Enhancing Cloud Forensic Investigation System in Distributed Cloud Computing Using DK-CP-ECC Algorithm and EK-ANFIS
DOI:
https://doi.org/10.13052/jmm1550-4646.1933Keywords:
Digital forensic investigation, group key generation, L-caesar cipher, elliptic curve cryptography, feature extraction, deer hunting genetic algorithm, exponential membership function adaptive neuro-fuzzy interference system (EK-ANFIS)Abstract
The investigation as well as recovery of data gathered as of digital devices associated with computer crime is involved in Digital Forensics (DF). In a distributed Cloud Server (CS), DF investigation is more complicated (during collecting, preserving, and reporting the evidence) as well as insecure during gathering evidence as of the cloud sources. Centered on the DF investigation system, numerous works were performed. However, lots of challenges still remain that bring about cybercrime. The work developed a robust cloud forensic investigation system centered upon distributed Cloud Computing (CC) for conquering the challenges. It is framed into the ‘3’ phase (i.e.) originally, Group Key Generation (GKG) phase that enables the authorized user to upload or download the evidence for maintaining the evidence’s trustworthiness. Distributed Key Cipher Policy Elliptic Curve Cryptography (DK-CP-ECC) algorithm performed the Secure Data Transfer (SDT) phase. It aids in maintaining the evidence’s privacy together with confidentiality. Exponential Membership Function Adaptive Neuro-Fuzzy Interference System (EK-ANFIS) carries out the CS selection with the aid of a deer hunting genetic algorithm that evades the reporting issues and renders secure evidence storage. 97% Security Level (SL) is obtained by the proposed work that is better analogized to the prevailing frameworks.
Downloads
References
Yong Zhang, Songyang Wu, Bo Jin, and Jiaying Du, ‘A blockchain-based process provenance for cloud forensics’, In IEEE 3rd IEEE International Conference on Computer and Communications (ICCC), pp. 2470–2473, 2017, doi: 10.1109/CompComm.2017.8322979.
Haider Al-Khateeb, Gregory Epiphaniou, and Herbert Daly, ‘Blockchain for modern digital forensics: The chain-of-custody as a distributed ledger’, In Blockchain and Clinical Trial, Springer, Cham, pp. 149–168, 2019, doi: 10.1007/978-3-030-11289-9_7.
Saurav Nanda, and Raymond A. Hansen, ‘Forensics as a service: Three-tier architecture for cloud based forensic analysis’, In IEEE15th International Symposium on Parallel and Distributed Computing (ISPDC), pp. 178–183, 2016, doi: 10.1109/ISPDC.2016.31.
Mahmud Hossain Md, Ragib Hasan, and Shams Zawoad, ‘Probe-IoT: A public digital ledger based forensic investigation framework for IoT’, In INFOCOM workshops, pp. 1–2, 2018, doi: 10.1109/INFCOMW.2018.8406875.
Victor R. Kebande, and Hein S. Venter, ‘Novel digital forensic readiness technique in the cloud environment’, Australian Journal of Forensic Sciences, vol. 50, no. 5, pp. 552–591, 2018.
Jung Hyun Ryu, Pradip Kumar Sharma, JeongHoon Jo, and Jong Hyuk Park, ‘A blockchain-based decentralized efficient investigation framework for IoT digital forensics’, The Journal of Supercomputing, vol. 75, no. 8, pp. 4372–4387, 2019.
Keke Gai, Jinnan Guo, Liehuang Zhu, and Shui Yu, ‘Blockchain Meets Cloud Computing: A Survey’, IEEE Communications Surveys & Tutorials, 2020, doi: 10.1109/COMST.2020.2989392.
Deevi Radha Rani and G. Geethakumari, ‘A framework for detecting anti-forensics in cloud environment’, In IEEE International Conference on Computing, Communication and Automation (ICCCA), pp. 1277–1280, 2016, doi: 10.1109/CCAA.2016.7813913.
Deevi Radha Rani, and G. Geethakumari, ‘A framework for the identification of suspicious packets to detect anti-forensic attacks in the cloud environment’, Peer-to-Peer Networking and Applications, pp. 1–14, 2020, doi: 10.1007/s12083-020-00975-6.
Shohreh Hosseinzadeh, Sampsa Rauti, Samuel Laurén, Jari-Matti Mäkelä, Johannes Holvitie, Sami Hyrynsalmi, and Ville Leppänen, ‘Diversification and obfuscation techniques for software security: A systematic literature review’, Information and Software Technology, vol. 104, pp. 72–93, 2018, doi: 10.1016/j.infsof.2018.07.007.
