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Cyber crime investigation: landscape, challenges, and future research directions.

1. Introduction

2. digital forensics, 2.1. host forensics, 2.2. mobile forensics, 2.2.1. investigation phases, 2.2.2. data extraction, 2.3. network forensics, 2.4. cloud forensics, 2.4.1. forensics as a service, 2.4.2. methods and frameworks, 2.4.3. cloud forensics and mobile devices, 3. online investigations, 3.1. sources of information, 3.1.1. open web, 3.1.2. deep web, 3.1.3. dark web, 3.2. specialized sources of information, 3.2.1. social media, 3.2.2. cryptocurrency flow, 3.3. data mining, 3.3.1. natural language processing, 3.3.2. social network analysis, 3.3.3. information extraction, 3.3.4. computer vision, 4. new forensic technologies, 4.1. automation, 4.2. machine learning (ai), 4.2.1. machine learning as an investigative tool, 4.2.2. machine learning as a criminal tool, 5. open issues and research directions.

• Technical issues (e.g., effectively implementing open-source intelligence tools used in investigations).
• Legal issues (e.g., obtaining legal basis for collecting evidence that is admissible in courts).
• Ethical issues (e.g., criminal profiling).

5.1. Technical Issues

5.2. legal issues, 5.3. ethical issues, 5.4. research directions of open issues, 6. conclusions and further research, author contributions, institutional review board statement, informed consent statement, conflicts of interest.

