School of Information Technology and Engineering
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Item 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.Item Lightweight IOT Security With Deep Learning-Driven Biometric for Human Authentication(Addis Ababa University, 2025-02) Girma Alemu; Henock Mulugeta (PhD)Now today the number of Internet of Things (IoT) devices increases in number, as the number of IoT device increase there is also a rise in risk with these IoT devices. IoT devices have a great impact on daily lives of human being. Huge number of data can be stored, transmitted and used through IoT devices. Some of the data are very sensitive which are vulnerable to different attacks. To protect IoT devices from these attacks, different counter measures are conduct through previous researches. Conventional biometric authentication methods like possession-based (tokens) and knowledge-based (passwords/PINs) are used to tackle the problem of access control which are prone to loss, duplication, guesswork, and forgetfulness. Similarly, single-modality biometric identification—like fingerprint or facial recognition—is insufficient due to its susceptibility to spoofing attacks. When merging and comparing large amounts of biometric data, it is important to consider variations in the quantity and caliber of data sources, even though multi-biometric systems improve security. Our proposed solution to these problems combines a lightweight deep learning algorithm designed for Internet of Things devices with multimodal biometrics that are using fingerprint and face. By conducting an experiment on both training and unseen datasets, the model demonstrated good classification ability with 82.5% validation accuracy and 99.3% training accuracy. The suggested solution addresses the security issues of IoT devices through modeling and experimental validation. Through hands-on testing, we assessed the system's performance, and the outcomes showed a robust IoT security solution. In the end, the combination of deep learning algorithms and dual biometric modalities has greatly improved secure authentication procedures for IoT applications. At the end, secure authentication techniques for IoT applications have advanced significantly with the combination of deep learning algorithms and dual biometric modalities.Item Investigating Malicious Capabilities of Android Malwares that Utilize Accessibility Services(Addis Ababa University, 2025-02) Tekeste Fekadu; Fitsum Assamnew (PhD)The Android accessibility service provides a range of powerful capabilities. These include observing user actions, reading on-screen content, and executing actions on behalf of the user. Although these features are designed to enhance the user experience for individuals with disabilities, they introduce design vulnerabilities that make the accessibility service susceptible to malicious exploitation. This research investigates how Android malware leverages accessibility services for malicious purposes. By analyzing a dataset of malicious applications, we identified common patterns of accessibility service abuse and developed a machine learning-based detection approach using TinyBERT and XGBoost models. We first manually compiled a base dataset of 134 accessibility service event patterns comprising source and sink API calls. These patterns were labeled according to specific malicious functionalities: BlockAccess, ManipulateUI, and ContentEavesdrop. To address data limitations, we generated callgraph from 121 malware samples using Flow- Droid taint analysis and applied agglomerative clustering and fuzzy matching, ultimately expanding the dataset size to 1,497 patterns. Our classification experiments compared the performance of TinyBERT, a transformer-based model, and XGBoost, a gradient-boosted decision tree model, in classifying malicious functionalities. Results show TinyBERT’s outstanding performance, achieving an accuracy of 97.7% and an F1 score of 97.6% over ten-fold cross-validation, compared to XGBoost’s 90.4% accuracy and 90.0% F1 score. This study demonstrates the potential of transformer-based models in capturing sequential dependencies and contextual characteristics in API call patterns, enabling robust detection of accessibility service misuse. Our findings contribute a novel approach to detecting malicious behavior in Android malware and a valuable dataset that may aid similar research.Item Lightweight Intrusion Detection System for IoT with Improved Feature Engineering and Advanced Dynamic Quantization(Addis Ababa University, 2024-11) Semachew Fasika; Henock Mulugeta (PhD)In recent years, the proliferation of Internet of Things (IoT) devices and applications has experienced a significant surge globally, owing to their inherent advantages in enhancing both business and industrial landscapes, as well as facilitating improvements in individuals’ daily routines. Nevertheless, IoT devices are not immune to malicious attacks, which results potential negative consequences and malfunctioning of IoT devices, therefore, attack detection and classification becomes an important issue in IoT devices. This research proposes a lightweight hybrid deep learning model (DNN-BiLSTM) to detect and classify attacks in an IoT system with improved feature engineering and advanced quantization. Although leveraging hybrid deep learning model which combines DNN alongside BiLSTM, facilitates the extraction of intricate network features in a nonlinear and bidirectional manner, aiding in the identification of complex attack patterns and behaviors, its implementation on IoT devices remains challenging. To mitigate the constraints inherent in IoT devices, this research incorporates improved feature engineering, specifically Redundancy-Adjusted Logistic Mutual Information Feature Selection (RAL-MIFS) combined with a two-stage IPCA algorithm. Additionally, advanced quantization (QAT + PTDQ) techniques, alongside advanced Optuna for hyperparameter optimization, are utilized to enhance computational efficiency without compromising detection accuracy. Experimental evaluations were conducted on the CIC IDS2017 and CICIoT2023 datasets to assess the performance of a quantized DNN-BiLSTMQ model. The model demonstrated superior detection accuracy & computational efficiency compared to state-ofthe- art methods. On the CIC IDS2017 dataset, it achieved a detection accuracy of 99.73% with a model size of 25.6 KB, while on the CICIoT2023 dataset, it achieved a detection accuracy of 93.95% with a model size of 31.3 KB. These results highlight the potential of the quantized DNN-BiLSTMQ model for efficient and accurate cyber attack detection on IoT.Item 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.Item 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.Item 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.Item Reinforcement Learning Based Layer Skipping Vision Transformer for Efficient Inference(Addis Ababa University, 2023-05) Amanuel Negash; Sammy Assefa (PhD)Recent advancements in language and vision tasks owe their success largely to the Transformer architecture. However, the computational requirements of these models have limited their applicability in resource-constrained environments. To address this issue, various techniques, such as Weight pruning, have been proven effective in reducing the deployment cost of such models. Additionally, methods tailored just for transformers, such as linear self-attention and token early exiting, have shown promise in making transformers more cost-effective. Nevertheless, these techniques often come with drawbacks such as decreased performance or additional training costs. This thesis proposes a layer-skipping dynamic vision transformer (ViT) network that skips layers depending on the given input based on decisions made by a reinforcement learning agent (RL). To the best of our knowledge, this work is the first to introduce such a model that not only significantly reduces the computational demands of transformers, but also improves performance. The proposed technique is extensively tested on various model sizes and three standard benchmarking datasets: CIFAR-10, CIFAR-100, and Tiny-ImageNet. First, we show that the dynamic models improve performance when compared to their state-of-the-art static counterparts. Second, we show that in comparison to these static models, they achieve an average inference speed boost of 53% across different model sizes, datasets, and batch sizes. Similarly, the technique lowers working space memory consumption by 53%, enabling larger input processing at a time without imposing an accuracy-speed trade-off. In addition, these models achieve very high accuracy when tested in transfer learning scenarios. We then show that, although these models have high accuracy, they can be optimized even more through post-training using genetic algorithms (NSGA-II). As such, we propose the joint RL-NSGA-II optimization technique, where the GA is aware of the dynamics of skipping through the RL reward. These optimized models achieve competitive performance compared to the already high-performing dynamic models while reducing the number of layers by 33%. In real-world applications, the technique translates to an average of 53% faster throughput, reduced power consumption, or lower computing costs without loss of accuracy.Item 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.