School of Information Technology and Engineering

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A Cyber Insurance Framework for Ethiopia: Key Components and Recommendations
(Addis Ababa University, 2024-11) Ephrem Baheru; Sileshi Demesie (PhD)
The exponential rise in cyber threats such as ransomware, identity theft, and other forms of cybercrime has driven many organizations to seek cyber insurance as an extra layer of protection. Cyber insurance has emerged as a means of mitigating residual risks that remain after implementing various cyber risk mitigation strategies. Cyber-attacks in Ethiopia have been rising steadily each year, driven by a surge in digital transformation initiatives across various sectors, including government, financial institutes, and other critical infrastructure. This highlights the urgent need for cyber insurance services in the country, as it could help organizations manage financial losses and recover more effectively from cyber incidents. This study reveals that no insurance provider in the country currently offers cyber insurance services. This research envisioned promoting cyber insurance practice in Ethiopia by developing a cyber insurance framework that could be used by public and private organizations. To develop the framework, data was collected through a face-to-face interview with insurers, potential insureds, and regulatory bodies, and the data was analyzed using a qualitative approach. We also studied global best practices and trends in cyber insurance. The framework is designed to help Ethiopian organizations manage cyber risks and effectively recover from cyber incidents and reputational damage. The framework includes key components such as stakeholder engagement, insurance coverage, risk assessment and underwriting, premium calculation, risk mitigation and loss prevention, incident response and claims process, regulatory compliance, awareness and education, review and iteration, collaboration, and information sharing. A case study is used to demonstrate how a company successfully implemented the cybersecurity framework.
A Hybrid Approach to Strike a Balance of Sampling Time and Diversity in Floorplan Generation
(Addis Ababa University, 2024-05) Azmeraw Bekele; Beakal Gizachew. (PhD)
Generative models have revolutionized various industries by enabling the generation of diverse outputs, and floorplan generation is one such application. Different methods, including GANs, diffusion models, and others, have been employed for floorplan generation. However, each method faces specific challenges, such as mode collapse in GANs and sampling time in diffusion models. Efforts to mitigate these issues have led to the exploration of techniques such as regularization methods, architectural modifications, knowledge distillation, and adaptive noise schedules. However, existing methods often struggle to effectively balance both sampling time and diversity simultaneously. In response, this thesis proposes a novel hybrid approach that amalgamates GANs and diffusion models to address the dual challenges of diversity and sampling time in floorplan generation. To the best of our knowledge, this work is the first to introduce a solution that not only balances sampling time and diversity but also enhances the realism of the generated floorplans. The proposed method is trained on the RPLAN dataset and combines the advantages of GANs and diffusion models while incorporating different techniques such as regularization methods and architectural modifications to optimize our objectives. To evaluate the effect of the denoising step, we experimented with different time steps and found better diversity results at T=20. The diversity of generated floorplans was evaluated using FID across the number of rooms, and the results of our model demonstrate an average 15.5% improvement over the state-of-the-art houseDiffusion model. Additionally, it reduces the time required for generation by 41% compared to the housediffusion model. Despite these advancements, it is acknowledged that the proposed research may encounter limitations in generating non-Manhattan floorplans and when dealing with complex layouts.
A Lightweight Model for Balancing Efficiency and Precision in PEFT-Optimized Java Unit Test Generation
(Addis Ababa University, 2025-06) Sintayehu Zekarias; Beakal Gizachew (PhD)
Software testing accounts for nearly 50% of development costs while being critical for ensuring software quality, creating an urgent need for more efficient testing solutions. This work addresses this challenge by developing an innovative framework that combines Parameter-Efficient Fine-Tuning (PEFT) techniques with transformer models to automate Java unit test generation. The study systematically evaluates three PEFT approaches—Low-Rank Adaptation(LoRA), Quantized LoRA (QLoRA), and Adapters—through a rigorous methodology involving specialized assertion pretraining using the Atlas dataset (1.2M Java method-assertion pairs), PEFT optimization, targeted fine-tuning with Methods2Test (780K test cases), and comprehensive validation on the unseen Defects4J benchmark to assess cross-project generalization. Experimental results demonstrate that LoRA maintains 92% full fine-tuning effectiveness (38.12% correct test cases) while reducing GPU memory requirements by 17% and improving generation speed by 23%. QLoRA achieves even greater efficiency with 36% memory reduction, making it particularly suitable for resource-constrained environments. However, evaluation on Defects4J, assessing cross-project generalization, showed that LoRA achieved 43.1% correct assertions (compared to a full fine-tuning baseline of 46.0% on Defects4J), indicating a minor reduction in generalization alongside the efficiency gains. Despite these promising advancements, it’s important to note that our findings are currently contextualized by the Java programming language and the specific datasets employed in our experiments. These findings provide valuable insights for the implementation of AI-powered test generation in practice, highlighting both the potential of PEFT techniques to reduce testing costs and the need for further research to address the nuances of maintaining test quality across diverse projects.
