Mohammed Benmubarak

The focus of this study is to identify and reduce Advanced Persistent Threats (APTs) in the cloud environment of Amazon Web Services (AWS). Popular security frameworks like MITRE ATT&CK, Cyber-Kill Chain and Pyramid of Pain were employed to improve effectiveness of forensic investigation in cloud environments. Tactics, techniques and procedures (TTPs) using Cloud Trail log data were analyzed in order to discover the efficiency of these frameworks in attack patterns identification. Findings from the study reveals that logs are crucial for identifying attack trends such as lateral movement, exfiltration of data, escalation of privileges in order to help improve understanding and analysis of APT activities in AWS environment, and the integration of frameworks such as MITRE ATT & CK, Cyber-Kill Pains and Pyramid of Pain provides strategies that are proactive to quelling advanced cyber adversaries

Internet of things (IoT) field is quickly developing, with billions of devices deployed worldwide to provide IT solutions. It is an intersection point between computer science and electronic engineering, meaning that learning the fundamentals of electronic engineering is critical for computing students pursuing a career path in this field. This research aims to investigate how simulation-based learning affects computing students' confidence in developing IoT solutions. A mixed-methods approach was used, utilising pre and post-surveys with both closed and open-ended questions. Results indicate a significant increase in student confidence after the intervention, with a paired samples t-test showing a statistically significant improvement (p = 0.007). Data reliability was confirmed using Cronbach's alpha, yielding values of 0.924 and 0.859 for the pre and post-surveys, respectively. The findings have been validated using the triangulation approach. The findings suggest that the use of simulation-based learning significantly improved students' confidence and practical skills in IoT development. Reference to this paper should be made as follows: BenMubarak, M., Harinda, E. and Ihsan, M. (xxxx) 'Investigating the impact of simulation-based learning on computing students' confidence in developing internet of things solutions', Int.). His research interests include networks, wireless communication, IoT, AI and machine learning. He has international teaching experience in Yemen, Malaysia, Saudi Arabia, and the UK and has published in peer-reviewed journals and conferences. Eugen Harinda has a PhD and is a Lecturer at the University of Bolton, specialising in IoT, Data Science, and Artificial Intelligence. He is also a Fellow of Advance HE. His academic interests span applied machine learning, 2 M. BenMubarak et al. with a current focus on computer vision and deep learning techniques. His recent research explores intelligent systems capable of visual recognition and automated decision-making. He actively contributes to interdisciplinary projects that harness AI to solve real-world problems. With a background in both academic teaching and research, he is committed to advancing innovation in data-driven technologies and mentoring the next generation of AI professionals. Mansoor Ihsan is a Lecturer in Computing at the University of Bolton and holds a PhD degree in Computer Sciences and MSc in Data Telecom and Networks. He is also a Fellow of Advance HE. He is a computer networks researcher with expertise in sensor networks, cybersecurity and network security. He has experience in teaching a range of subjects including Linux OS, programming, cybersecurity and computer networks. He has a particular research interest in IoT, sensor networks, cybersecurity and cloud security. His current research area involves security in IoT and cloud leveraging machine learning technologies.

Mobile learning is seen as another leap in education system as it complements the traditional classroom teaching due to the advancement in mobile technology. Despite this advancement, the elements that will define the acceptance of mobile technology in education have yet to be explored. This paper emphasizes on mobile technology as a key component in the acceptance of mobile learning. In the existing acceptance model, the user perception is taken as the most important factor for the real use of the system. Whereas in Information System model, the factor that drives Users satisfaction is based on quality of service and information. However in mobile learning the strong successful factor must integrate mobile technology as one component in the model. This paper proposes mobile learning technology model that includes mobile technology as an important components besides user acceptance and information success model to ensure successful implementation of m-learning in higher learning institutions (HLI). The results showed that there are three main elements which constitute m-learning environment namely academicians, students and mobile technology. Mobile technology, infrastructure and technical system quality have been added in the technology acceptance model and information system success model.

