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The conventional diagnostic process and tools of cardiovascular autonomic neuropathy (CAN) can easily identify the two main categories of the condition: severe/definite CAN and normal/healthy without CAN. Conventional techniques encounter significant challenges when identifying CAN in its early or atypical stages due to the inherent imbalanced and incompleteness condition in the collected clinical multimodal data, including electrocardiogram (ECG) data from ECG sensors, blood chemistry, podiatry, and endocrinology features. Therefore, most detection tools and techniques are limited to binary CAN classification. However, early diagnosis of CAN or diagnosis of the atypical stages of CAN is more important than the diagnosis of severe CAN, which, in fact, is easily identifiable with a few diagnostic reports. In this paper, we propose a novel multi-class classification approach for timely CAN detection. The proposed classification algorithm develops a multistage fusion model by combining feature selection and multimodal feature fusion techniques. The proposed method develops a performance criterion-based feature selection technique to guarantee highly significant features. A multimodal feature fusion technique was developed using deep learning feature fusion and selected original features. The experimental results obtained from testing with a large CAN dataset indicate that the proposed algorithm significantly improved the diagnostic accuracy of CAN compared to conventional Ewing battery features. The algorithm also identified the early or atypical stages of CAN with an AUC score of 0.931 using leave-one-out cross-validation.
Rafiul Hassan; Shamsul Huda; Mohammad Mehedi Hassan; Jemal Abawajy; Ahmed Alsanad; Giancarlo Fortino. Early detection of cardiovascular autonomic neuropathy: A multi-class classification model based on feature selection and deep learning feature fusion. Information Fusion 2021, 77, 70 -80.
AMA StyleRafiul Hassan, Shamsul Huda, Mohammad Mehedi Hassan, Jemal Abawajy, Ahmed Alsanad, Giancarlo Fortino. Early detection of cardiovascular autonomic neuropathy: A multi-class classification model based on feature selection and deep learning feature fusion. Information Fusion. 2021; 77 ():70-80.
Chicago/Turabian StyleRafiul Hassan; Shamsul Huda; Mohammad Mehedi Hassan; Jemal Abawajy; Ahmed Alsanad; Giancarlo Fortino. 2021. "Early detection of cardiovascular autonomic neuropathy: A multi-class classification model based on feature selection and deep learning feature fusion." Information Fusion 77, no. : 70-80.
In an increasingly connected cyberspace where cloud-enabled Internet of things (IoT) applications are exploding, ensuring trust and privacy are two major requirements but often neglected. In this paper, we discuss a lightweight authenticated encryption for simultaneously protecting authenticity and privacy of messages in the cloud-enabled IoT platforms.
Zainab S. AlJabri; Jemal H. Abawajy; Shamsul Huda. Lightweight Authenticated Encryption for Cloud-assisted IoT Applications. Lecture Notes in Electrical Engineering 2021, 295 -299.
AMA StyleZainab S. AlJabri, Jemal H. Abawajy, Shamsul Huda. Lightweight Authenticated Encryption for Cloud-assisted IoT Applications. Lecture Notes in Electrical Engineering. 2021; ():295-299.
Chicago/Turabian StyleZainab S. AlJabri; Jemal H. Abawajy; Shamsul Huda. 2021. "Lightweight Authenticated Encryption for Cloud-assisted IoT Applications." Lecture Notes in Electrical Engineering , no. : 295-299.
Automatic information extraction from online published scientific documents is useful in various applications such as tagging, web indexing and search engine optimization. As a result, automatic information extraction has become among the hottest areas of research in text mining. Although various information extraction techniques have been proposed in the literature, their efficiency demands domain specific documents with static and well-defined format. Furthermore, their accuracy is challenged with a slight modification in the format. To overcome these issues, a novel ontological framework for information extraction (OFIE) using fuzzy rule-base (FRB) and word sense disambiguation (WSD) is proposed. The proposed approach is validated with a significantly wider document domains sourced from well-known publishing services such as IEEE, ACM, Elsevier, and Springer. We have also compared the proposed information extraction approach against state-of-the-art techniques. The results of the experiment show that the proposed approach is less sensitive to changes in the document format and has a significantly better average accuracy of 89.14% and F-score as 89%.