A Multimodal Security Information and Event Management Solution Empowered by Deep Learning and Alert Fusion
(Addis Ababa University, 2024-11) Behailu Adugna; Sileshi Demisie (PhD)
The cybersecurity threat landscape is marked by a growing number of increasingly complex and sophisticated attacks affecting organizations across various sectors. In response, solutions like SIEM systems are essential for providing centralized threat detection, real-time analysis, and compliance support, making them integral to modern cybersecurity strategies. One of the reasons for this is that SIEM solutions collect and aggregate log data from across an organization's IT infrastructure, providing a single pane of glass for monitoring security events. And this centralized approach is essential for identifying threats that span multiple systems and environments, identifying indicative patterns of attacks such as privilege escalation and polymorphic malware, helping proactively identify signs of unusual data accesses or exfiltration before significant damage occurs. Furthermore, SIEM solutions support compliance by maintaining detailed audit logs and providing preconfigured reporting tools. However, SIEM systems usually encounter significant challenges in effectively identifying and responding to sophisticated cyberattacks. Since they rely heavily on predefined rules, even if complex correlations, and signatures, they struggle to adapt to novel attack techniques that do not match the predefined patterns. They often lack sophisticated analytics capabilities such as deep learning and behavioral analysis, which deprives them of the effectiveness at detecting advanced threats. Furthermore, they frequently produce an overwhelming volume of alerts, many of which are irrelevant or false positives. This leads to alert fatigue, causing cybersecurity analysts to become desensitized to alerts and increase the risk of overlooking critical incidents. This research proposes a multimodal architecture of SIEM designed to overcome current limitations in threat detection by integrating diverse data sources, including network traffic and event logs. The solution utilizes advanced neural networks to analyze intricate relationships within network connection features and their temporal dependencies. By further employing alert fusion, it creates a melting-pot for alerts from different sources that can provide a more comprehensive and complementary understanding of potential threats that can address the issue of false positives.
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.
Assessing Cybersecurity Readiness in Ethiopia Fintech Sector
(Addis Ababa University, 2024-10) Teklehymanot Meheret; Elefelious Getachew (PhD)
Ethiopian fintech sector brought a significant transformation on the financial transaction and payment instrument business. This change however poses concerns on various stakeholders that the country’s ability to protect the business and to mitigate the risks caused by bad actors to exploited the vulnerability. The research aim to investigate the cybersecurity readiness and preparedness of fintech and also how their practice is met the international standard through answering three research questions.Regulators and fintech companies the major stakeholders this study utilized the proposes of got the relevant information. The research identified governance, resilience and competency as a core variable to evaluates the readiness of the sector which is very much mapped with the international standard including NIST CSF, ISO/IEC 27001 and FFIEC. The study also prepared two separates the questionnaires to address the two participants current cybersecurity practice. The collected data analyzed and observed that there is clear gap and lack of readiness. The sector lacks comprehensive framework that meet the international standard according to the research findings. There was limited practice of the backup, business continuity plan and an incident response plan which impact the resilience of the sector. The other challenge this research identified was inadequate skilled cybersecurity experts and awareness level that impacted the competency of fintech ecosystem to enhance the awareness level as well as creating cybersecurity culture. The research developed a cybersecurity assessment framework that help the sector to protect their critical assets through a proper evaluation and assessment their risk and weakness. The proposed framework subjected to went through a validation process to make sure the framework relevance to the challenged identified in the research and met the basic global standard. The research concludes with valuable recommendations and consideration to enhance cybersecurity practice, collaboration and developed tailored cybersecurity framework for continuous improvement..
