ORCID Profile
0000-0002-0245-5290
Current Organisation
Deakin University
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Publisher: Elsevier BV
Date: 02-2020
Publisher: IEEE
Date: 21-03-2022
Publisher: Wiley
Date: 11-06-2019
DOI: 10.1002/ETT.3647
Abstract: Off late, the ever increasing usage of a connected Internet‐of‐Things devices has consequently augmented the volume of real‐time network data with high velocity. At the same time, threats on networks become inevitable hence, identifying anomalies in real time network data has become crucial. To date, most of the existing anomaly detection approaches focus mainly on machine learning techniques for batch processing. Meanwhile, detection approaches which focus on the real‐time analytics somehow deficient in its detection accuracy while consuming higher memory and longer execution time. As such, this paper proposes a novel framework which focuses on real‐time anomaly detection based on big data technologies. In addition, this paper has also developed streaming sliding window local outlier factor coreset clustering algorithms (SSWLOFCC), which was then implemented into the framework. The proposed framework that comprises BroIDS, Flume, Kafka, Spark streaming, SparkMLlib, Matplot and HBase was evaluated to substantiate its efficacy, particularly in terms of accuracy, memory consumption, and execution time. The evaluation is done by performing critical comparative analysis using existing approaches, such as K‐means, hierarchical density‐based spatial clustering of applications with noise (HDBSCAN), isolation forest, spectral clustering and agglomerative clustering. Moreover, Adjusted Rand Index and memory profiler package were used for the evaluation of the proposed framework against the existing approaches. The outcome of the evaluation has substantially proven the efficacy of the proposed framework with a much higher accuracy rate of 96.51% when compared to other algorithms. Besides, the proposed framework also outperformed the existing algorithms in terms of lesser memory consumption and execution time. Ultimately the proposed solution enable analysts to precisely track and detect anomalies in real time.
Publisher: Association for Computing Machinery (ACM)
Date: 28-08-2024
DOI: 10.1145/3596598
Abstract: Cooperative Intelligent Transport Systems (C-ITS) is one of the proposed solutions to improve the safety and efficiency of road transport. However, C-ITS is prone to misbehaviours that can cause devastating effects such as road accidents and potential loss of life. While there are several research studies including technical studies and surveys that discuss misbehaviour detection in C-ITS, they tend to focus on specific aspects of misbehaviour detection and do not provide a comprehensive and unified coverage. With the objective of serving as a reference for future researchers, this study provides a comprehensive survey of misbehaviour detection in C-ITS. It identifies and discusses the key causes of misbehaviour in C-ITS, and the mechanisms used to detect them. Additionally, it proposes a thematic taxonomy on misbehaviour detection based on a comparative analysis of the technical studies. Furthermore, the existing solutions from the state-of-the-art and their shortcomings are also presented. Finally, this study highlights several significant research challenges and discusses future research directions in the area of misbehaviour detection in C-ITS. In doing so, this study contributes to the existing literature in an important field of study.
Publisher: Elsevier BV
Date: 09-2022
No related grants have been discovered for Mohamed Ahzam Amanullah.