http://hdl.handle.net/123456789/10362 2026-08-05T14:36:03Z http://hdl.handle.net/123456789/21538 Self-Healing And Lightweight Intrusion Detection For Iot-Based Systems Mahawish, 02-200182-006 The rapid growth of Internet of Things (IoT) has revolutionized modern technology, enabling seamless connectivity across diverse domains. However, resourceconstrained IoT devices are highly vulnerable to cyber threats, particularly Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks. Such attacks can severely compromise the reliability and functionality of IoT networks, necessitating robust, resource-efcient, and adaptive intrusion detection mechanisms. Traditional Intrusion Detection Systems (IDSs) often fail to meet the unique demands of IoT environments due to their high computational overhead and inability to adapt dynamically to emerging threats. Addressing these challenges requires an innovative approach that balances lightweight design with high detection accuracy and resilience. The frst part of this research proposes an Ensemble Lightweight Intrusion Detection System (ELIDS) approach, which combines the capabilities of seven distinct flter-based Feature Selection (FS) methods to identify the most relevant features in classifying normal and DoS/DDoS attack packets. In the proposed ELIDS, each FS method generates a ranked list of features along with their corresponding scores. These lists are subsequently aggregated, resulting in a consolidated fnal list that includes the reduced set of features. The selected features are further utilized to train six Machine Learning (ML) algorithms, designing lightweight ML-enabled IDS. The proposed ELIDS is evaluated for in-domain and cross-domain testing scenarios. The results reveal that incorporating an ensemble FS approach improves detection accuracy also optimizes computational resources. Moreover, ELIDS outperforms conventional FS methods, especially over cross-domain testing scenarios. The second part of this research presents a Self-Healing for Internet of Thing (SHviii IoT), designed to enhance the performance of ML-enabled IDS models, especially when a decline in their effectiveness is observed during cross-domain testing. In this framework, the health monitor continuously checks the behavior of simulated IoT devices and generates a danger signal when it detects any deviation from normal operating patterns caused by a DoS/DDoS attack. Once the danger signal is triggered, the proposed SHIoT initiates its healing mechanism, which activates defensive measures to counter DoS/DDoS attacks. The framework is assessed using performance metrics and obtained results demonstrate a notable enhancement in the performance of ML models, which retain both previous and newly acquired knowledge to identify both existing and emerging DoS/DDoS attacks. Finally, the third part of this research integrates the Self-Healing and Ensemble-based Lightweight IDS (SHIoT-ELIDS). The SHIoT-ELIDS is evaluated using nine unseen datasets, achieving a peak accuracy of 99.9% while employing Random Forest as the learning algorithm. Additionally, the proposed SHIoT-ELIDS performs efciently in terms of resource utilization, where testing a single packet takes only 0.0050 msec, demonstrating its appropriateness for real-time detection environments. The CPU usage is around 0.0014% per packet, ensuring the system imposes minimal load on the underlying hardware. Additionally, memory consumption remains low at 0.1456 MB per packet, while the fnal classifcation model size is of 697 KB, making it ideal for deployment on devices with limited processing and storage capacity, such as IoT devices. Overall, SHIoT-ELIDS can improve the security posture of IoT networks, providing a scalable and resource-efcient defense against evolving cyber threats Supervised by Dr. Osama Rehman 2025-01-01T00:00:00Z http://hdl.handle.net/123456789/21539 An Agile Approach for Inquiry-Based Learning in Ubiquitous Environment Bushra Fazal Khan, 02-200181-009 Education is an evolving field where challenges are constantly changing of content and learners' needs. Thus, there was a shift in pedagogy from teacher-centric to student-centric learning with the advancement of technology. In recent times, the concept of smart classrooms or ubiquitous learning (u-learning) environment has been prevalent in the learning domain as it increases the accessibility of resources and learning activities anywhere, anytime using smart devices and internet. Also, enhance engagement by creating interactive, engaging, and enjoyable learning experience. Inquiry-based learning (IBL) is an effective instructional strategy that can be in the form of a problem or task used for triggering student engagement and enhancing students’ knowledge by introducing inquiring activities for fostering critical thinking, problem-solving, and student engagement. Also, ubiquitous learning environments integrate real-world and digital-world resources and learning scenarios, thus providing a new opportunity for implementing technologyenhanced inquiry-based activities to enrich student learning experience. But, implementing IBL in a ubiquitous learning