Cybersecurity and Cyber Forensics: Machine Learning Approach Systematic Review

Source:
By:Ibrahim Goni, Jerome Mishion Gumpy, Timothy Umar Maigari

DOI: https://doi.org/10.30564/ssid.v2i2.2495

Abstract:

The proliferation of cloud computing and internet of things has led to the connectivity of states and nations (developed and developing countries) worldwide in which global network provide platform for the connection.Digital forensics is a field of computer security that uses software applications and standard guidelines which support the extraction of evidences from any computer appliances which is perfectly enough for the court of law to use and make a judgment based on the comprehensiveness, authenticity and objectivity of the information obtained. Cybersecurity is of major concerned to the internet users worldwide due to the recent form of attacks,threat, viruses, intrusion among others going on every day among internet of things. However, it is noted that cybersecurity is based on confidentiality,integrity and validity of data. The aim of this work is make a systematic review on the application of machine learning algorithms to cybersecurity and cyber forensics and pave away for further research directions on the application of deep learning, computational intelligence, soft computing to cybersecurity and cyber forensics.

References:

[1] Shahzad S. protecting the integrity of digital evidence and basic human rights during the process of digital forensics. Ph.D. thesis Stockholm University,2015. [2] Abdalzim A. M. A., Amin B. A. M. A survey on mobile forensics for android smart phones IOSR.Journal of computer engineering, 2015, 17(2): 15-19. [3] Nickson M. K., Victor R. K.,Venter H. Divergency deep learning cognitive computing techniques into cyber forensics. Elservier Forensics Science international synergy, 2019, 1: 61-67. [4] Rukayat A. A., Charles O. U., Florence A. O. Computer forensics guidelines: a requirement for testing cyber crime in Nigeria now? 2017. [5] Casey E. Editorial- A sea change in digital forensics and incident response. Digital investigation evidence Elservier Ltd., 2016, 17: A1-A2. [6] Ehsan S., Giti J. Seminars in proactive artificial intelligence for cyber security consulting and research.Systematic cybernetics and informatics, 2019, 17(1):297-305 [7] Bandir A. Forensics analysis using text clustering in the age of large volume data: a review. International journal of advanced computer and application, 2019,10(6): 72-76. [8] Al-Jadir I., Wong K. W., Fing C. C., Xie H. Enhensing digital forensics analysis using memetic algorithm feature selection method for document clustering. IEEE international conference on systems, Man and cybernetics, 2018: 3673-3678. [9] Sunil B., Preeti B. Application of artificial intelligence in cyber security. International journal of engineering research in computer science and engineering, 2018, 5(4): 214-219. [10] David O. A., Goodness O., Etecte M. A. Unbated cyber terrorism and huma security in Nigeria.Asian social science, 2019, 15(11): 105-115. [11] April. Threat start-SMS spam volume by month of each region SC magazine. 2014. Available online at:https://www.scmagazine.com/april-2014-threat-stats/slideshowz [12] Apruzze G., Colajanni M. F., Ferreti L., Marchett M.On the effectiveness of machine learning for cyber security in 2018. IEEE international conference on cyber conflict, 2018: 371-390. [13] Buckza A. L., Guven E. A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE communication survey and totorials, 2016, 18(2): 1153-1176 [14] Biswas S. K. Intrusion detection using machine learning: A comparison study. International Journal of pure and applied mathematics, 2018, 118(19):101-114 [15] Y. Xin, Kong L., Liu Z., Chen Y., Zhu H., Gao M.,Hou H., Wang C. Machine learning and deep learning methods for cyber security. IEEE Access, 2018, 6:35365-35381. [16] N. Miloseivic, Denghantanh A., Choo K. K. R. Machine learning aided android malware classification.Computer and electrical engineering, 2017, 61: 266-274. [17] B. Geluvaraj, Stawik P. M., Kumar T. A. The future of cyber security: the major role of Artificial intelligence, Machine learning and deep learning in cyber space. International conference on computer network and communication technologies Springer Singapore,2019: 739-747. [18] H. Mohammed B., Vinaykumar R., Soman K. P. A short review on applications of deep learning for cyber security, 2018. [19] M. Rege, Mbah R. B. K. Machine learning for cyber defense and attack. in the 7th International conference on data analysis. 2018: 73-78. [20] D. Ding, Hang Q. L., Xing Y., Ge X., Zhang X. M.A survey on security control and attack detection for industrial cyber physical system. Neuro-computing,2018, 275: 1674-1683. [21] D. Berman S., Buczak A. L., Chavis J. S., Corbelt C.L. A survey of deep learning methods for cyber security information. 2018, 10(4). [22] Y. Wang, Ye Z., Wan P., Zhao J. A survey of dynamic spectrum allocation based on reinforcement learning algorithms in cognitive radio network. Artificial intelligence review, 2019, 51(3): 413-506. [23] A. Abubakar, Paranggono B. Machine learning based intrusion detection system for software defined networks. 