Gohar Zaman; Hairulnizam Mahdin; Khalid Hussain; Atta- Ur- Rahman; Jemal Abawajy; Salama A. Mostafa. An Ontological Framework for Information Extraction From Diverse Scientific Sources. IEEE Access 2021, 9, 42111 -42124.
AMA StyleGohar Zaman, Hairulnizam Mahdin, Khalid Hussain, Atta- Ur- Rahman, Jemal Abawajy, Salama A. Mostafa. An Ontological Framework for Information Extraction From Diverse Scientific Sources. IEEE Access. 2021; 9 ():42111-42124.
Chicago/Turabian StyleGohar Zaman; Hairulnizam Mahdin; Khalid Hussain; Atta- Ur- Rahman; Jemal Abawajy; Salama A. Mostafa. 2021. "An Ontological Framework for Information Extraction From Diverse Scientific Sources." IEEE Access 9, no. : 42111-42124.
Malicious software (“malware”) has become one of the serious cybersecurity issues in Android ecosystem. Given the fast evolution of Android malware releases, it is practically not feasible to manually detect malware apps in the Android ecosystem. As a result, machine learning has become a fledgling approach for malware detection. Since machine learning performance is largely influenced by the availability of high quality and relevant features, feature selection approaches play key role in machine learning based detection of malware. In this paper, we formulate the feature selection problem as a quadratic programming problem and analyse how commonly used filter-based feature selection methods work with emphases on Android malware detection. We compare and contrast several feature selection methods along several factors including the composition of relevant features selected. We empirically evaluate the predictive accuracy of the feature subset selection algorithms and compare their predictive accuracy and the execution time using several learning algorithms. The results of the experiments confirm that feature selection is necessary for improving accuracy of the learning models as well decreasing the run time. The results also show that the performance of the feature selection algorithms vary from one learning algorithm to another and no one feature selection approach performs better than the other approaches all the time.
Jemal Abawajy; Abdulbasit Darem; Asma A. Alhashmi. Feature Subset Selection for Malware Detection in Smart IoT Platforms. Sensors 2021, 21, 1374 .
AMA StyleJemal Abawajy, Abdulbasit Darem, Asma A. Alhashmi. Feature Subset Selection for Malware Detection in Smart IoT Platforms. Sensors. 2021; 21 (4):1374.
Chicago/Turabian StyleJemal Abawajy; Abdulbasit Darem; Asma A. Alhashmi. 2021. "Feature Subset Selection for Malware Detection in Smart IoT Platforms." Sensors 21, no. 4: 1374.
In a typical formulation of decision making under uncertainty, a decision maker must choose a single optimal option among many possible options. However, the problem of selecting a unique and an optimal choice has remained a significant challenge to solve. In this paper, we propose a new interval-valued intuitionistic fuzzy soft set based decision making approach to address this problem. The proposed approach is based on the choice value and score value of membership/non- membership degrees. Furthermore, three parameter reduction algorithms are proposed. We apply the proposed approaches on a real application to demonstrate their working and effectiveness. We also compare the proposed approach against the adjustable interval-valued intuitionistic fuzzy soft sets approach and show that the proposed approach has lower computation overhead and enable a decision maker to choose top options to make proper decision.
Xiuqin Ma; Hongwu Qin; Jemal Abawajy. Interval-valued intuitionistic fuzzy soft sets based decision making and parameter reduction. IEEE Transactions on Fuzzy Systems 2020, PP, 1 -1.
AMA StyleXiuqin Ma, Hongwu Qin, Jemal Abawajy. Interval-valued intuitionistic fuzzy soft sets based decision making and parameter reduction. IEEE Transactions on Fuzzy Systems. 2020; PP (99):1-1.
Chicago/Turabian StyleXiuqin Ma; Hongwu Qin; Jemal Abawajy. 2020. "Interval-valued intuitionistic fuzzy soft sets based decision making and parameter reduction." IEEE Transactions on Fuzzy Systems PP, no. 99: 1-1.
Fog computing is an extension of cloud computing that offers computing, storage and communication resources near the network edge, which makes it an ideal platform for processing latency-sensitive and compute-intensive tasks. However, efficient task execution in a fog platform is a challenging problem since fog nodes are loosely interconnected, highly dynamic, heterogeneous, and prone to failures. Therefore, a task scheduling algorithm that ensures reliable execution of the tasks while optimising response time is paramount. To address this challenge, we propose a new Dynamic Fault Tolerant Learning Automata (DFTLA) task scheduling approach. DFTLA determines an efficient assignment of the tasks to the fog nodes based on variable-structure learning automata. We evaluated the proposed DFTLA scheduler and compared its performance with three baseline methods. The results of the experiments show that the proposed algorithm ensures reliable execution of the tasks while optimising response time and energy consumption. Moreover, the proposed approach outperforms the baseline algorithms in all performance evaluation criteria.
