List of Titles

A novel countermeasure technique for reactive jamming attack in internet of things

- Fadele, Alaba, Othman, Mazliza, Hashem, Ibrahim, Yaqoob, Ibrar, Imran, Muhammad, Shoaib, Muhammad

Authors: Fadele, Alaba , Othman, Mazliza , Hashem, Ibrahim , Yaqoob, Ibrar , Imran, Muhammad , Shoaib, Muhammad
Date: 2019
Type: Text , Journal article
Relation: Multimedia Tools and Applications Vol. 78, no. 21 (2019), p. 29899-29920
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Description: In recent years, Internet of Things (IoT) has attracted significant attention because of its wide range of applications in various domains. However, security is a growing concern as users of small devices in an IoT network are unable to defend themselves against reactive jamming attacks. These attacks negatively affect the performance of devices and hinder IoT operations. To address such an issue, this paper presents a novel countermeasure detection and consistency algorithm (CDCA), which aims to fight reactive jamming attacks on IoT networks. The proposed CDCA uses a change in threshold value to detect and treat an attack. The algorithm employs channel signal strength to check packet consistency by determining if the data transmission value contradicts the threshold value. The node that sends the threshold value is periodically checked and the threshold value is compared with the current value after data transmission to find out if an attack has occurred in the network. Based on realistic simulation scenarios (e.g., with varying traffic interval, number of malicious nodes, and random mobility patterns), the performance of the proposed CDCA is evaluated using a Cooja simulator. Simulation results demonstrate the superiority of the proposed technique compared with contemporary schemes in terms of performance metrics such as energy consumption, traffic delay, and network throughput. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Big data analytics in industrial IoT using a concentric computing model

- Rehman, Muhammad, Ahmed, Ejaz, Yaqoob, Ibrar, Hashem, Ibrahim, Imran, Muhammad, Ahmad, Shafiq

Authors: Rehman, Muhammad , Ahmed, Ejaz , Yaqoob, Ibrar , Hashem, Ibrahim , Imran, Muhammad , Ahmad, Shafiq
Date: 2018
Type: Text , Journal article
Relation: IEEE Communications Magazine Vol. 56, no. 2 (2018), p. 37-43
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Description: The unprecedented proliferation of miniaturized sensors and intelligent communication, computing, and control technologies have paved the way for the development of the Industrial Internet of Things. The IIoT incorporates machine learning and massively parallel distributed systems such as clouds, clusters, and grids for big data storage, processing, and analytics. In IIoT, end devices continuously generate and transmit data streams, resulting in increased network traffic between device-cloud communication. Moreover, it increases in-network data transmissions. requiring additional efforts for big data processing, management, and analytics. To cope with these engendered issues, this article first introduces a novel concentric computing model (CCM) paradigm composed of sensing systems, outer and inner gateway processors, and central processors (outer and inner) for the deployment of big data analytics applications in IIoT. Second, we investigate, highlight, and report recent research efforts directed at the IIoT paradigm with respect to big data analytics. Third, we identify and discuss indispensable challenges that remain to be addressed for employing CCM in the IIoT paradigm. Lastly, we provide several future research directions (e.g., real-Time data analytics, data integration, transmission of meaningful data, edge analytics, real-Time fusion of streaming data, and security and privacy). © 1979-2012 IEEE.

Clustering-based real-time anomaly detection—a breakthrough in big data technologies

- Habeeb, Riyaz, Nasaruddin, Fariza, Gani, Abdullah, Amanullah, Mohamed, Hashem, Ibrahim, Ahmed, Ejaz, Imran, Muhammad

Authors: Habeeb, Riyaz , Nasaruddin, Fariza , Gani, Abdullah , Amanullah, Mohamed , Hashem, Ibrahim , Ahmed, Ejaz , Imran, Muhammad
Date: 2022
Type: Text , Journal article
Relation: Transactions on Emerging Telecommunications Technologies Vol. 33, no. 8 (2022), p.
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Description: 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. © 2019 John Wiley & Sons, Ltd.

