الفهرس 
Chapter1: IntroductionOnly 14 pages are availabe for public view
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Abstract
Digital forensics has emerged as a critical field within law enforcement, playing a pivotal role in investigating cybercrimes and ensuring digital security. Concurrently, it stands as a vibrant area of research within the cybersecurity domain, continuously evolving to address emerging challenges. A fundamental aspect of digital forensics involves the analysis of internet traffic and content, essential for uncovering evidence crucial to investigations. However, the widespread adoption of encryption in internet traffic has posed a significant obstacle, rendering traditional analysis methods ineffective. In response to this challenge, a pioneering framework and methodology have been proposed in a recent thesis to extract valuable insights from encrypted traffic originating from Instant Messages (IM) and Voice Over IP (VoIP) applications.
The proposed framework offers a novel approach to address the complexities of encrypted traffic, enabling analysts to detect, classify, and analyze various encrypted activities, including typing, chatting, and media transmissions of audio and video calls. This comprehensive methodology presents a significant advancement in digital forensics, allowing investigators to extract valuable user behaviour information from encrypted traffic effectively. To validate the efficacy of the proposed framework, the study conducted rigorous testing on over 30 trace files, scrutinizing specific payload patterns. The results demonstrate the framework’s capability to detect and extract application user behaviour, providing tangible evidence for forensic investigations. Furthermore, the study highlights the feasibility of extracting valuable information from encrypted WhatsApp and Telegram traffic, showcasing the adaptability of the proposed methodology to popular IM platforms.
In addition to the challenges posed by encrypted traffic, the increasing use of Instant Messaging (IM) applications has introduced new avenues for illicit activities, necessitating specialized forensic techniques. In this context, the study proposes a network forensic approach (NFA) tailored for correlating IM calls to identify suspects’ IP addresses. By capturing and analyzing IM call data, correlating it with broader network traffic, and leveraging these correlations, law enforcement agencies can effectively identify individuals involved in illicit IM conversations. The NFA offers a superior alternative to traditional methods, which often require physical access to end-user devices, making it suitable for early crime detection and scenarios where devices may be inaccessible or compromised. Rigorous testing on real-world IM call data showcases the efficacy of the proposed method, achieving a remarkable success rate of 92.5% in identifying voice IM calls and associated participants.
In conclusion, the integration of innovative frameworks and methodologies within digital forensics underscores its vital role in modern law enforcement and cybersecurity efforts. By addressing the challenges posed by encrypted traffic and IM communications, these advancements equip investigators with powerful tools to combat cybercrime effectively. Furthermore, the continual evolution of digital forensics serves as a testament to the field’s dynamism and its critical importance in safeguarding digital ecosystems and ensuring justice in the digital age.