Clustering-based real-time anomaly detection—a breakthrough in big data technologies
- 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.
Deep learning and big data technologies for IoT security
- Authors: Amanullah, Mohamed , Habeeb, Riyaz , Nasaruddin, Fariza , Gani, Abdullah , Ahmed, Ejaz , Nainar, Abdul , Akim, Nazihah , Imran, Muhammad
- Date: 2020
- Type: Text , Journal article , Review
- Relation: Computer Communications Vol. 151, no. (2020), p. 495-517
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- Description: Technology has become inevitable in human life, especially the growth of Internet of Things (IoT), which enables communication and interaction with various devices. However, IoT has been proven to be vulnerable to security breaches. Therefore, it is necessary to develop fool proof solutions by creating new technologies or combining existing technologies to address the security issues. Deep learning, a branch of machine learning has shown promising results in previous studies for detection of security breaches. Additionally, IoT devices generate large volumes, variety, and veracity of data. Thus, when big data technologies are incorporated, higher performance and better data handling can be achieved. Hence, we have conducted a comprehensive survey on state-of-the-art deep learning, IoT security, and big data technologies. Further, a comparative analysis and the relationship among deep learning, IoT security, and big data technologies have also been discussed. Further, we have derived a thematic taxonomy from the comparative analysis of technical studies of the three aforementioned domains. Finally, we have identified and discussed the challenges in incorporating deep learning for IoT security using big data technologies and have provided directions to future researchers on the IoT security aspects. © 2020 Elsevier B.V.
Process migration-based computational offloading framework for IoT-supported mobile edge/cloud computing
- Authors: Yousafzai, Abdullah , Yaqoob, Ibrar , Imran, Muhammad , Gani, Abdullah , Md Noor, Rafidah
- Date: 2020
- Type: Text , Journal article
- Relation: IEEE Internet of Things Journal Vol. 7, no. 5 (2020), p. 4171-4182
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- Description: Mobile devices have become an indispensable component of Internet of Things (IoT). However, these devices have resource constraints in processing capabilities, battery power, and storage space, thus hindering the execution of computation-intensive applications that often require broad bandwidth, stringent response time, long-battery life, and heavy-computing power. Mobile cloud computing and mobile edge computing (MEC) are emerging technologies that can meet the aforementioned requirements using offloading algorithms. In this article, we analyze the effect of platform-dependent native applications on computational offloading in edge networks and propose a lightweight process migration-based computational offloading framework. The proposed framework does not require application binaries at edge servers and thus seamlessly migrates native applications. The proposed framework is evaluated using an experimental testbed. Numerical results reveal that the proposed framework saves almost 44% of the execution time and 84% of the energy consumption. Hence, the proposed framework shows profound potential for resource-intensive IoT application processing in MEC. © 2014 IEEE.
Process state synchronization-based application execution management for mobile edge/cloud computing
- Authors: Ahmed, Ejaz , Naveed, Anjum , Gani, Abdullah , Hamid, Siti , Imran, Muhammad , Guizani, Mohsen
- Date: 2019
- Type: Text , Journal article
- Relation: Future Generation Computer Systems Vol. 91, no. (2019), p. 579-589
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- Description: Mobile cloud computing (MCC) and mobile edge computing (MEC) facilitate the mobile devices to augment their capabilities by utilizing the resources and services offered by Cloud and Edge Cloud, respectively. However, due to mobility, network connection becomes unstable that causes application execution disruption. Such disruption increases the execution time and in some cases restrain the mobile devices from getting execution results from the cloud. This research work analyzes the impact of user mobility on the execution of cloud-based mobile applications. We propose a Process State Synchronization (PSS)-based execution management to solve the aforementioned problem. We analytically compute a sufficient condition on synchronization interval that ensure reduction in mobile application execution time under PSS in case of disconnection. Similarly, we compute the upper bound on synchronization interval whereby a larger synchronization interval did not result in significant benefits in terms of execution time for the mobile application. The analytical results were confirmed by the sample implementation of PSS with the computed synchronization intervals. Moreover, we also compare the performance of proposed solution with the state-of-the-art solutions. The results show that the PSS-based execution outperforms the other contemporary solutions. © 2018 Elsevier B.V.