Roberto Battistoni, Roberto Di Pietro, and Flavio Lombardi, ‘CURE—Towards enforcing a reliable timeline for cloud forensics: Model, architecture, and experiments’, Computer Communications, vol. 91, pp. 29-43, 2016, doi: 10.1016/j.comcom.2016.03.024.
Deevi Radha Rani, and G. Geethakumari, ‘Secure data transmission and detection of anti-forensic attacks in cloud environment using MECC and DLMNN’, Computer Communications, vol. 150, pp. 799–810, 2019, doi: 10.1016/j.comcom.2019.11.048.
Indumathi Jayaraman, and Amala Stanislaus Panneerselvam, ‘A novel privacy preserving digital forensic readiness provable data possession technique for health care data in cloud’, Journal of Ambient Intelligence and Humanized Computing, pp. 1–14, 2020, doi: 10.1007/s12652-020-01931-1.
Nesrine Kaaniche, and Maryline Laurent, ‘Data security and privacy preservation in cloud storage environments based on cryptographic mechanisms’, Computer Communications, vol. 111, pp. 120–141, 2017, doi: 10.1016/j.comcom.2017.07.006.
Santosh Kumar, Sanjay Kumar Singh, Amit Kumar Singh, Shrikant Tiwari, and Ravi Shankar Singh, ‘Privacy preserving security using biometrics in cloud computing’, Multimedia Tools and Applications, vol. 77, no. 9, pp. 11017–11039, 2018.
Deebak B. D., and AL-Turjman Fadi, ‘Lightweight authentication for IoT/Cloud-based forensics in intelligent data computing’, Future Generation Computer Systems, 2020, doi: 10.1016/j.future.2020.11.010.
Jia Wang, Fang Peng, Hui Tian, Wenqi Chen, and Jing Lu, ‘Public auditing of log integrity for cloud storage systems via blockchain’, In International Conference on Security and Privacy in New Computing Environments, Springer, Cham, pp. 378–387, 2019, doi: 10.1007/978-3-030-21373-2_29.
Anyi Liu, Huirong Fu, Yuan Hong, Jigang Liu, and Yingjiu Li, ‘$LiveForen$: Ensuring Live Forensic Integrity in the Cloud’, IEEE Transactions on Information Forensics and Security, vol. 14, no. 10, pp. 2749–2764, 2019.
Kirti Dhwaj Singh, Ayushi Sharma, Shivali Singh, Vikram Singh, and SagarRane, ‘Integrity and confidentiality preservation in cloud’, In IEEE International conference of Electronics, Communication and Aerospace Technology (ICECA), vol. 2, pp. 419–424, 2017, doi: 10.1109/ICECA.2017.8212848.
Jiamin Zheng, Qikun Zhang, Xiaosong Zhang, Yuanzhang Li, and Quanxin Zhang, ‘A specific-targeting asymmetric group key agreement for cloud computing’, Chinese Journal of Electronics, vol. 27, no. 4, pp. 866–872, 2018.
Mehran Pourvahab, and Gholamhossein Ekbatanifard, ‘Digital forensics architecture for evidence collection and provenance preservation in iaas cloud environment using sdn and blockchain technology’, IEEE Access, vol. 7, pp. 153349–153364, 2019, doi: 10.1109/ACCESS.2017.
Yuan Zhang, Xiaodong Lin, and Chunxiang Xu, ‘Blockchain-based secure data provenance for cloud storage’, In International conference on information and communications security, Springer, Cham, pp. 3–19, 2018, doi: 10.1007/978-3-030-01950-1_1.
Xueping Liang, Sachin Shetty, Deepak Tosh, Charles Kamhoua, Kevin Kwiat, and Laurent Njilla, ‘Provchain: A blockchain-based data provenance architecture in cloud environment with enhanced privacy and availability’, In 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), pp. 468-477, 2017, doi: 10.1109/CCGRID.2017.8.
Xuanyu Liu, Xiao Fu, Bin Luo, and Xiaojiang Du, ‘Distributed Cloud Forensic System with Decentralization and Multi-participation’, In International Wireless Internet Conference, Springer, Cham, pp. 181–196, 2017, doi: 10.1007/978-3-319-90802-1_16.