Sara Ghanavati; Jemal Abawajy; Davood Izadi. Automata-based Dynamic Fault Tolerant Task Scheduling Approach in Fog Computing. IEEE Transactions on Emerging Topics in Computing 2020, PP, 1 -1.
AMA StyleSara Ghanavati, Jemal Abawajy, Davood Izadi. Automata-based Dynamic Fault Tolerant Task Scheduling Approach in Fog Computing. IEEE Transactions on Emerging Topics in Computing. 2020; PP (99):1-1.
Chicago/Turabian StyleSara Ghanavati; Jemal Abawajy; Davood Izadi. 2020. "Automata-based Dynamic Fault Tolerant Task Scheduling Approach in Fog Computing." IEEE Transactions on Emerging Topics in Computing PP, no. 99: 1-1.
Fog computing has become a platform of choice for executing emerging applications with low latency requirements. Since the devices in fog computing tend to be resource constraint and highly distributed, how fog computing resources can be effectively utilized for executing delay-sensitive tasks is a fundamental challenge. To address this problem, we propose and evaluate a new task scheduling algorithm with the aim of reducing the total system makespan and energy consumption for fog computing platform. The proposed approach consists of two key components: 1) a new bio-inspired optimization approach called Ant Mating Optimization (AMO) and 2) optimized distribution of a set of tasks among the fog nodes within proximity. The objective is to find an optimal trade-off between the system makespan and the consumed energy required by the fog computing services, established by end user devices. Our empirical performance evaluation results demonstrate that the proposed approach outperforms the bee life algorithm, traditional particle swarm optimization and genetic algorithm in terms of makespan and consumed energy.
Sara Ghanavati; Jemal H. Abawajy; Davood Izadi. An Energy Aware Task Scheduling Model Using Ant-Mating Optimization in Fog Computing Environment. IEEE Transactions on Services Computing 2020, PP, 1 -1.
AMA StyleSara Ghanavati, Jemal H. Abawajy, Davood Izadi. An Energy Aware Task Scheduling Model Using Ant-Mating Optimization in Fog Computing Environment. IEEE Transactions on Services Computing. 2020; PP (99):1-1.
Chicago/Turabian StyleSara Ghanavati; Jemal H. Abawajy; Davood Izadi. 2020. "An Energy Aware Task Scheduling Model Using Ant-Mating Optimization in Fog Computing Environment." IEEE Transactions on Services Computing PP, no. 99: 1-1.
Efficient big data analysis is critical to support applications or services in Internet of Things (IoT) system, especially for the time-intensive services. Hence, the data center may host heterogeneous big data analysis tasks for multiple IoT systems. It is a challenging problem since the data centers usually need to schedule a large number of periodic or online tasks in a short time. In this paper, we investigate the heterogeneous task scheduling problem to reduce the global task execution time, which is also an efficient method to reduce energy consumption for data centers. We establish the task execution for heterogeneous tasks respectively based on the data locality feature, which also indicate the relationship among the tasks, data blocks and servers. We propose a heterogeneous task scheduling algorithm with data migration. The core idea of the algorithm is to maximize the efficiency by comparing the cost between remote task execution and data migration, which could improve the data locality and reduce task execution time. We conduct extensive simulations and the experimental results show that our algorithm has better performance than the traditional methods, and data migration actually works to reduce th overall task execution time. The algorithm also shows acceptable fairness for the heterogeneous tasks.
Xin Li; Liangyuan Wang; Jemal H. Abawajy; Xiaolin Qin; Giovanni Pau; IlSun You. Data-Intensive Task Scheduling for Heterogeneous Big Data Analytics in IoT System. Energies 2020, 13, 4508 .
AMA StyleXin Li, Liangyuan Wang, Jemal H. Abawajy, Xiaolin Qin, Giovanni Pau, IlSun You. Data-Intensive Task Scheduling for Heterogeneous Big Data Analytics in IoT System. Energies. 2020; 13 (17):4508.