environment within school settings presents its own set of challenges, especially in structuring and controlling collaboration effectively. Collaborative learning is a process of sharing knowledge and constructing meaning by complete involvement of all the peers discuss what they know and gain more knowledge with the help of other peers. Agile methodologies are very successful in the software engineering domain for collaboration professional development environment. These methodologies have been used for quite some time to teach software engineering concepts and agile development using a projectdriven approach in computer science. Due to their incremental, iterative, adaptive, and collaborative nature, they have gained popularity in educational domains. There is a lack in literature about the use of agile methods in IBL in ubiquitous environment. Moreover, there is also the opportunity to develop agile-based teaching approaches for other disciplines other than computing, particularly for school students.VIII The significance of this study is about the developing and evaluating the ScrumBan Ubiquitous Inquiry Framework (SBUIF) through the uASK application for collaborative IBL amongst school students. For evaluation purposes, computer-supported collaborative learning (CSCL) affordances, along with micro and meso levels of the M3 evaluation framework, have been applied. An experiment was conducted to test the effectiveness of the uASK application against Trello application. The sample involved 205 seventh-grade students. Results showed that the uASK learners scored better than those using Trello. At the micro-level, student satisfaction score by uASK users was 77.14% in comparison to Trello users which was 54.12%. It showed significant improvement and revealing better results in technology, communication, and learning performance. The meso-level measures of students learning performance revealed that uASK users had better learning outcomes then Trello (68.88% vs 58.13% for post-test improvement). Also more efficient use of time (34.7% faster). Thus, the results showed that uASK users were more engaged, satisfied, and enjoyed uASK more than the other users. The study further recommends that uASK is more effective than Trello in promoting collaborative learning activities inquiring approach in any place. The study demonstrates that the use of such framework and application based on SBUIF can support agile based collaborative inquiry learning at school level, thereby advancing knowledge in the fields of agile and inquiry-based education. Furthermore, by implementing the framework in a real-world classroom setting, it highlights its practical value, offering guidance for educators in designing similar applications to support future teaching and learning Supervised by Dr. Sohaib Ahmed 2025-01-01T00:00:00Z http://hdl.handle.net/123456789/16916 Applications Of Value-Centric Regression Testing For Software Applications Farrukh Shahzad Ahmed, 01-281162-001 In the current era, businesses are Information Technology (IT) reliant, but most companies are deteriorating to maximize the value of their IT initiatives to their businesses. IT professionals do not know the value of distinct software features to the business. Likewise, they do not know the business value of diverse software quality attributes to the business. Therefore, they prioritize their project tasks based on their perceptions without considering formally measured business value. Ignoring value in software processes, practices, and artifacts is a value-neutral approach. In regression testing, software testers cannot re-execute all the test cases to find out the ripple effects of the changes due to time and budget constraints. No company can afford exhaustive regression testing in rapidly growing applications. Therefore, software testing professionals need a way through which they can prioritize their test cases for regression testing to uncover maximum bugs and side effects by utilizing minimum time and cost. Test Case Prioritization (TCP) is one of the processes to address this challenge. TCP is a smart way for regression testing to handle testing resource constraints. The main advantage of TCP is to save time through the prioritization of critical tests earlier. Current TCP techniques can be categorized as Value-Neutral (VN) and Value-Based (VB) approaches. In a VB approach, the cost of test cases and severity of faults are considered while, in a VN approach these are not considered. The VN approach is dominant over VB approach, and it assumes that all test cases have identical costs and that all software faults have same severity. But this notion seldom holds in practice. Therefore, VN TCP techniques are likely to deliver unreliable results. To fill this gap, focus should be shifted from VN to VB test prioritization. Presently, limited research work is done in a VB approach. To address this issue, a Systematic Literature Review (SLR) of VB TCP techniques is performed, and its results are presented in this thesis. Its purpose is to combine the overall knowledge related to VB TCP techniques and to highlight some open research issues in this