7th International conference on Emerging security techniques IEEE, 2017: 138-143. [24] S. Jose, Malathi D., Reddy B., Jayaseeli D. A survey on anomaly based host intrusion detection system. Journal of physics. Conference series, 2018,1000(1). [25] S. Dey, Ye Q., Sampalli S. A Machine learning based intrusion detection scheme for data fusion in mobile cloud involving heterogeneous clients network. Information fussion, 2019, 49: 205-215. [26] P. Deshpande, Sharma S. C., Peddoju S. K., Junaid S. HIDS: a host based intrusion detection system for cloud computing environment. International journal of system assuarance engineering and management,2018, 9(3): 567-576. [27] M. Nobakht, Sivaraman V., Boreli R. A host-Based Intrusion detection and mitigation framework for smart IoT using open flow in 11th International conference on availability reliability and security IEEE.2016: 147-156. [28] A. Meshram, Christian H. Anomaly detection in industrial networks using machine learning: A road map. Machine learning for cyber physical system.Springer Berlin Heldelberg, 2017: 65-72. [29] R. Devakunchari, Souraba, Prakhar M. A study of cyber security using machine learning techniques.International journal of innovative technology and exploring engineering, 2019, 8(7): 183-186. [30] E. Alison N. FLUF: fuzzy logic utility framework to support computer network defense decision making.IEEE, 2016. [31] A. Taylor, Leblanc S., Japkowicz N. Anomaly detection in auto-mobile control network data with long short term memory network in data science and advance analytics. IEEE international conference.2016: 130-139. [32] O. Amosov S., Ivan Y. S., Amosovo S. G. Recognition of abnormal traffic using deep neural networks and fuzzy logic. International Multi-conference on industrial engineering and modern technologies IEEE, 2019. [33] M. Gyun L. Artificial Intelligence for development series: Report on AI and IoT in Security Aspect.2018. [34] L. Matt. Rise of machine: machine learning & its cybersecurity applications. NCC group white paper,2017. [35] National cyber security center UK. https://www.ncsc.gov.uk [36] A. Nuril, Supriyanto. Forensic Authentication of WhatsApp Messenger Using the Information Retrieval Approach. International Journal of Cyber Security and Digital Forensics (IJCSDF), 2019, 8(3):206-212. [37] A Marfianto, I Riadi. WhatsApp Messenger Forensic Analysis Based on Android Using Text Mining Method. International Journal of Cyber Security and Digital Forensics (IJCSDF), 2018 7(3): 319-327. [38] N Anwar, I. Riadi. Forensic Investigative Analysis of WhatsApp Messenger Smartphone Against WhatsApp Web-Based. Journal Information Technology Electromagnetic Computing and Information,2017, 3(1): 1-10. [39] S. Ikhsani, C. Hidayanto, Whatsapp and LINE Messenger Forensic Analysis with Strong and Valid Evidence in Indonesia. Tek. ITS, 2016, 5(2): 728-736. [40] M. Ashawa, S. Morris. Analysis of Android Malware Detection Techniques: A Systematic Review. International Journal of Cyber Security and Digital Forensics (IJCSDF), 2019, 8(3): 177-187. [41] W. Songyang, Wang, P., Zhang, Y. Effective detection of android malware based on the usage of data flow APIs and machine learning: Information and Software Technology, 2016, 75: 17-25. [42] Anastasia, S., Gamayunov, D. Review of the mobile malware detection approaches: Parallel, Distributed and Network-Based Processing (PDP). In: Proc.2015. IEEE 23rd Euro micro International Conference, 2015: 600--603. [43] D. Anusha, Troia, F. D., Visaggio, C. A., Austin, T.H., Stamp, M. A comparison of static,dynamic, and hybrid analysis for malware detection. Journal of Computer Virology and Hacking Techniques,2017,13(1): 1-12. [44] S. Morgan. Cyber security Business Report. 2017.Retrieved from CSO:https://www.csoonline.com/article/3237674/ransomware/ransomware-damage-costs-predicted-to-hit-115b-by-2019 [45] R. Collier. NHS ransomware attack spreads worldwide. CMAJ. 2017, 189(22): 786-787.https://doi.org/10.1503/cmaj.1095434 [46] H. Trisnasenjaya, I. Riadi Forensic Analysis of Android-based WhatsApp Messenger Against Fraud Crime Using The National Institute of Standard and Technology Framework. International Journal of Cyber Security and Digital Forensics (IJCSDF), 2019,8(1): 89-97. [47] H. Parag Rughani. Artificial Intelligence Based Digital Forensics Framework. International Journal of Advanced Research in Computer Science, 2017,8(8): 10-14. [48] 2016: Current State of Cybercrime, RSA Whitepaper.2016. [49] World Internet Users and 2017 Population Stats. Accessed from http://http://www.internetworldstats.com/stats [50] R. Mark. Computer forensics: Basics. Lecture note Purdue University, 2004. [51] Ibrahim Goni & Ahmed L. Propose Neuro-Fuzzy-Genetic Intrusion Detection System.International Journal of Computer Applications, 2015, 115(8). Available online at:http://www.ijcaonline.com/archives/volume115/number8/20169-2320