Attribution Methods for Explainability of Predictive and Deep Generative Diffusion Models
(Addis Ababa University, 2025-06) Debela Desalegen; Beakal Gizachew (PhD)
As machine learning models grow in complexity and their deployment in high-stakes domains becomes more common, the demand for transparent and faithful explainability methods has become increasingly urgent. However, most existing attribution techniques remain fragmented, targeting either predictive or generative models, and lack a hybrid approach that offers coherent interpretability across both domains. While predictive modeling faces challenges such as faithfulness, sparsity, stability, and reliability, generative diffusion models introduce additional complexity due to their temporal dynamics, tokento- region interactions, and diverse architectural designs. This work presents a hybrid attribution method designed to improve explainability for both predictive black-box models and generative diffusion models. We propose two novel methods: FIFA (Firefly-Inspired Feature Attribution), an optimization-based approach for sparse and faithful attribution in tabular models; and DiffuSAGE (Diffusion Shapley Attribution with Gradient Explanations), a temporally and spatially grounded method that attributes generated image content to individual prompt tokens using Aumann-Shapley values, Integrated Gradients, and cross-attention maps. FIFA applied to the Random Forest, XGBoost, CatBoost, and TabNet models in three benchmark datasets: Adult Income, Breast Cancer, and Diabetes, outperforming SHAP and LIME in key metrics: +6.24% sparsity, +9.15% Insertion AUC,-8.65% Deletion AUC, and +75% stability. DiffuSAGE evaluated on Stable Diffusion v1.5 trained on the LAION-5B dataset, yielding a 12.4% improvement in Insertion AUC and a 9.1% reduction in Deletion AUC compared to DF-RISE and DF-CAM. A qualitative user study further validated DiffuSAGE’s alignment with human perception. Overall, these contributions establish the first hybrid attribution methods for both predictive and\ generative models, addressing fundamental limitations in current XAI approaches and enabling more interpretable, robust, and human-aligned AI systems.
Collatz Sequence-Based Weight Initialization for Enhanced Convergence and Gradient Stability in Neural Networks
(Addis Ababa University, 2025-06) Zehara Eshetu; Beakal Gizachew (PhD); Adane Letta (PhD)
Deep neural networks have achieved state-of-the-art performance in tasks ranging from image classification to regression. However, their training dynamics remain highly sensitive to weight initialization. This is a fundamental factor that influences both convergence speed and model performance. Traditional initialization methods such as Xavier and He rely on fixed statistical distributions and often underperform when applied across diverse architectures and datasets. This study introduces Collatz Sequence-Based Weight Initialization, a novel deterministic approach that leverages the structured chaos of Collatz sequences to generate initial weights. CSB applies systematic transformations and scaling strategies to improve gradient flow and enhance training stability. It is evaluated against seven baseline initialization techniques using a CNN on the CIFAR-10 dataset and an MLP on the California Housing dataset. Results show that CSB consistently outperforms conventional methods in both convergence speed and final performance. Specifically, CSB achieves up to 55.03% faster convergence than Xavier and 18.49% faster than He on a 1,000-sample subset, and maintains a 20.64% speed advantage over Xavier on the full CIFAR-10 dataset. On the MLP, CSB shows a 58.12% improvement in convergence speed over He. Beyond convergence, CSB achieves a test accuracy of 78.12% on CIFAR-10, outperforming Xavier by 1.53% and He by 1.34%. On the California Housing dataset, CSB attains an R score of 0.7888, marking a 2.35% improvement over Xavier. Gradient analysis reveals that CSB-initialized networks maintain balanced L2 norms across layers, effectively reducing vanishing and exploding gradient issues. This stability contributes to more reliable training dynamics and improved generalization.However, this study is limited by its focus on shallow architectures and lacks a robustness analysis across diverse hyperparameter settings.
