Enhancing Cloud Network Security with Innovative Time Series Analysis Amer Al-Mazrawe, Bahaa Al-Musawi Journal of Internet Services and Applications, 2025 Cloud computing has revolutionized computing infrastructure abstraction and utilization, distinguished by its cost-effective and high-quality services. However, the challenge of securing cloud networks persists, mainly due to the broad exchange of data and the inherent complexity of these techniques. Anomaly detection emerges as a promising solution to improve cloud network safeness, presenting perception into system behavior and alerting operators for further actions. This paper offers a novel time series analysis method for detecting anomalies in cloud networks. Our technique employs innovative time series analysis techniques based on a matrix profile, and the Kneedle algorithm to identify multi-dimensional anomalous patterns within multiple features extracted from network traffic streams. To evaluate the efficacy of our approach, we implemented timestamp-based and index-based methods to two distinct datasets: the most widely used UNSW-NB15 and the recently introduced CICIoT2023 datasets. The results highlight the efficacy of our proposed method in identifying cloud network anomalies. It achieved an impressive accuracy of 99.6% and an F1-score of 99.8% using the timestamp-based analysis method. For the index-based analysis method, accuracy reached 98%, accompanied by an outstanding F1-score of 99.9%.
Novel Data-Driven Geolocation Approach for Detecting Smuggled Internet Traffic Bahaa Al-Musawi, Dina Shehada, Abir Jaafar Hussain IEEE Access, 2025 Enforcing Internet censorship in decentralised networks, such as those in India, Iraq, and Russia, poses significant challenges due to the intricate nature of their interconnected subnetworks. This paper presents a novel approach that combines Internet routing traffic analysis with IP geolocation data to identify smuggled prefixes and their associated autonomous system numbers, using Iraq as a case study. Our methodology integrates diverse datasets, including integrated autonomous system number data from IPinfo and Cloudflare Radar, Internet routing traffic from the RouteViews project, and patterns observed during periodic Internet shutdowns for national exams. By cross-referencing these data sources, we enhance the detection of smuggled autonomous system numbers and provide insights into the geographical distribution of unauthorised Internet traffic. Furthermore, the study addresses evasion techniques, such as AS-PATH prepending, and proposes collaborative strategies to improve detection accuracy. These contributions provide a scalable and robust framework for strengthening Internet governance and enhancing security in fragmented network infrastructures.
Anomaly Detection in Cloud Network: A Review Amer Al-Mazrawe, Bahaa Al-Musawi Bio Web of Conferences, 2024 Cloud computing stands out as one of the fastest-growing technologies in the 21st century, offering enterprises opportunities to reduce costs, enhance scalability, and increase flexibility through rapid access to a shared pool of elastic computing resources. However, its security remains a significant challenge. As cloud networks grow in complexity and scale, the need for effective anomaly detection becomes crucial. Identifying anomalous behavior within cloud networks poses a challenge due to factors such as the voluminous data exchanged and the dynamic nature of underlying cloud infrastructures. Detecting anomalies helps prevent threats and maintain cloud operations. This literature review examines previous works in anomaly detection in the cloud that employ various strategies for anomaly detection, describes anomaly detection datasets, discusses the challenges of anomaly detection in cloud networks, and presents directions for future studies.
A Comparative Study of IDS-Based Deep Learning Models for IoT Network Bassam Noori Shaker, Bahaa Qasim Al-Musawi, Mohammed Falih Hassan ACM International Conference Proceeding Series, 2023 The proliferation of connected devices within Internet of Things (IoT) networks has underscored the critical necessity of developing efficient Intrusion Detection Systems (IDS) that can adeptly identify and counteract threats. Deep learning algorithms are better than classical machine learning at automatically learning patterns and representations from raw data, enabling them to detect complex attacks. This research aims to conduct a comparative study of IDSs implemented through three deep learning models: Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and Recurrent Neural Networks (RNN). The study employs three datasets (NF-UNSW-NB15, NF-BoT-IoT, and NF-ToN-IoT) to assess model performance in both binary and multiclass classification scenarios. The findings indicate that for binary traffic classification, DNN outperforms the other two models with an accuracy bout (98%) for all datasets. In the context of multiclass traffic classification, the DNN model surpasses the performance of the other model except for NF-ToN-IoT a CNN model score (69.08%).