• Billard, D. Weighted Forensics Evidence Using Blockchain. In Proceedings of the 2018 International Conference on Computing and Data Engineering, Shanghai, China, 4–6 May 2018; pp. 57–61. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Zhang, L.; Li, F.; Wang, P.; Su, R.; Chi, Z. A Blockchain-Assisted Massive IoT Data Collection Intelligent Framework. IEEE Internet Things 2021 , 15 . [ Google Scholar ] [ CrossRef ]
• Barmpatsalou, K.; Cruz, T.; Monteiro, E.; Simoes, P. Current and Future Trends in Mobile Device Forensics. ACM Comput. Surv. 2018 , 51 , 1–31. [ Google Scholar ] [ CrossRef ]
• Gu, Y.; Lin, Z. Derandomizing Kernel Address Space Layout for Memory Introspection and Forensics. In Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy, New Orleans, LA, USA, 9–11 March 2016; ACM: New York, NY, USA, 2016; pp. 62–72. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Chernyshev, M.; Zeadally, S.; Baig, Z.; Woodward, A. Mobile Forensics: Advances, Challenges, and Research Opportunities. IEEE Secur. Priv. 2017 , 15 , 42–51. [ Google Scholar ] [ CrossRef ]
• Caviglione, L.; Wendzel, S.; Mazurczyk, W. The future of digital forensics: Challenges and the road ahead. IEEE Secur. Priv. 2017 , 15 , 12–17. [ Google Scholar ] [ CrossRef ]
• Stoyanova, M.; Nikoloudakis, Y.; Panagiotakis, S.; Pallis, E.; Markakis, E.K. A survey on the Internet of things (IoT) forensics: Challenges, approaches, and open issues. IEEE Commun. Surv. Tutor. 2020 , 22 , 1191–1221. [ Google Scholar ] [ CrossRef ]
• Manral, B.; Somani, G.; Choo, K.-K.R.; Conti, M.; Gaur, M.S. A Systematic Survey on Cloud Forensics Challenges, Solutions, and Future Directions. ACM Comput. Surv. 2020 , 52 , 1–38. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Khan, S.; Gani, A.; Wahab, A.W.; Bagiwa, M.A.; Shiraz, M.; Khan, S.U.; Zomaya, A.Y. Cloud Log Forensics: Foundations, State of the Art, and Future Directions. ACM Comput. Surv. 2016 , 49 , 1–42. [ Google Scholar ] [ CrossRef ]
• Tavabi, N.; Bartley, N.; Abeliuk, A.; Soni, S.; Ferrara, E.; Lerman, K. Characterizing Activity on the Deep and Dark Web. In Proceedings of the Companion of The 2019 World Wide Web Conference, San Francisco, CA, USA, 13–17 May 2019; ACM: New York, NY, USA, 2019; pp. 206–213. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Celestini, A.; Me, G.; Mignone, M. Tor marketplaces exploratory data analysis: The Drugs Case. In Global Security, Safety and Sustainability–The Security Challenges of the Connected World ; Springer: New York, NY, USA, 2016; pp. 218–229. [ Google Scholar ] [ CrossRef ]
• Internet Organized Crime Threat Assessment. 2020. Available online: https://www.europol.europa.eu/activities-services/main-reports/internet-organised-crime-threat-assessment-iocta-2020 (accessed on 12 September 2021).
• Alonso-Fernandez, F.; Belvisi, N.M.; Hernandez-Diaz, K.; Muhammad, N.; Bigun, J. Writer Identification Using Microblogging Texts for Social Media Forensics. IEEE Trans. Biom. Behav. Identity Sci. 2021 , 3 , 405–426. [ Google Scholar ] [ CrossRef ]
• Nazah, S.; Huda, S.; Abawajy, J.; Hassan, M.M. Evolution of dark web threat analysis and detection: A systematic approach. IEEE Access 2020 , 8 , 171796–171819. [ Google Scholar ] [ CrossRef ]
• Edwards, M.; Rashid, A.; Rayson, P. A Systematic Survey of Online Data Mining Technology Intended for Law Enforcement. ACM Comput. Surv. 2015 , 48 , 54. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Zhang, X.; Li, W.; Ying, H.; Li, F.; Tang, S.; Lu, S. Emotion Detection in Online Social Networks: A Multilabel Learning Approach. IEEE Internet Things J. 2020 , 7 , 8133–8143. [ Google Scholar ] [ CrossRef ]
• Liao, X.; Yuan, K.; Wang, X.F.; Li, Z.; Xing, L.; Beyah, R. Acing the IoC Game: Toward Automatic Discovery and Analysis of Open-Source Cyber Threat Intelligence. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, Vienna, Austria, 24–28 October 2016; ACM: New York, NY, USA, 2016; pp. 755–766. [ Google Scholar ] [ CrossRef ]
• Završnik, A. Criminal justice, artificial intelligence systems, and human rights. ERA Forum 2020 , 20 , 567–583. [ Google Scholar ] [ CrossRef ] [ Green Version ]
• Raaijmakers, S. Artificial Intelligence for Law Enforcement: Challenges and Opportunities. IEEE Secur. Priv. 2019 , 17 , 74–77. [ Google Scholar ] [ CrossRef ]
• Zhang, F.; Li, W.; Zhang, Y.; Feng, Z. Data Driven Feature Selection for Machine Learning Algorithms in Computer Vision. IEEE Internet Things J. 2018 , 5 , 4262–4272. [ Google Scholar ] [ CrossRef ]
• Du, X.; Hargreaves, C.; Sheppard, J.; Anda, F.; Sayakkara, A.; Le-Khac, N.-A.; Scanlon, M. SoK: Exploring the state of the art and the future potential of artificial intelligence in digital forensic investigation. In Proceedings of the 15th International Conference on Availability, Reliability and Security, Virtual Event, Ireland, 25–28 August 2020; pp. 1–10. [ Google Scholar ] [ CrossRef ]
• Shaukat, K.; Luo, S.; Varadharajan, V.; Hameed, I.A.; Xu, M. A Survey on Machine Learning Techniques for Cyber Security in the Last Decade. IEEE Access 2020 , 8 , 222310–222354. [ Google Scholar ] [ CrossRef ]
• Zhang, X.; Liu, L.; Xiao, L.; Ji, J. Comparison of Machine Learning Algorithms for Predicting Crime Hotspots. IEEE Access 2020 , 8 , 181302–181310. [ Google Scholar ] [ CrossRef ]
• Jeong, D. Artificial intelligence security threat, crime, and forensics: Taxonomy and open issues. IEEE Access 2020 , 8 , 184560–184574. [ Google Scholar ] [ CrossRef ]
• Quick, D.; Choo, K.-K.R. Digital forensic intelligence: Data subsets and Open-Source Intelligence (DFINT+OSINT): A timely and cohesive mix. Future Gener. Comput. Syst. 2018 , 78 , 558–567. [ Google Scholar ] [ CrossRef ]
• Amatoa, F.; Castiglione, A.; Cozzolino, G.; Narduccib, F. A semantic-based methodology for digital forensics analysis. J. Parallel Distrib. Comput. 2020 , 138 , 172–177. [ Google Scholar ] [ CrossRef ]
• Watson, S.; Dehghantanha, A. Digital forensics: The missing piece of the Internet of Things promise. Comput. Fraud. Secur. 2016 , 2016 , 5–8. [ Google Scholar ] [ CrossRef ]
• Wolfe, H. Evidence analysis. Comput. Secur. 2003 , 22 , 289–291. [ Google Scholar ] [ CrossRef ]
• Louw, D. Forensic psychology. In International Encyclopedia of the Social & Behavioral Sciences , 2nd ed.; Elsevier: Amsterdam, The Netherlands, 2015; pp. 351–356. [ Google Scholar ] [ CrossRef ]
• Rogers, M. The role of criminal profiling in the computer forensics process. Comput. Secur. 2003 , 22 , 292–298. [ Google Scholar ] [ CrossRef ]

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Share and Cite

Horan, C.; Saiedian, H. Cyber Crime Investigation: Landscape, Challenges, and Future Research Directions. J. Cybersecur. Priv. 2021 , 1 , 580-596. https://doi.org/10.3390/jcp1040029

Horan C, Saiedian H. Cyber Crime Investigation: Landscape, Challenges, and Future Research Directions. Journal of Cybersecurity and Privacy . 2021; 1(4):580-596. https://doi.org/10.3390/jcp1040029

Horan, Cecelia, and Hossein Saiedian. 2021. "Cyber Crime Investigation: Landscape, Challenges, and Future Research Directions" Journal of Cybersecurity and Privacy 1, no. 4: 580-596. https://doi.org/10.3390/jcp1040029

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