Real-time big data processing for anomaly detection : a survey
- Authors: Ariyaluran Habeeb, Riyaz , Nasaruddin, Fariza , Gani, Abdullah , Targio Hashem, Ibrahim , Ahmed, Ejaz , Imran, Muhammad
- Date: 2019
- Type: Text , Journal article , Review
- Relation: International Journal of Information Management Vol. 45, no. (2019), p. 289-307
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- Description: The advent of connected devices and omnipresence of Internet have paved way for intruders to attack networks, which leads to cyber-attack, financial loss, information theft in healthcare, and cyber war. Hence, network security analytics has become an important area of concern and has gained intensive attention among researchers, off late, specifically in the domain of anomaly detection in network, which is considered crucial for network security. However, preliminary investigations have revealed that the existing approaches to detect anomalies in network are not effective enough, particularly to detect them in real time. The reason for the inefficacy of current approaches is mainly due the amassment of massive volumes of data though the connected devices. Therefore, it is crucial to propose a framework that effectively handles real time big data processing and detect anomalies in networks. In this regard, this paper attempts to address the issue of detecting anomalies in real time. Respectively, this paper has surveyed the state-of-the-art real-time big data processing technologies related to anomaly detection and the vital characteristics of associated machine learning algorithms. This paper begins with the explanation of essential contexts and taxonomy of real-time big data processing, anomalous detection, and machine learning algorithms, followed by the review of big data processing technologies. Finally, the identified research challenges of real-time big data processing in anomaly detection are discussed. © 2018 Elsevier Ltd
SDN-Based load balancing service for cloud servers
- Authors: Abdelaziz, Ahmed , Ahmed, Ejaz , Fong, Ang , Gani, Abdullah , Imran, Muhammad
- Date: 2018
- Type: Text , Journal article
- Relation: IEEE Communications Magazine Vol. 56, no. 8 (2018), p. 106-111
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- Description: With the continuous growth, heterogeneity, and ever increasing demand of services, load balancing of cloud servers is an emerging challenge to meet highly demanding requirements (e.g., data rates, latency, quality of service) of 5G network applications. Although various load balancing techniques have been proposed, some of these techniques either require installation of dedicated additional load balancers for each service, or manual reconfiguration of the device to handle new services is desired. These techniques are expensive, time-consuming, and impractical. Moreover, most of the existing load balancing schemes do not consider service types. This article presents an SDN-based load balancing (SBLB) service for cloud servers to maximize resource utilization and minimize response time of users. The constituents of the proposed scheme are an application module that runs on top of an SDN controller and server pools that connect to the controller through OpenFlow switches. The application module consists of a service classification module, a dynamic load balancing module, and a monitoring module. The controller handles all messages, manages host pools, and maintains the load of host in real time. Experimental results validate the performance of the proposed scheme. Through experimental results, SBLB demonstrates significant decrease in average response time and reply time. © 1979-2012 IEEE.
Heterogeneity-aware task allocation in mobile ad hoc cloud
- Authors: Yaqoob, Ibrar , Ahmed, Ejaz , Gani, Abdullah , Mokhtar, Salimah , Imran, Muhammad
- Date: 2017
- Type: Text , Journal article
- Relation: IEEE Access Vol. 5, no. (2017), p. 1779-1795
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- Description: Mobile Ad Hoc Cloud (MAC) enables the use of a multitude of proximate resource-rich mobile devices to provide computational services in the vicinity. However, inattention to mobile device resources and operational heterogeneity-measuring parameters, such as CPU speed, number of cores, and workload, when allocating task in MAC, causes inefficient resource utilization that prolongs task execution time and consumes large amounts of energy. Task execution is remarkably degraded, because the longer execution time and high energy consumption impede the optimum use of MAC. This paper aims to minimize execution time and energy consumption by proposing heterogeneity-aware task allocation solutions for MAC-based compute-intensive tasks. Results of the proposed solutions reveal that incorporation of the heterogeneity-measuring parameters guarantees a shorter execution time and reduces the energy consumption of the compute-intensive tasks in MAC. A system model is developed to validate the proposed solutions' empirical results. In comparison with random-based task allocation, the proposed five solutions based on CPU speed, number of core, workload, CPU speed and workload, and CPU speed, core, and workload reduce execution time up to 56.72%, 53.12%, 56.97%, 61.23%, and 71.55%, respectively. In addition, these heterogeneity-aware task allocation solutions save energy up to 69.78%, 69.06%, 68.25%, 67.26%, and 57.33%, respectively. For this reason, the proposed solutions significantly improve tasks' execution performance, which can increase the optimum use of MAC. © 2013 IEEE.
Internet of things architecture : recent advances, taxonomy, requirements, and open challenges
- 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.
Overcoming the key challenges to establishing vehicular communication : is SDN the answer?
- Authors: Yaqoob, Ibrar , Ahmad, Iftikhar , Ahmed, Ejaz , Gani, Abdullah , Imran, Muhammad , Guizani, Nadra
- Date: 2017
- Type: Text , Journal article
- Relation: IEEE Communications Magazine Vol. 55, no. 7 (2017), p. 128-135
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- Description: Considerable development in software-based configurable hardware has paved the way for a new networking paradigm called software-defined vehicular networks (SDVNs). The distinctive features of SDN, such as its flexibility and programmability, can help fulfill the performance and management requirements for VANETs. Although several studies exist on VANET and SDN, a tutorial on SDVNs is still lacking. In this article, we initially investigate recent premier research advances in the SDVN paradigm. Then we categorize and classify SDVN concepts and establish a taxonomy based on important characteristics, such as services, access technologies, network architectural components, opportunities, operational modes, and system components. Furthermore, we identify and outline the key requirements for SDVNs. Finally, we enumerate and outline future research challenges. © 2017 IEEE.