domain. The literature review yields that value-orientation is vital in the TCP process to achieve its targeted goals and this is a potential area for further research. Many TCP techniques are available, and their performance is usually measured through a metric Average Percentage of Fault Detection (APFD). This metric is value-neutral because it only works well when all test cases have identical costs, and all faults have equal severity. Using APFD for performance evaluation of test case orders where test case cost or fault severity varies is prone to produce false results. Therefore, using the right metric for performance evaluation of TCP techniques is very important to get reliable and correct results. To the best of the author’s knowledge, there is no formal technique available to quantify business value based on which test cases can be prioritized. To overcome this problem, a business value quantification model has been proposed in this work to estimate fault severities and test case cost. The proposed model supports the business value measurement of software requirements. We use the term software features as functional requirements and software quality attributes as nonfunctional requirements. The business value calculation of software features and quality attributes is based on three factors client priority, feature complexity, and feature usage. To compute the value of client priority, the proposed model utilizes five business success factors including profitability, productivity, operational efficiency, customer satisfaction, and time to market. Software fault severity and test case cost are estimated through the business value of requirements because different test cases and faults are directly associated with some requirements. Business value has been incorporated into the TCP process through the proposed model. The model is validated through two working examples. Based on the proposed model, two value-based TCP techniques have been introduced in this thesis using Genetic Algorithms (GA). These techniques are Value-Cognizant Fault Detection-Based TCP (VCFDB-TCP) and Value-Cognizant Requirements Coverage-Based TCP (VCRCB-TCP). Two novel value-based performance evaluation metrics are also introduced for value-based TCP including the APFDv and Average Percentage of Requirements Coverage per value (APRCv). Two case studies are performed to validate proposed techniques and performance evaluation metrics quantitatively. A statistical analysis of the results is performed by a statistical test. The statistical results reveal that the proposed approaches provide significantly better results than traditional value-neutral TCP techniques. Supervised by Dr, Tamim Ahmed Khan 2023-01-01T00:00:00Z http://hdl.handle.net/123456789/16918 Services Provisioning By Using Intelligent Learning For Long Range Wide Area Network (LoRaWAN) Zulfiqar Ali, 01-281151-001 The exponential growth of Internet of Things (IoT) services and ecosystems recently emerged with a novel type of communication network known as Low Power Wide Area Network (LPWAN). This standard enables low-power long-range communication at a low data rate. Besides, Long Range Wide Area Network (LoRaWAN), is a recent standard of LPWAN that incorporates LoRa Wireless into a networked infrastructure. Consequently, Quality of Service (QoS) efficient service provisioning is a major challenge due to the highly dense network environment, the limited battery lifetime of LoRa-based End Devices (EDs), spectrum coverage, and data collisions. Intelligent and efficient service provisioning is a dire need of a network to streamline and address these problems. This study proposes a novel and Intelligent Learning (IL) based framework for efficient service provisioning without placing any extra burden on the network and its resource constraint LoRaWAN EDs. The proposed framework intelligently learns from varied underlying potential parameters such as real-time Packet Error Rate, data throughput, data delay, data collisions, and energy consumption to improve the overall network performance. The proposed framework is extensively simulated and evaluated with current state-of-the-art benchmark algorithms using standard and extended evaluation metrics. Slotted Aloha with Markov chain model mitigates collision and enhances the performance of LoRaWAN by 38% in terms of data throughput. Results of Slotted Aloha with Markov chain model are compared with Pure Aloha used by conventional LoRaWAN. Adaptive Scheduling Algorithm (ASA) with Gaussian Mixture Model (GMM) is extensively compared with conventional LoRaWAN and Dynamic PST (Priority Scheduling Technique). ASA with GMM enhanced performance in terms of delay by 5% in the LoRaWAN environment. Dynamic Reinforcement Learning Resource Allocation significantly reduced the energy consumption of EDs by 20% measured in Joules. Results of Dynamic Reinforcement Learning Resource Allocation is compared with conventional LoRaWAN and Adaptive Priority-aware Resource Allocation (APRA). The proposed work is properly cross-validated to utterly show unbiased results. Supervised by Dr, Kashif Naseer Qureshi 2023-01-01T00:00:00Z