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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.
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Method | Complexity | Risk | Notes |
---|---|---|---|
Low Complexity | High Risk | Puts the integrity of the data at risk of accidental tampering | |
Low Complexity | Low Risk | Utilizes an external workstation | |
Medium Complexity | Low Risk | Analyzes dumps of flash memory on an external device | |
High Complexity | Medium Risk | Physically removes the flash memory | |
High Complexity | High Risk | A last resort option because it is very complex and time consuming |
Method | Sources of Information | Number of Cases | Methods of Obtaining Information | Notes |
---|---|---|---|---|
1 | 18 | 85 | Contains four subcategories, each of which can be used in investigations | |
1 | 5 | 22 | Looks for relationships and patterns in user activity | |
4 | 6 | 39 | Utilizes web crawling technology to look for crime trademarks | |
3 | 6 | 21 | Searches images, video, and audio for criminal content |
Technical Issues | Legal Issues | Ethical Issues |
---|---|---|
Effective implementation | Gathering evidence | Criminal profiling |
Author identification | Following documented method | Relationships between racial and criminal profiling |
Big forensic data reduction and management | Chain of custody | Evaluating reliability of criminal profiles |
Defining data patterns in criminal activities | Determining the validity of criminal profiles | |
IoT and digital forensics |
MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
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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Together, these six articles demonstrate the progress that cybercrime research has made over the past few years, leveraging a diverse set of theoretical …