Chicago/Turabian StyleXin Li; Liangyuan Wang; Jemal H. Abawajy; Xiaolin Qin; Giovanni Pau; IlSun You. 2020. "Data-Intensive Task Scheduling for Heterogeneous Big Data Analytics in IoT System." Energies 13, no. 17: 4508.
The challenges of the conventional cloud computing paradigms motivated the emergence of the next generation cloud computing architectures. The emerging cloud computing architectures generate voluminous amount of data that are beyond the capability of the shallow intelligent algorithms to process. Deep learning algorithms, with their ability to process large-scale datasets, have recently started gaining tremendous attentions from researchers to solve problem in the emerging cloud computing architectures. However, no comprehensive literature review exists on the applications of deep learning architectures to solve complex problems in emerging cloud computing architectures. To fill this gap, we conducted a comprehensive literature survey on the applications of deep learning architectures in emerging cloud computing architectures. The survey shows that the adoption of deep learning architectures in emerging cloud computing architectures are increasingly becoming an interesting research area. We introduce a new taxonomy of deep learning architectures for emerging cloud computing architectures and provide deep insights into the current state-of-the-art active research works on deep learning to solve complex problems in emerging cloud computing architectures. The synthesis and analysis of the articles as well as their limitation are presented. A lot of challenges were identified in the literature and new future research directions to solve the identified challenges are presented. We believed that this article can serve as a reference guide to new researchers and an update for expert researchers to explore and develop more deep learning applications in the emerging cloud computing architectures.
Fatsuma Jauroac; Haruna Chiromab; Abdulsalam Y. Gital; Mubarak Almutairid; Shafi’I M. Abdulhamid; Jemal H. Abawajy. Deep learning architectures in emerging cloud computing architectures: Recent development, challenges and next research trend. Applied Soft Computing 2020, 96, 106582 .
AMA StyleFatsuma Jauroac, Haruna Chiromab, Abdulsalam Y. Gital, Mubarak Almutairid, Shafi’I M. Abdulhamid, Jemal H. Abawajy. Deep learning architectures in emerging cloud computing architectures: Recent development, challenges and next research trend. Applied Soft Computing. 2020; 96 ():106582.
Chicago/Turabian StyleFatsuma Jauroac; Haruna Chiromab; Abdulsalam Y. Gital; Mubarak Almutairid; Shafi’I M. Abdulhamid; Jemal H. Abawajy. 2020. "Deep learning architectures in emerging cloud computing architectures: Recent development, challenges and next research trend." Applied Soft Computing 96, no. : 106582.
Particle swarm optimization (PSO) algorithms have low-quality initial particle swarm, which is generated by a random method when handling the problem of task scheduling in networked data centres. Such algorithms also fall easily into local optimum when searching for the optimal solution. To address these problems, this study proposes combining opposition-based learning (OBL) and tentative perception (TP) with PSO; the proposed method is called OBL–TP–PSO. This algorithm uses reverse learning to generate the initial population, such that the quality of the initial particle swarm can be improved. Before the particle speed and location are updated, the TP method is used to search for the individual optimum around each particle, thereby reducing the possibility of missing the potential optimal solution during the process of searching. In this manner, the problem in which the PSO algorithm easily falls into the local optimal is effectively solved. To evaluate the performance of the proposed algorithm, simulation experiments are performed on CloudSim toolkit. Experimental results show that in comparison with other algorithms (namely, Min-Min, Max-Min and PSO algorithm), the proposed OBL–TP–PSO algorithm has better performance in terms of the total execution time, load balancing and quality of service.
Zhou Zhou; Fangmin Li; Jemal H. Abawajy; Chaochao Gao. Improved PSO Algorithm Integrated With Opposition-Based Learning and Tentative Perception in Networked Data Centres. IEEE Access 2020, 8, 55872 -55880.
AMA StyleZhou Zhou, Fangmin Li, Jemal H. Abawajy, Chaochao Gao. Improved PSO Algorithm Integrated With Opposition-Based Learning and Tentative Perception in Networked Data Centres. IEEE Access. 2020; 8 (99):55872-55880.
Chicago/Turabian StyleZhou Zhou; Fangmin Li; Jemal H. Abawajy; Chaochao Gao. 2020. "Improved PSO Algorithm Integrated With Opposition-Based Learning and Tentative Perception in Networked Data Centres." IEEE Access 8, no. 99: 55872-55880.
