Model (BSSM) with auditing and integrity analysis in the cloud

Digital Forensics Architecture for Evidence Collection and Provenance Preservation in IaaS Cloud Environment Using SDN and Blockchain Technology

A Method of Mining Key Accounts from Internet Pyramid Selling Data

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