Mobile WiMAX is a broadband technology that is capable of delivering triple play services (voice, data, and video). However, mobility in mobile WiMAX system is still an issue when the mobile station (MS) moves and its connection is handed over between base stations (BSs). In the handover process, scanning is one of the required phases to find the target BS. During the handover scanning process, the MS must synchronize with all the advertised neighbour BSs (nBSs) to select the best BS candidate for the incoming handover action. Without terminating the connection between the SBS and MS, the SBS will schedule the scanning intervals and sleep-intervals (also called interleaving interval) to MS for the handover scanning. However, during the scanning interval period, all the coming transmissions will be paused. Therefore, the redundant or unnecessary scanning of neighbouring BS cause delay and MAC overhead which may affect real-time applications. In this paper, the MS movement direction prediction (MMDP) based handover scanning scheme is introduced to overcome the mobile WiMAX handover scanning issue. It based on dividing the BS coverage area is into zones and sectors. According to the signal quality; there are three zones, no handover (No-HO), low handover (Low-HO) and high handover (High-HO) zones respectively and six sectors. In this scheme, only two BSs can become candidates; the two that the MS moves toward them will be chosen as the candidate for the handover scanning purpose. Hence, the handover scanning process repetition will be reduced with these two shortlisted BS candidates instead of scanning all nBSs. Thus, MMDP will reduce scanning delay and the number of exchange messages during the handover scanning comparing to the conventional scanning scheme. Although, the MMDP may need an extra computational time, the prediction and scanning process will be finished before the MS reach the High-HO zone, which mean the end-user's running application will be affected. Simulation results show that the proposed MMDP scheme reduces the total handover scanning delay and scanning interval duration by 25 and 50 % respectively. Also, the size of scanning message is reduced, which leads to reduced signalling overhead.

It is well known that WiMAX is a broadband technology that is capable of delivering triple play (voice, data, and video) services. However, mobility in WiMAX system is still a main issue when the mobile station (MS) moves across the base station (BS) coverage and be handed over between BSs. Among the challenging issues in mobile WiMAX handover are unnecessary handover, handover failure and handover delay, which may affect real-time applications. The conventional handover decision algorithm in mobile WiMAX is based on a single criterion, which usually uses the received signal strength indicator (RSSI) as an indicator, with the other fixed handover parameters such as handover threshold and handover margin. In this paper, a fuzzy logic based self-adaptive handover (FuzSAHO) algorithm is introduced. The proposed algorithm is derived from the self-adaptive handover parameters to overcome the mobile WiMAX ping-pong handover and handover delay issues. Hence, the proposed FuzSAHO is initiated to check whether a handover is necessary or not which depends on its fuzzy logic stage. The proposed FuzSAHO algorithm will first self-adapt the handover parameters based on a set of multiple criteria, which includes the RSSI and MS velocity. Then the handover decision will be executed according to the handover parameter values. Simulation results show that the proposed FuzSAHO algorithm reduces the number of ping-pong handover and its delay. When compared with RSSI based handover algorithm and mobility improved handover (MIHO) algorithm, respectively, FuzSAHO reduces the number of handovers by 12.5 and 7.5 %, respectively, when the MS velocity is < 17 m/s. In term of handover delay, the proposed FuzSAHO algorithm shows an improvement of 27.8 and 8 % as compared to both conventional and MIHO algorithms, respectively. Thus, the proposed multi-criteria with fuzzy logic based self-adaptive handover algorithm called FuzSAHO, outperforms both conventional and MIHO handover algorithms.

Mobile WiMAX is a broadband technology that is capable of delivering triple play services (voice, data, and video). However, mobility in WiMAX system is still an issue when the mobile station (MS) moves and be handovered between base stations (BSs). This issue can cause unnecessary neighboring BS scanning and association, handover delay, and MAC overhead which may affect real-time applications. In this paper, the MS movement direction prediction (MMDP) based MS scanning to overcome the mobile WiMAX handover issues is presented. In this scheme, only potential BS candidates will be chosen for scanning purpose. Hence, the MS scanning process reputation will be reduced with these shortlisted BS candidates instead of scanning all neighbors BSs. Simulation results show that the proposed scheme reduces total scanning delay and scanning interval duration by 25% and 50% respectively.

Handover (HO) mechanism is one of the critical operations in mobile WiMAX. It takes place when a mobile station (MS) moves from a serving base station (BS) to another BS. However, the HO latency in mobile WiMAX is still an issue that may affect continuity of real-time application sessions such as Voice over Internet Protocol (VoIP). This paper presents performance comparison of some HO mechanisms for real-time applications in mobile WiMAX, including HO, cross-layer HO, pre-coordination HO, passport HO mechanisms and fast intra-network and cross-layer HO (FINCH). Each one of these mechanisms reduces HO latency, especially during downlink traffic; however, they still produce insufficient HO latency during uplink traffic. Except for FINCH, they do not consider the scenario of HO between BSs that belong to different access service network-gateways (ASN-GWs) or to different connectivity service networks (CSNs); these two cases cause extra layer 3 (L3) HO latency.

Grid computing technology is a new field which focuses on large-scale resource sharing. The grid concept is coordinated resource sharing and problem solving in dynamic, multi-institutional virtual organizations (VO). In this dynamic and geographically dispersed setting, information services are regarded as a vital component of the grid infrastructure. Grid Information Services (GIS) address the challenging problems of the discovery and ongoing monitoring of the existence and characteristics of resources, services, computations and other entities of value to the grid. The growing number of grid services available within an organization raises a new and challenging search problem: locating desired grid services. This paper proposes a search engine for grid services with improved the system efficiency by using a multi agent system (MAS)-based system for crawling