AI in Cyber Forensics

By: Ameya Sree kasa, Department of Computer Science & Engineering (Artificial Intelligence), Madanapalle Institute of Technology & Science, Angallu (517325), Andhra Pradesh. ameyasreekasa@gmail.com

Abstract:

Since present day cyber threats are highly evolved and the volume of digital evidence is huge in most cases, ancillary techniques must be employed incurs for cyber forensic investigation. This article shows that AI technology has a huge impact on the field of cyber forensics as it significantly increases the speed and efficacy of forensic examination and analysis of cases. The article deals with the implications of AI in the field of cyber forensics with more focus on how the concepts of machine learning, deep learning, and other AI technologies in the collection, analysis, and analysis of digital evidence. This article has provided a broad consultation of the current practices of AI concerning their opportunities, limitations, and prospects for enhancing the practice of cyber forensics.

Keywords: Cyber Forensics, Digital Data, Incident Investigation, Artificial Intelligence

1. Introduction:

With the sophistication of cyber-attacks over the recent years and the amount of data generated in the digital environment been the challenge of doing cyber forensic investigations has therefore been brought. Traditional forensic methods mostly lag in helping to resolve the size and nature of the modern cybercrimes. Artificial intelligence (AI) presents some exciting solutions by automating and enhancing most areas of digital forensic analysis, from evidence collection to data interpretation. The present paper is a review of the transformative role that AI can play in cyber forensics, with special attention to how it can make investigative processes more efficient and finally improve overall results.

2. Cyber Forensics:

Cyber forensics, sometimes also known as digital forensics, is the process of retrieving, analyzing, and presenting digital evidence from computers, across a network, or digital devices concerning cybercrime and legal actions. Evidence collection, guaranteeing integrity in the acquisition of digital data is analysis of evidence, which refers to the scrutiny and identification of information and patterns relevant to the case and reporting or documentation and presentation of the investigative findings, are critical components. [1] It applies several techniques like disk forensics, network, mobile, and cloud forensics, directed at another range of digital data and environments. This field of digital forensic science has some major challenges: dealing with huge volumes of data, bypassing encryption and obfuscation methods, and finally, following the legal and ethical norms. However, new technologies in artificial intelligence, machine learning, and deep learning change the aspect of cyber forensics through automated data analyses, discovering patterns, and increasing accuracy and speed in investigations. These technologies significantly enhance the capacity of criminal activity investigation, security incident management, and compliance requirements. [2]

3. Role of AI in cyber Forensics:

AI transforms cyber forensics given that it accelerates data analysis, expunges manual and repetitive steps, and improves the identification of novel risks using sophisticated pattern identification techniques. Its functionality is to sort and analyze a great amount of data to discovers any irregularities and risks that could remain unnoticed by policemen, as well as provide an opportunity to detect breaches and malicious activities in real-time mode. AI takes it a notch higher by using previous data and identifying emerging patterns that may pose a threat to an organization’s security. It also helps separating valuable evidence from noisy or partial data sources and gives behavioral analytics for insider threat identification. [3] Rapid data processing capability, automation, and enhancement of techniques in terms of analytics make forensic investigations more efficient, and thereby deliver more targeted and efficient solutions to circumvent the cyber threats. [4]

4. Applications:

AI significantly enhances cyber forensics through various applications like below and also from figure 1:

• Malware Analysis: AI breaks viruses into components so as to identify their actions, sources, and consequences, which is helpful in designing ways to combat the viruses.
• Incident Response: AI helps in faster detection of threats, categorization and then moves on to quickly aiding recovery. [5]
• Log Analysis: AI analyze large log files seeking to identify anomalous patterns and ICOs.
• Network Traffic Monitoring: Here, AI defines other abnormal activities, penetration as well as attempts at data leak in network traffic flows. [6]
• Data Recovery: AI aids in reconstructing files that have been stripped off or in reconstructing media that has been corrupted or fragmented.
• Behavioral Analysis: AI discovers insider threats through looking for irregular patterns of how the user behaves and or communicates[7].
• Phishing Detection: Some techniques used by AI to detect phishing are as follows, The content of the mailing, URL and the senders’ information. [8]
• Predictive Threat Intelligence: AI actually predicts future threats in alignment to streak history and trends.
• Anomaly Detection: The AI is able to identify anomalous behavioral patterns in the system that may likely be security events.
• Automated Evidence Collection: This presents AI as having the added advantage of bringing order into digital evidence collection and therefore, more efficient forensics. [9]

These applications exploit AI’s capabilities of dealing with many data inputs to enhance the results and efficiency of the cyber forensic investigation.