Cybersecurity Governance Framework for Ethiopian National Identification Program
(Addis Ababa University, 2025-06) Selwa Nurye; Henock Mulugeta (PhD)
Ethiopia launched its digital transformation strategy, Digital Ethiopia 2025, in 2020 to build a sustainable digital economy. One of the key priorities of this strategy is to implement digital identification for all citizens and residents. Digitalizing government services and businesses requires a secure, electronic representation of individuals and entities, proving their identity and reliability during transactions or interactions, both online and in person. However, the increasing interconnectivity of the digital world poses ongoing cybersecurity challenges. Digital IDs, while crucial to enabling the digital economy, are vulnerable to the same cyber risks that affect other widely used digital technologies. Although global efforts to develop national digital identity systems aim to enhance security and convenience, they also face significant technical, ethical, and security challenges. These systems are vital for achieving the Sustainable Development Goals (SDGs), but they often grapple with issues such as privacy, data management, enrollment processes, and costs. As a result, effective cybersecurity governance is essential. The cybersecurity governance activities of the body responsible for overseeing these programs must align closely with the strategy’s objectives. This study employed a qualitative research methodology, including in-depth interviews and document analysis, to collect the necessary data. Thematic data analysis was used to process the data, leading to conclusions from which recommendations were derived. Based on the findings and insights from reviewed literatures, we developed a cybersecurity governance framework that was validated through hypothetical cyber incident scenarios to show that the proposed framework mitigate those incidents. Besides, key performance indicators were prepared to assess the effectiveness of the framework in real-world scenarios.
Cybersecurity Incident Management Framework for Smart Grid Systems in Ethiopia
(Addis Ababa University, 2024-06) Getinet Admassu; Henock Mulugeta (ጵህD)
Merging OT and IT into smart grid systems brought along new advantages. Smart grids will be able to use this amalgamation to manage energy generation and transmission with minimal loss of energy, a factor that results in high efficiency. Besides that, integrating IT and OT into the smart grid presents real-time infrastructure management monitoring. On the other hand, this digital change subjected smart grids to many cybersecurity threats. This will be achieved by developing and implementing stable cybersecurity incident management systems to secure key infrastructures. Based on evidence from existing literature and expert judgments, this paper enumerates the principal challenges power utilities face in managing cybersecurity incidents. Then, it outlines a comprehensive cybersecurity incident management framework. This framework will, hence, enable power utilities to take on an active role and deal with relevant powers regarding cybersecurity incidents. Also, the model ensures that cybersecurity, concerning all strategic, engineering, procurement, construction, and operational aspects and involving all parties and resources concerned, is put together systematically. The underlying design science qualitative approach facilitated the development of this framework. It organizes sophisticated threat detection techniques and counter-threat strategies and correlates with Risk Management, Threat Analysis, Security Controls, Operational Models, and Management. They also involve real-time network traffic and system log monitoring, anomaly detection algorithms, intrusion detection, and prevention systems. Power utilities will significantly improve the ability to effectively detect and respond to cybersecurity-related events. The following threat scenarios, including organized DDoS and ransomware attacks as a taxonomy against the various components of the proposed framework, show how these smart grid technologies mentioned above can be used to develop effective solutions in response to cyber security incidents. It is indeed a systematic framework; it gives good advice. The recommendations will target particular challenge areas within the electric power industry and underpin its cybersecurity posture, with a view that our critical energy infrastructure will be reliable and capable of being counted upon in grace. This research encourages sustainable development and social welfare by resilience in cybersecurity for smart grid systems.