Internet of things architecture : recent advances, taxonomy, requirements, and open challenges

- Yaqoob, Ibrar, Ahmed, Ejaz, Hashem, Ibrahim, Ahmed, Abdelmuttlib, Gani, Abdullah, Imran, Muhammad, Guizani, Mohsen

Authors: Yaqoob, Ibrar , Ahmed, Ejaz , Hashem, Ibrahim , Ahmed, Abdelmuttlib , Gani, Abdullah , Imran, Muhammad , Guizani, Mohsen
Date: 2017
Type: Text , Journal article
Relation: IEEE Wireless Communications Vol. 24, no. 3 (2017), p. 10-16
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Description: Recent years have witnessed tremendous growth in the number of smart devices, wireless technologies, and sensors. In the foreseeable future, it is expected that trillions of devices will be connected to the Internet. Thus, to accommodate such a voluminous number of devices, scalable, flexible, interoperable, energy-efficient, and secure network architectures are required. This article aims to explore IoT architectures. In this context, first, we investigate, highlight, and report premier research advances made in IoT architecture recently. Then we categorize and classify IoT architectures and devise a taxonomy based on important parameters such as applications, enabling technologies, business objectives, architectural requirements, network topologies, and IoT platform architecture types. We identify and outline the key requirements for future IoT architecture. A few prominent case studies on IoT are discovered and presented. Finally, we enumerate and outline future research challenges. © 2002-2012 IEEE.

Recent advances and challenges in mobile big data

- Ahmed, Ejaz, Yaqoob, Ibrar, Hashem, Ibrahim, Shuja, Junaid, Imran, Muhammad

Authors: Ahmed, Ejaz , Yaqoob, Ibrar , Hashem, Ibrahim , Shuja, Junaid , Imran, Muhammad
Date: 2018
Type: Text , Journal article
Relation: IEEE Communications Magazine Vol. 56, no. 2 (2018), p. 102-108
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Description: The unabated flurry of research activities dedicated to gaining business insights from a flood of data generated by heterogeneous mobile sources, such as the Internet of Vehicles, sensors, and smartphones, has instigated a new research domain called MBD. At the core of this mobile environment, scalability, cost effectiveness, reliability, analytics, and security are important concerns. Coping with these issues in handling MBD requires understanding the challenges associated with it. Mobile computing and big data have been widely studied separately; however, very few studies have explored the convergence of these two domains. In this article, we critically review recent research efforts directed at MBD. We also classify the MBD by devising a thematic taxonomy that is based on source, analytics, applications, characteristics, security, and data type. Furthermore, we discuss the opportunities offered by MBD in terms of analytics. Some potential uses of MBD in healthcare, telecommunication, digital advertising, and transportation are also presented. Several open research challenges are discussed as future research directions. © 1979-2012 IEEE. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran” is provided in this record**

The role of big data analytics in internet of things

- Ahmed, Ejaz, Yaqoob, Ibrar, Hashem, Ibrahim, Khan, Imran, Imran, Muhammad

Authors: Ahmed, Ejaz , Yaqoob, Ibrar , Hashem, Ibrahim , Khan, Imran , Imran, Muhammad
Date: 2017
Type: Text , Journal article
Relation: Computer Networks Vol. 129, no. (2017), p. 459-471
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Description: The explosive growth in the number of devices connected to the Internet of Things (IoT) and the exponential increase in data consumption only reflect how the growth of big data perfectly overlaps with that of IoT. The management of big data in a continuously expanding network gives rise to non-trivial concerns regarding data collection efficiency, data processing, analytics, and security. To address these concerns, researchers have examined the challenges associated with the successful deployment of IoT. Despite the large number of studies on big data, analytics, and IoT, the convergence of these areas creates several opportunities for flourishing big data and analytics for IoT systems. In this paper, we explore the recent advances in big data analytics for IoT systems as well as the key requirements for managing big data and for enabling analytics in an IoT environment. We taxonomized the literature based on important parameters. We identify the opportunities resulting from the convergence of big data, analytics, and IoT as well as discuss the role of big data analytics in IoT applications. Finally, several open challenges are presented as future research directions. © 2017 Elsevier B.V. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Muhammad Imran” is provided in this record**

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