Measuring Network-Level Internet Censorship: DNS and IP-Based Filtering Across Iraqi Residential ISPs A Al-Dujaily, B Al-Musawi Computers & Security, 104956 , 2026 2026
Explainable AI for enhancing IDS against advanced persistent kill chain BN Shaker, B Al-Musawi, MF Hassan Cluster Computing 28 (7), 459 , 2025 2025 Citations: 3
A lightweight IDS for early APT detection using a novel feature selection method BN Shaker, B Al-Musawi, MF Hassan arXiv preprint arXiv:2506.12108 , 2025 2025 Citations: 4
A Lightweight IDS for Early APT Detection Using a Novel Feature Selection Method B Noori Shaker, B Al-Musawi, M Falih Hassan arXiv e-prints, arXiv: 2506.12108 , 2025 2025
Explainable AI for Enhancing IDS Against Advanced Persistent Kill Chain B Noori Shaker, B Al-Musawi, M Falih Hassan arXiv e-prints, arXiv: 2506.07480 , 2025 2025
Novel data-driven geolocation approach for detecting smuggled internet traffic B Al-Musawi, D Shehada, AJ Hussain IEEE Access , 2025 2025 Citations: 2
Enhancing Cloud Network Security with Innovative Time Series Analysis A Al-Mazrawe, B Al-Musawi Journal of Internet Services and Applications 16 (1), 13-24 , 2025 2025 Citations: 28
Anomaly detection in cloud network: A review A Al-Mazrawe, B Al-Musawi BIO Web of Conferences 97, 00019 , 2024 2024 Citations: 12
A comparative study of ids-based deep learning models for IoT network BN Shaker, BQ Al-Musawi, MF Hassan Proceedings of the 2023 International Conference on Advances in Artificial … , 2023 2023 Citations: 18
Innovative fitness functions for robust energy management in WSNs MF Hassan, B Al-Musawi, AK Al-Janabi Journal of Network and Systems Management 31 (4), 76 , 2023 2023 Citations: 5
A new intrusion detection system based on using non-linear statistical analysis and features selection techniques A Al-Bakaa, B Al-Musawi Computers & Security 122, 102906 , 2022 2022 Citations: 25
Flow-based intrusion detection systems: A survey A Al-Bakaa, B Al-Musawi International Conference on Applications and Techniques in Information … , 2021 2021 Citations: 6
A survey of BGP anomaly detection using machine learning techniques NH Hammood, B Al-Musawi, AH Alhilali International Conference on Applications and Techniques in Information … , 2021 2021 Citations: 16
Using BGP features towards identifying type of BGP anomaly NH Hammood, B Al-Musawi 2021 International Congress of Advanced Technology and Engineering (ICOTEN … , 2021 2021 Citations: 21
Improving the performance of intrusion detection system through finding the most effective features A Al-Bakaa, B Al-Musawi 2021 International Congress of Advanced Technology and Engineering (ICOTEN), 1-9 , 2021 2021 Citations: 7
Internet of Things security techniques: A survey SM Alturfi, HA Marhoon, B Al-Musawi AIP Conference Proceedings 2290 (1), 040016 , 2020 2020 Citations: 6
An advanced classification of cloud computing security techniques: A survey SM Alturfi, B Al-Musawi, HA Marhoon AIP Conference Proceedings 2290 (1), 040017 , 2020 2020 Citations: 5
Design and implementation of fast floating point units for FPGAs MF Hassan, KF Hussein, B Al-Musawi Indonesian Journal of Electrical Engineering and Computer Science 19 (3 … , 2020 2020 Citations: 6
RDTD: A tool for detecting internet routing disruptions at AS-level B Al-Musawi, MF Hassan, SM Alturfi Journal of Telecommunications and the Digital Economy 8 (2), 18-30 , 2020 2020 Citations: 3
Energy-balanced and distributed clustering protocol for IoT wireless sensors MF Hassan, SR Pokhrel, B Al-Musawi 2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW … , 2020 2020 Citations: 7