Cybersecurity Maturity Assessment Framework: The Case of Ethiopian Banks
(Addis Ababa University, 2024-10) Yafet Ashebir; Elefelious Getachew (PhD)
As the banking sector becomes a key player in globalized cyberspace with increasing reliance on digital services, it is prone to a wide range of emerging cybersecurity risks. As cybersecurity can only be achieved through a well-organized set of controls; existing cybersecurity maturity frameworks, while comprehensive and vague, fail to address the unique cybersecurity challenges faced by Ethiopian banks. The literature review discovered that no study has proposed a cybersecurity maturity assessment framework for the Ethiopian banking sector. This study aims to propose a customized framework by reviewing multiple cybersecurity maturity assessment frameworks to identify their weaknesses and strengths. After a thorough assessment, we have identified the major limitations of the existing frameworks and they are not easy to understand, expensive to implement, require intensive and equipped human resources, and are not tailored to the banking sectors to fix operational challenges. Moreover, to assess existing cybersecurity maturity frameworks in banks, data was collected from 9 selected governmental and private banks, and a thematic analysis approach was utilized for the qualitative data collected. As the findings reveal, all selected banks don’t have a proper cybersecurity maturity assessment framework as well as improper adoption of international standards. To address identified weaknesses, a customized cybersecurity maturity assessment framework is proposed to enable banks to identify their security posture and manage their security risks. The proposed framework comprises various components such as regulatory requirements, personal data protection, supply chain security, awareness and culture development, cyber governance, cyber risk management, business continuity and disaster recovery, incident response plan, information sharing, and collaboration, and incorporates international best practices like General Data Protection Regulation (GDPR). To evaluate the framework expert review has been done as the framework contributes to both academic literature and industry practice by providing a customized framework for banks to assess and improve their cybersecurity maturity.
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.
Enhancing Neural Machine Translation Through Incorporation of Unsupervised Language Understanding and Generation Techniques: The Case of English-Afaan Oromo Translation
(2024-05) Chala Bekabil; Fantahun Bogale (PhD)
Breaking down language barriers is a paramount pursuit in the realm of Artificial Intelligence. Machine Translation (MT), a domain within Natural Language Processing (NLP), holds the potential to bridge linguistic gaps and foster global communication. Enhancing cross-cultural communication through MT will be realized only if we succeed in developing accurate and adaptable techniques which in turn demands adequate availability of linguistic resources. Unluckily, under-resourced languages face challenges due to limited linguistic resources and sparse parallel data. Previous studies tried to solve this problem by using monolingual pre-training techniques. However, such studies solely rely on either Language Understanding (LU) or Language Generation (LG) techniques resulting in skewed translation. This study aims to enhance translation outcomes beyond the capabilities of previous studies by marrying the concepts of LU and LG and hence boosting the quality of MT in both directions. Our proposed model, the BERT-GPT incorporated Transformer, combines SOTA language models, BERT and GPT, trained on monolingual data into the original Transformer model and demonstrates substantial improvements. Experimental results shows that translation quality leaps forward, as evidenced by a significant increase in the BLEU score reaching 42.09, from the baseline score of 35.75 for English to Afaan Oromo translation, and 44.51 from the baseline score of 40.35 for Afaan Oromo to English translation on test dataset. Notably, our model unveils a deep understanding of Afaan Oromo’s linguistic nuances, resulting in translations that are precise, contextually appropriate, and faithful to the original intent. By leveraging the power of unsupervised pre-training and incorporation of unsupervised LU and LG techniques to the transformer model, we pave the way for enhanced cross-cultural communication, advanced understanding and inclusivity in our interconnected world.
Ensemble Learning with Attention and Audio for Robust Video Classification
(Addis Ababa University, 2025-06) Dereje Tadesse; Beakal Gizachew (PhD)
The classification of video scenes is a fundamental task for many applications, such as content recommendation, indexing, and monitoring broadcasts. Current methods often depend on annotation-dependent object detection models, restricting their generalizability when working with different types of broadcast content, particularly cases where visual clues like logos or brands may not have clear definition or presence. This thesis is intended to address the problems associated with current methods through describing a two-stage classification framework that integrates both recognized and unheard information to improve accuracy and robustness of classification. The first stage utilizes a detection model based on pretrained models of object detection and enhanced spatial attention to detect physical visual markers (such as program logo or branded intro sequences) in video program content. However, individual visual indicators are sometimes not robust enough to add confidence, especially in content such as situational comedies where logos do not exist. The second stage describes a twostaged, early fusion ensemble presentation of convolutional neural network-based visual features and recurrent neural network-based audio features. The two modes each use some complementary properties, thus could be used for more robust classification. Experiments were completed with a dataset of approximately 19 hours of content from 13 TV programs across three channels, all focused on intro, credit, and outro segments. The visual-only model achieved 96.83% accuracy, while the audio-only model achieved 90.91%. The proposed early fusion ensemble method achieved 94.13% accuracy and revealed more robustness in difficult situations when quality of visual data was low or ambiguous. Ablation studies contrasting model performance with different ensemble methods confirmed the greater utility of early fusion and its capturing of cross-modal interactions. The system is also designed to be computationally efficient allowing for operationalization in broadcast media settings. This work, while also demonstrating methodical video classification ability, fills a significant gap for scalable and generalizable video classification through the integration of multimodal learning, especially with large amounts of uncontrollable annotations which has previously been a hurdle to more typical models.