5. Limitations and Challenges:

AI in cyber forensics faces many challenges and limitations like as mentioned below in Figure1. [2]

6. Future Advances:

In the future, the progress of AI use in cyber forensics will ensure absolute precision and present real-time analysis, and learning models will always be one step ahead of the threats. Leveraging on quantum computing could enhance the speed of computation and advanced features of predictive analytics and natural language processing would enhance threat forecasting and data mining capabilities. [10] AI will involve the decentralization of decision-making and at the same time improve the coordination of forensic disciplines. Further, progress made in ethical/Explainable AI shall solve the issue of transparency while improvement in the behavioral analysis shall improve on the identification of professional insiders and patterns of attack. [11]

7. Conclusion:

With the development of new forms of computer crimes and increasing use of digital evidence, AI act as a revolutionary means of increasing the effectiveness of investigations in the field of cyber forensics. With the help of AI including machine learning, deep learning, etc., all the forensic activities starting right from evidence capturing through data analysis are made efficient. Thus, using AI in regards to the traditional and advanced tasks of a forensic firm is immensely beneficial due to its ability to auto analyze data, detect anomalies, and provide real time intelligence on threats. When it comes to utilizing AI diverse domains like malware analysis, incident response, and predictive threat intelligence demonstrate AI’s capabilities of processing piles of data and detecting underlying patterns. However, certain issues including data privacy concerns, problems with false positives, and the necessity of developing explainable AI remain a problem. Thus, the future of cyber forensics looks rather bright with AI sustaining innovations that would add up and make the investigative techniques more efficient and precise.

8. References

1. M. F. Ansari, P. Sharma, and B. Dash, “Prevention of Phishing Attacks Using AI-Based Cybersecurity Awareness Training,” vol. 3, pp. 61–72, Mar. 2022, doi: 10.47893/IJSSAN.2022.1221.
2. D. Hayes and M. Kyobe, “The Adoption of Automation in Cyber Forensics,” in 2020 Conference on Information Communications Technology and Society (ICTAS), Mar. 2020, pp. 1–6. doi: 10.1109/ICTAS47918.2020.233977.
3. A. K. Abdallah and R. K. Abdallah, “Smart Solutions for Smarter Schools: Leveraging Artificial Intelligence to Revolutionize Educational Administration and Leadership,” in Encyclopedia of Information Science and Technology, Sixth Edition, IGI Global, 2025, pp. 1–14. doi: 10.4018/978-1-6684-7366-5.ch078.
4. B. Murdoch, “Privacy and artificial intelligence: challenges for protecting health information in a new era,” BMC Med. Ethics, vol. 22, no. 1, p. 122, Sep. 2021, doi: 10.1186/s12910-021-00687-3.
5. M. Rahaman, B. Chappu, N. Anwar, and P. K. Hadi, “Analysis of Attacks on Private Cloud Computing Services that Implicate Denial of Services (DoS),” vol. 4, 2022.
6. A. Alhogail and A. Alsabih, “Applying machine learning and natural language processing to detect phishing email,” Comput. Secur., vol. 110, p. 102414, Nov. 2021, doi: 10.1016/j.cose.2021.102414.
7. M. Rahaman, C.-Y. Lin, P. Pappachan, B. B. Gupta, and C.-H. Hsu, “Privacy-Centric AI and IoT Solutions for Smart Rural Farm Monitoring and Control,” Sensors, vol. 24, no. 13, Art. no. 13, Jan. 2024, doi: 10.3390/s24134157.
8. A. Basit, M. Zafar, X. Liu, A. R. Javed, Z. Jalil, and K. Kifayat, “A comprehensive survey of AI-enabled phishing attacks detection techniques,” Telecommun. Syst., vol. 76, no. 1, pp. 139–154, Jan. 2021, doi: 10.1007/s11235-020-00733-2.
9. Rahaman M (2024) Foundations of Phishing Detection Using Deep Learning: A Review of Current Techniques, Insights2TechinfoAvailable: https://insights2techinfo.com/foundations-of-phishing-detection-using-deep-learning-a-review-of-current-techniques/
10. S. Feuerriegel, J. Hartmann, C. Janiesch, and P. Zschech, “Generative AI,” Bus. Inf. Syst. Eng., vol. 66, no. 1, pp. 111–126, Feb. 2024, doi: 10.1007/s12599-023-00834-7.
11. P. Pappachan, Sreerakuvandana, and M. Rahaman, “Conceptualising the Role of Intellectual Property and Ethical Behaviour in Artificial Intelligence,” in Handbook of Research on AI and ML for Intelligent Machines and Systems, IGI Global, 2024, pp. 1–26. doi: 10.4018/978-1-6684-9999-3.ch001.
12. Gupta, B. B., & Agrawal, D. P. (2021). Security, privacy and forensics in the enterprise information systems. Enterprise Information Systems, 15(4), 445-447.
13. Shrivastava, G., & Gupta, B. B. (2014, October). An encapsulated approach of forensic model for digital investigation. In 2014 IEEE 3rd Global Conference on Consumer Electronics (GCCE) (pp. 280-284). IEEE.

Cite As

Kasa A. .S. (2024) AI in Cyber Forensics, Insights2Techinfo, pp.1

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