MOST CITED SCHOLAR PUBLICATIONS
BGP anomaly detection techniques: A survey B Al-Musawi, P Branch, G Armitage IEEE Communications Surveys & Tutorials 19 (1), 377-396 , 2016 2016 Citations: 216
Mitigating DoS/DDoS attacks using iptables BQM Al-Musawi International Journal of Engineering & Technology 12 (3), 101-111 , 2012 2012 Citations: 54
Detecting BGP instability using recurrence quantification analysis (RQA) B Al-Musawi, P Branch, G Armitage 2015 IEEE 34th International Performance Computing and Communications … , 2015 2015 Citations: 48
Enhancing Cloud Network Security with Innovative Time Series Analysis A Al-Mazrawe, B Al-Musawi Journal of Internet Services and Applications 16 (1), 13-24 , 2025 2025 Citations: 28
A new intrusion detection system based on using non-linear statistical analysis and features selection techniques A Al-Bakaa, B Al-Musawi Computers & Security 122, 102906 , 2022 2022 Citations: 25
Using BGP features towards identifying type of BGP anomaly NH Hammood, B Al-Musawi 2021 International Congress of Advanced Technology and Engineering (ICOTEN … , 2021 2021 Citations: 21
A comparative study of ids-based deep learning models for IoT network BN Shaker, BQ Al-Musawi, MF Hassan Proceedings of the 2023 International Conference on Advances in Artificial … , 2023 2023 Citations: 18
Recurrence behaviour of BGP traffic B Al-Musawi, P Branch, G Armitage 2017 27th International Telecommunication Networks and Applications … , 2017 2017 Citations: 17
A survey of BGP anomaly detection using machine learning techniques NH Hammood, B Al-Musawi, AH Alhilali International Conference on Applications and Techniques in Information … , 2021 2021 Citations: 16
Identifying OSPF LSA falsification attacks through non-linear analysis B Al-Musawi, P Branch, MF Hassan, SR Pokhrel Computer Networks 167, 107031 , 2020 2020 Citations: 15
Anomaly detection in cloud network: A review A Al-Mazrawe, B Al-Musawi BIO Web of Conferences 97, 00019 , 2024 2024 Citations: 12
Detecting BGP anomalies using recurrence quantification analysis B Al-Musawi Swinburne , 2018 2018 Citations: 11
BGP Replay Tool (BRT) v0.2 B Al-Musawi, R Al-Saadi, P Branch, G Armitage http://i4t.swin.edu.au/reports/I4TRL-TR-170606A.pdf , 2017 2017 Citations: 10
Improving the performance of intrusion detection system through finding the most effective features A Al-Bakaa, B Al-Musawi 2021 International Congress of Advanced Technology and Engineering (ICOTEN), 1-9 , 2021 2021 Citations: 7
Energy-balanced and distributed clustering protocol for IoT wireless sensors MF Hassan, SR Pokhrel, B Al-Musawi 2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW … , 2020 2020 Citations: 7
Flow-based intrusion detection systems: A survey A Al-Bakaa, B Al-Musawi International Conference on Applications and Techniques in Information … , 2021 2021 Citations: 6
Internet of Things security techniques: A survey SM Alturfi, HA Marhoon, B Al-Musawi AIP Conference Proceedings 2290 (1), 040016 , 2020 2020 Citations: 6
Design and implementation of fast floating point units for FPGAs MF Hassan, KF Hussein, B Al-Musawi Indonesian Journal of Electrical Engineering and Computer Science 19 (3 … , 2020 2020 Citations: 6
Innovative fitness functions for robust energy management in WSNs MF Hassan, B Al-Musawi, AK Al-Janabi Journal of Network and Systems Management 31 (4), 76 , 2023 2023 Citations: 5
An advanced classification of cloud computing security techniques: A survey SM Alturfi, B Al-Musawi, HA Marhoon AIP Conference Proceedings 2290 (1), 040017 , 2020 2020 Citations: 5