Ensemble Learning with Attention and Audio for Robust Video Classification
(Addis Ababa University, 2025-06) Dereje Tadesse; Beakal Gizachew (PhD)
The classification of video scenes is a fundamental task for many applications, such as content recommendation, indexing, and monitoring broadcasts. Current methods often depend on annotation-dependent object detection models, restricting their generalizability when working with different types of broadcast content, particularly cases where visual clues like logos or brands may not have clear definition or presence. This thesis is intended to address the problems associated with current methods through describing a two-stage classification framework that integrates both recognized and unheard information to improve accuracy and robustness of classification. The first stage utilizes a detection model based on pretrained models of object detection and enhanced spatial attention to detect physical visual markers (such as program logo or branded intro sequences) in video program content. However, individual visual indicators are sometimes not robust enough to add confidence, especially in content such as situational comedies where logos do not exist. The second stage describes a twostaged, early fusion ensemble presentation of convolutional neural network-based visual features and recurrent neural network-based audio features. The two modes each use some complementary properties, thus could be used for more robust classification. Experiments were completed with a dataset of approximately 19 hours of content from 13 TV programs across three channels, all focused on intro, credit, and outro segments. The visual-only model achieved 96.83% accuracy, while the audio-only model achieved 90.91%. The proposed early fusion ensemble method achieved 94.13% accuracy and revealed more robustness in difficult situations when quality of visual data was low or ambiguous. Ablation studies contrasting model performance with different ensemble methods confirmed the greater utility of early fusion and its capturing of cross-modal interactions. The system is also designed to be computationally efficient allowing for operationalization in broadcast media settings. This work, while also demonstrating methodical video classification ability, fills a significant gap for scalable and generalizable video classification through the integration of multimodal learning, especially with large amounts of uncontrollable annotations which has previously been a hurdle to more typical models.
Framework for Identifying Forensic Artifacts from Ride-hailing Android Applications
(Addis Ababa University, 2025-03) Munir Kemal; Fitsum Assamnew (PhD)
Different services are offered through our mobile devices as a result of the increasing usage of smartphones in this world. One of these services is the ride-hailing service in which the taxi transportation service is managed from a common operation center with the help of driver and passenger applications that the end users have installed on their smartphones. In our country, Ethiopia, there are many companies that offer this service, such as Ride, Feres, ZayRide, Seregela, Safe, Taxiye, and others. Today, many crimes such as theft, murder, etc. are committed against drivers or riders while working or using this transportation service in Ethiopia. Current research focuses mainly on the forensic investigation of social networks and banking applications. A research by K. Kiptoo proposed a forensic investigation framework to identify forensic artifacts from Android on-demand ride applications such as Uber, Little and Bolt that operate in Kenya. In this research, we propose a forensic framework by customizing the existing framework proposed by K. Kiptoo to enhance the identification of forensic artifacts from Android based ride-hailing applications after experimentation with ride-hailing applications such as Ride, Zayride and Feres. The proposed forensic framework for ride-hailing applications involves six phases: Collection, Setting up and Configuration, Extraction and Preservation, Application Database Location, Examination and Analysis, and finally Reporting. While experimenting, we were able to recover valuable artifacts such as passenger profile information, passenger device details, location data, time information, and driver-related data from ride-hailing applications, which are crucial digital evidence in the investigation of digital crimes. This research also investigated the level of role and the challenges of using digital forensic evidence to close a criminal case by Ethiopian law enforcement agencies using a specially designed questionnaire distributed to them. The research findings show that even though its role as evidence usage is increasing, we were able to identify major issues such as legal and procedural inconsistencies, lack of expertise, resource limitation, and lack of clear forensic standards that may hinder the use of digital evidence obtained from digital systems such as ride-hailing applications in a digital world full of complex digital crimes.