Social-aware resource allocation and optimization for D2D communication
- Authors: Ahmed, Ejaz , Yaqoob, Ibrar , Gani, Abdullah , Imran, Muhammad , Guizani, Mohsen
- Date: 2017
- Type: Text , Journal article
- Relation: IEEE Wireless Communications Vol. 24, no. 3 (2017), p. 122-129
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- Description: The undiminished growth of research activities to converge social awareness with D2D communication has paved the way for facilitating and providing significant benefits to users. Realizing these benefits depends on efficiently addressing several main technical challenges associated with the convergence. Although there are many research studies related to social networks and D2D communication, convergence of these two areas leads to further research efforts to implement social-aware D2D communication. In this article, we discuss recent advances in the domain of D2D communication from the perspective of social-aware resource allocation and optimization. We also categorize and classify the literature by devising a taxonomy based on channel-centric attributes, objectives, solving approaches, networking technologies, characteristics, and communication types. Moreover, we also outline the key requirements with the aim of providing guidelines for the domain researchers and designers to enable the social-aware resource allocation for D2D communication. Several open research challenges are presented as future research directions. © 2002-2012 IEEE.
Green industrial networking : recent advances, taxonomy, and open research challenges
- Authors: Ahmed, Ejaz , Yaqoob, Ibrar , Ahmed, Ahmed , Gani, Abdullah , Imran, Muhammad , Guizani, Sghaier
- Date: 2016
- Type: Text , Journal article
- Relation: IEEE Communications Magazine Vol. 54, no. 10 (2016), p. 38-45
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- Description: The consciousness of environmental problems has attracted the industry's attention toward the reduction of unnecessary energy emission by enabling green industrial networking. The reduction of unnecessary energy emitted by industrial networks can be a possible solution to many environmental issues. Green industrial networking is in its infancy, and an overview of the domain is still lacking. In this article, we discuss recent advances in industrial and green networking paradigms to investigate the impact on global communities. We also classify the literature by devising a taxonomy based on networking technologies, machines, network types, topologies, field bus types, transmission media, and hierarchical levels. Moreover, we identify and discuss key enablers (adaptive links, resource-based energy conservation, energy-efficient scheduling, energy-aware systems, energy-aware proxying, energy-conservative approaches, and low-power wireless protocols) for green industrial networking. Furthermore, we discuss challenges that remain to be addressed as future research directions. © 2016 IEEE.
Internet-of-things-based smart environments : state of the art, taxonomy, and open research challenges
- Authors: Ahmed, Ejaz , Yaqoob, Ibrar , Gani, Abdullah , Imran, Muhammad , Guizani, Mohsen
- Date: 2016
- Type: Text , Journal article
- Relation: IEEE Wireless Communications Vol. 23, no. 5 (2016), p. 10-16
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- Description: The rapid advancements in communication technologies and the explosive growth of the Internet of Things have enabled the physical world to invisibly interweave with actuators, sensors, and other computational elements while maintaining continuous network connectivity. The continuously connected physical world with computational elements forms a smart environment. A smart environment aims to support and enhance the abilities of its dwellers in executing their tasks, such as navigating through unfamiliar space and moving heavy objects for the elderly, to name a few. Researchers have conducted a number of efforts to use IoT to facilitate our lives and to investigate the effect of IoT-based smart environments on human life. This article surveys the state-of-the-art research efforts to enable IoT-based smart environments. We categorize and classify the literature by devising a taxonomy based on communication enablers, network types, technologies, local area wireless standards, objectives, and characteristics. Moreover, the article highlights the unprecedented opportunities brought about by IoT-based smart environments and their effect on human life. Some reported case studies from different enterprises are also presented. Finally, we discuss open research challenges for enabling IoT-based smart environments. © 2016 IEEE.
Mobile ad hoc cloud : a survey
- Authors: Yaqoob, Ibra , Ahmed, Ejaz , Gani, Abdullah , Mokhtar, Salimah , Imran, Muhammad , Guizani, Sghaier
- Date: 2016
- Type: Text , Journal article
- Relation: Wireless Communications and Mobile Computing Vol. 16, no. 16 (2016), p. 2572-2589
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- Description: The unabated flurry of research activities to augment various mobile devices in terms of compute-intensive task execution by leveraging heterogeneous resources of available devices in the local vicinity has created a new research domain called mobile ad hoc cloud (MAC) or mobile cloud. It is a new type of mobile cloud computing (MCC). MAC is deemed to be a candidate blueprint for future compute-intensive applications with the aim of delivering high functionalities and rich impressive experience to mobile users. However, MAC is yet in its infancy, and a comprehensive survey of the domain is still lacking. In this paper, we survey the state-of-the-art research efforts carried out in the MAC domain. We analyze several problems inhibiting the adoption of MAC and review corresponding solutions by devising a taxonomy. Moreover, MAC roots are analyzed and taxonomized as architectural components, applications, objectives, characteristics, execution model, scheduling type, formation technologies, and node types. The similarities and differences among existing proposed solutions by highlighting the advantages and disadvantages are also investigated. We also compare the literature based on objectives. Furthermore, our study advocates that the problems stem from the intrinsic characteristics of MAC by identifying several new principles. Lastly, several open research challenges such as incentives, heterogeneity-ware task allocation, mobility, minimal data exchange, and security and privacy are presented as future research directions. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.