By: Pinaki Sahu, International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan, 0000pinaki1234.kv@gmail.com
Abstract
In the rapidly evolving field of cybersecurity, there has never been a greater need for efficient and prompt incident response. This article explores the integration of Chat-Bot technology into digital forensics as a means of expediting the investigation of cyber events. By using Chat-Bots, investigators may enhance collaboration, automate tedious tasks, and streamline communication within forensic teams. This synergy expedites the detection and mitigation of cyber threats and boosts the overall efficacy of digital forensics.
Introduction
Cyber-attacks are common in the digital era, thus prompt and accurate incident response is required. Despite its importance, traditional digital forensics frequently struggles to handle the massive amount and complexity of data connected to cyber disasters. This article explores the revolutionary possibilities of Chat-Bot-enhanced digital forensics, wherein conversational agents with intelligence are essential to accelerating the investigation. Chat-Bots facilitate investigators ability to quickly and easily browse through large datasets, interact fluidly, and make well-informed conclusions by means of natural language interfaces, automation, and real-time information retrieval. With the potential to transform cyber incident investigations and strengthen organisations against the ever-present challenges of the digital landscape, this combination represents a paradigm leap.
Chat-Bots in Digital Forensics
By utilising sophisticated natural language processing (NLP) algorithms, chatbots can comprehend and produce responses that resemble those of humans. This technology may be utilised in digital forensics to automate tedious duties, respond to investigators’ inquiries, and aid in the examination of digital evidence. Conversational interfaces provided by chat-bots facilitate improved collaboration and communication among investigators, thereby diminishing the amount of time allocated to administrative duties[1].
Key Benefits:
- Automation of Repetitive tasks: Data collecting, keyword research, and preliminary analysis are just a few of the routine tasks that chatbots can perform. This increases overall efficiency by enabling forensic analysts to concentrate on more intricate elements of the inquiry[2].
- Real-time cooperation: By offering a platform for rapid information exchange, chatbots enable real-time cooperation among investigators. In the early phases of an incident response, when quick decisions are crucial, this can be very helpful[2].
- Improved User engagement: Because chatbots are conversational in nature, they provide better user engagement and increase the accessibility of digital forensics for investigators of all skill levels. This democratisation of the research process guarantees that important discoveries are not restricted to a small group of people.
- Continuous Learning: Chatbots with machine learning (ML) algorithms included in are able to learn from user interactions over time, adjust to new attack patterns and developing forensic approaches. Over time, this flexibility makes the forensic skills stronger.
Use Cases:
- First Triage: By gathering data about the event, classifying its seriousness, and recommending early investigative steps, chatbots can help with the first triage stage.
- Data Collection: By gathering pertinent information from a variety of sources, such as logs, endpoints, and network traffic, automated chatbots can expedite the process of gathering evidence[3].
- Analysis Assistance: Chatbots can help with analysis by using machine learning (ML) to find trends, abnormalities, and possible signs of compromise in massive datasets[3].
- Reporting: Chatbots are capable of producing draught reports that include a summary of the most important discoveries and give investigators a starting point for more research and documentation.
Challenges and Considerations
While there are many benefits to chat-bot augmented digital forensics, issues including data privacy, morality, and possible biases in ML systems need to be addressed. The integrity of the inquiry process depends on finding a balance between automation and human competence.
Conclusion
A new age in cyber incident investigations is being brought in by the incorporation of chat-bot technology into digital forensics. Chat-bot augmented digital forensics has the potential to revolutionise incident response time and efficacy by automating repetitive processes, enhancing cooperation, and responding to emerging threats. It’s critical to adopt cutting-edge solutions as the cybersecurity landscape changes in order to remain ahead of cyber enemies.
References
- Gendi, M., & Munteanu, C. (2021, July). Towards a chatbot for evidence gathering on the dark web. In Proceedings of the 3rd Conference on Conversational User Interfaces (pp. 1-3).
- Iqbal, S., & Alharbi, S. A. (2019). Advancing automation in digital forensic investigations using machine learning forensics. Digital Forensic Science.
- Qadir, A. M., & Varol, A. (2020, June). The role of machine learning in digital forensics. In 2020 8th International Symposium on Digital Forensics and Security (ISDFS) (pp. 1-5). IEEE.
- occurrence in different river basins of India and blockchain technology based framework for disaster management. Journal of Cleaner Production, 312, 127737.
- Gupta, B. B., & Sheng, Q. Z. (Eds.). (2019). Machine learning for computer and cyber security: principle, algorithms, and practices. CRC Press.
- Singh, A., & Gupta, B. B. (2022). Distributed denial-of-service (DDoS) attacks and defense mechanisms in various web-enabled computing platforms: issues, challenges, and future research directions. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-43.
- Almomani, A., Alauthman, M., Shatnawi, M. T., Alweshah, M., Alrosan, A., Alomoush, W., & Gupta, B. B. (2022). Phishing website detection with semantic features based on machine learning classifiers: a comparative study. International Journal on Semantic Web and Information Systems (IJSWIS), 18(1), 1-24.
Cite As
Sahu P. (2024) Chatbots Assistance for Early DiseasChat-Bot Enhanced Digital Forensics: Accelerating Cyber Incident Investigation Processes, Insights2Techinfo, pp.1