Framework for PKI Implementation: Optimizing Project Management in Ethiopia
(Addis Ababa University, 2024-09) Binyam Ayele; Henock Mulugeta (PhD)
In today's increasingly digital world, the security of online communications and transactions is paramount. Public Key Infrastructure (PKI) has emerged as a cornerstone technology for ensuring secure, authenticated, and confidential digital interactions. However, the implementation of PKI projects remains challenging due to its inherent complexities, including certificate management, key distribution, and system integration, National legal framework contradictions & Limitations, lack of interoperability. The lack of a standardized implementation framework further exacerbates these challenges, leading to inconsistent and often flawed deployments that fail to leverage the full potential of PKI. This study investigates the importance of optimizing a PKI Project implementation framework that support the establishment of a national or organizational PKI project at national or organizational level by developing a comprehensive framework that mitigate PKI project implementation challenges. The study seeks to address the critical need for a comprehensive PKI Project Implementation Framework that can guide organizations in navigating the complexities of PKI deployment. The problem under investigation is the absence of standardized and generic framework and best practices for PKI implementation, which has resulted in varied levels of security and effectiveness across different sectors. The study aims to develop a framework that is adaptable to diverse organizational contexts, ensuring that PKI systems are implemented in a manner that is both secure and scalable. To achieve this goal, a systematic literature review (SLR) methodology will be employed as the primary research method. The SLR will systematically identify, evaluate, and synthesize existing research on PKI implementation, focusing on the challenges, best practices, and potential solutions proposed in the literature. By analyzing a wide range of studies, the SLR will provide a comprehensive understanding of the current state of PKI implementation and identify gaps that the proposed framework can address. This method will ensure a rigorous and evidence-based approach to the development of the PKI Project Implementation Framework. This research focused on developing a PKI implementation framework that assist PKI project management. A case study and Key Performance Indictor (KPI) is incorporated to evaluate the proposed framework. As a direct outcome of this study, stakeholders who have plans to implement PKI within Ethiopia or other country will obtain a proactive understanding of potential implementation considerations that should be taken.
Identification and Classification of Illegal Dark Web Activities in East Africa Region
(Addis Ababa University, 2024-08) Tariku Eshetu; Fitsum Assamnew (PhD)
Online criminal activity manifests in various forms across the Surface, Deep, and Dark Web layers of the Internet. The darknet environment is notorious for various illegal activities, including financial crimes, hacking, recruitment for terrorism and extremism, child pornography, human organ trafficking, drug trafficking, and illegal arms trading. Law enforcement faces significant challenges in identifying specific criminal websites due to the ineffectiveness of traditional investigative techniques. In East Africa, the growth of technology has created economic and social opportunities, but it has also led to increased internet penetration and connectivity, making the region an attractive target for cybercriminals. Compounding the issue are the insufficient readiness of security organizations and a lack of user awareness, which further facilitate cybercrime. This thesis investigates the landscape of cybercrime on the Dark Web, focusing specifically on East African Internet Protocol (IP) address spaces, an area that has been largely under-researched in the existing literature. This research seeks to address a pronounced gap in knowledge regarding the types of illegal activities and associated protocols on the Dark Web, particularly given existing studies’ inadequacies in contextualizing research within East African socio-political frameworks. The research pivots around two key questions: (1) What types of protocols operate through the Dark Web in East African IP address spaces? and (2) What illegal activities are conducted through these protocols? The objectives of this study are multifaceted, aiming to develop a robust methodology for data collection and analysis from Tor exit nodes within the East African, classify the prevalent communication protocols, and categorize the diverse illegal activities identified. Through thorough examination of Tor network traffic, the study reveals crucial patterns, including a dominance of TCP and TLS protocols, smaller percentages using other protocols such as DATA, Bitcoin, HTTP, DNS, and SSH and with illicit activities significantly associated with drug, violence, and software piracy. The findings underscore the pressing need for tailored law enforcement strategies, informed policymaking, and collaborative regional approaches to manage the escalating threats. By innovatively integrating advanced data analytics techniques and multithreaded computing, this thesis provides a unique framework for ongoing cybercrime analysis, enhancing situational awareness for stakeholders and facilitating more effective monitoring of the Dark Web. The implications of this research extend beyond academic inquiry; it offers practical resources for law enforcement agencies, policymakers, and researchers in mitigating cyber threats, thereby contributing to a safer digital environment in East Africa.
