Browsing by Author "Henok Mulugeta (PhD)"

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    A Structured Framework for Email Forensic Investigations
    (Addis Ababa University, 2025) Biruk Bekele; Henok Mulugeta (PhD)
    Email forensics investigations become vital regarding legal, cybersecurity, and corporate challenges. However, most of the existing frameworks are suffering from inefficiency problems, data integrity, and handling such diverse data sources with complexity, considering encrypted emails and metadata. This thesis applied the Design Science Methodology to develop a structured framework that enhanced efficiency and effectiveness in email forensic investigations. These specifically deal with data quality, diversity in data management, and integrity of evidence. Among others, one key component is case management, which systemizes and keeps track of the investigation from the very outset to the last step in an appropriate manner and ensures each step is conducted methodically. The framework comprises key phases: case management, governance, identification, preservation, classification, analysis, presentation and compliance that address critical challenges such as ensuring data quality, managing diverse data sources, and maintaining evidence integrity. Case management forms the core part of the proposed framework for organizing, tracking the investigation process from start to finish in order ensuring that evidence is handled properly, and all phases are executed in a systematic manner. It integrates open-source tools, case studies of different varieties, and best practices to be relevant to different real-world scenarios. The effectiveness of the artifact can also be demonstrated in practical application, performance being measured in terms of speed of investigation, data quality, accuracy, and user satisfaction, among other metrics. This research underscores that the suggested framework decreases the time of investigation, reduces the rate of errors, increases the quality of data management, and guarantees the effective access of various data sources. This thesis contributes on both practical and theoretical levels, guiding practitioners and researchers comprehensively in the area of digital forensics to bring current email forensic investigations into a more efficient, accountable, and adaptable condition.
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    Deep Learning-Based Amharic Keyword Extraction for Open-Source Intelligence Analysis
    (Addis Ababa Univeristy, 2025-06) Alemayehu Gutema; Henok Mulugeta (PhD)
    In today's digital age, the problem of information overload has become a pressing concern, especially in the field of OSINT (Open-Source Intelligence). With vast amounts of data available on the internet, it is challenging to separate relevant and credible information from the noise. An OSINT approach involves gathering intelligence from publicly available sources. However, with the increasing volume and diversity of online content, it has become difficult to extract actionable intelligence from enormous amounts of data. Deep learning can help identify patterns in large amounts of data and automate decision-making processes. Despite these advances, a problem of information overload still exists. One approach to addressing this problem is to develop effective deep learning model to extract the relevant information. Leveraging both machine and deep learning algorithms with natural language processing (NLP) can help automatically classify and categorize information. The purpose of this study is to design deep learning model to extract intelligence from vast amount of Amharic dataset, aiming to design model for keyword extraction. Keyword extraction is the process of identifying important words or phrases that capture the essence of a given piece of text. This task is critical for many natural language processing applications, including document summarization, information retrieval, and search engine optimization. In recent years, deep learning algorithms have shown great promise in this field, largely due to their ability to learn from vast amounts of data and extract complex patterns. In this paper, we propose a novel keyword extraction approach based on deep learning methods. We will explore different algorithms, such as recurrent neural networks (RNNs) and transformer models, to learn the relevant features from the input text and predict the most salient keywords. We evaluate our proposed method on datasets containing Amharic content, and show that it outperforms state-of-the-art methods. Our results suggest that deep learning-based approaches have the potential to significantly improve keyword extraction accuracy and scalability in realworld application.