Improving Knowledge Distillation For Smaller Networks Via Reducing Regularization
(Addis Ababa University, 2023-05) Mubarek Mohammed; Beakal Gizachew(PhD)
Knowledge Distillation (KD) is one of the numerous model compression methods that help reduce the size of models to address problems that come with large models. In KD a bigger model termed the teacher, transfers its knowledge, referred to as the Dark Knowledge (DK), to a smaller network usually termed the student network. The key part of the mechanism is a Distillation Loss added in the loss term that plays adual role: one as a regularizer and one as a carrier of the categorical information to be transferred from the teacher to the student which is sometimes termed DK [1]. It is known that the conventional KD does not produce high compression rates. Existing works focus on improving the general mechanism of KD and neglect the strong regularization entangled with the DK in the KD mechanism. The impact of reducing the regularization effect that comes entangled with DK remained unexplored. This research proposes a novel approach, which we termed Dark Knowledge Pruning (DKP), to lower this regularization effect in the form of a newly added term on the Distillation Loss. Experiments done across representative and benchmark datasets and models demonstrate the effectiveness of the proposed mechanism. We find that it can help improve the performance of a student against the baseline KD even in extreme compression, a phenomenon normally considered not well suited for KD. An increment of 3% is achieved in performance with a less regularized network on CIFAR 10 dataset with ResNet teacher and student models against the baseline KD. It also improves the current reported smallest result on ResNET 8 on the CIFAR-100 dataset from 61.82% to 62.4%. To the best of our knowledge, we are also the first to study the effect of reducing the regularizing nature of the distillation loss in KD when distilling into very small students. Beyond bridging Pruning and KD in an entirely new way, the proposed approach improves the understanding of knowledge transfer, helps achieve better performance out of very small students via KD, and poses questions for further research in the areas of model efficiency and knowledge transfer. Furthermore, it is model agnostic and showed interesting properties, and can potentially be extended for other interesting research such as quantifying DK.
Integrating Hierarchical Attention and Context-Aware Embedding For Improved Word Sense Disambiguation Performance Using BiLSTM Model
(Addis Ababa University, 2024-06) Robbel Habtamu; Beakal Gizachew (PhD)
Word Sense Disambiguation is a fundamental task in natural language processing, aiming to determine the correct sense of a word based on its context. Word sense ambiguity, such as polysomy, and semantic ambiguity poses significant challenges in the task of WSD. Recent advancements in research have focused on utilizing deep contextual models to address these challenges. However, despite this positive progress, semantical ambiguity remains a challenge, especially when dealing with polysomy words. This research introduces a new approach that integrates hierarchical attention mechanisms and BERT embeddings to enhance WSD accuracy. Our model, incorporating both local and global attention, demonstrates significant improvements in accuracy, particularly in complex sentence structures. To the best of our knowledge, our model is the first to incorporate hierarchical attention mechanisms integrated with contextual embedding. This integration enhances the model’s performance, especially when combined with the contextual model BERT as word embeddings. Through extensive experimentation, we demonstrate the effectiveness of our proposed model. Our research highlights several key points. First, we showcase the effectiveness of hierarchical attention and contextual embeddings for WSD. Second, we adapted the model to Amharic word sense disambiguation, demonstrating strong performance. Despite the lack of a standard benchmark dataset for Amharic WSD, our model performs 92.4% Accuracy on a self-prepared dataset. Third, our findings emphasize the importance of linguistic features in capturing relevant contextual information for WSD. We also note that Part-of-Speech (POS) tagging has a less significant impact on our English data, while word embeddings significantly impact model performance. Furthermore, applying local and global attention leads to better results, with local attention at the word level showing promising results. Overall, our model achieves state-of-the-art results in WSD within the same framework. Our results demonstrate a significant improvement of 1.8% to 2.9% F1 score over baseline models. We also achieve state-of-the-art performance on the Italian language by achieving 0.5% to 0.7% F1 score over baseline papers. These findings underscore the importance of considering contextual information in WSD, paving the way for more sophisticated and context-aware natural language processing systems.

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