Discount coefficient is an efficient method to address conflicting evidence combination in Dempster-Shafer evidence theory. However, how to determine the discount coefficient of each evidence is an open issue. In this paper, considering both the influence of the amount of information contained in the evidence itself and the fuzziness of the evidence based on the negation of basic belief assignment, a new discount coefficient is presented. The proposed discount coefficient is a fractional form. The numerator is Deng entropy, and the denominator is entropy difference between initial body of evidence (BOE) and its negation. The more information contained in the evidence, the more value is obtained. And the lower fuzziness of evidence, the less value is obtained. A numerical example is given to illustrate the application of this proposed method in the combination of highly conflicting evidence.
Shanshan Li; Fuyuan Xiao; Jemal H. Abawajy. Conflict Management of Evidence Theory Based on Belief Entropy and Negation. IEEE Access 2020, 8, 37766 -37774.
AMA StyleShanshan Li, Fuyuan Xiao, Jemal H. Abawajy. Conflict Management of Evidence Theory Based on Belief Entropy and Negation. IEEE Access. 2020; 8 (99):37766-37774.
Chicago/Turabian StyleShanshan Li; Fuyuan Xiao; Jemal H. Abawajy. 2020. "Conflict Management of Evidence Theory Based on Belief Entropy and Negation." IEEE Access 8, no. 99: 37766-37774.
The immense popularity of Android makes it a primary target of malicious attackers and developers which brings a significant threat from malicious applications for android users through the escalation of the abuse of android permissions and inter-component communication (ICC) mechanism. Therefore, protecting android users from malicious developers and applications is crucial for Android market and communities. As malicious applications can hide their malicious behavior and change the behaviors frequently by abusing the android’s ICC mechanism and related vulnerabilities, it is a challenging task to identify them accurately before it becomes a prevalent reason for users’ privacy and data breach. Therefore, it is essential to develop such a malware detection engine that will ensure zero-day detection. In this research, we propose an adaptive framework which can learn the behavior of malware from the usage of permissions and their escalations. For our adaptive framework, we proposed two different detection models using deep learning and semi-supervised approaches. The proposed detection models can extract knowledge from unlabeled apps to identify the new malicious behavior using the unsupervised training nature of deep learning and clustering techniques and their integration to the supervised detection engine. Thus, our adaptive framework learns about new malicious apps and their behavior without supervised labeling by manual expert and can ensure zero-day protection. The proposed detection models have been tested on a real mobile malware test-bed and data set. The Experimental results show that the deep learning and semi-supervised based models achieve 99.024% of accuracies, more effective for zero-day protection and outperform other existing supervised detection engines.
Shaila Sharmeen; Shamsul Huda; Jemal Abawajy; Mohammad Mehedi Hassan. An adaptive framework against android privilege escalation threats using deep learning and semi-supervised approaches. Applied Soft Computing 2020, 89, 106089 .
AMA StyleShaila Sharmeen, Shamsul Huda, Jemal Abawajy, Mohammad Mehedi Hassan. An adaptive framework against android privilege escalation threats using deep learning and semi-supervised approaches. Applied Soft Computing. 2020; 89 ():106089.
Chicago/Turabian StyleShaila Sharmeen; Shamsul Huda; Jemal Abawajy; Mohammad Mehedi Hassan. 2020. "An adaptive framework against android privilege escalation threats using deep learning and semi-supervised approaches." Applied Soft Computing 89, no. : 106089.
Fuyuan Xiao; Zili Zhang; Jemal Abawajy. Workflow scheduling in distributed systems under fuzzy environment. Journal of Intelligent & Fuzzy Systems 2019, 37, 5323 -5333.
AMA StyleFuyuan Xiao, Zili Zhang, Jemal Abawajy. Workflow scheduling in distributed systems under fuzzy environment. Journal of Intelligent & Fuzzy Systems. 2019; 37 (4):5323-5333.
Chicago/Turabian StyleFuyuan Xiao; Zili Zhang; Jemal Abawajy. 2019. "Workflow scheduling in distributed systems under fuzzy environment." Journal of Intelligent & Fuzzy Systems 37, no. 4: 5323-5333.
Malicious applications can be a security threat to Cyber-physical systems as the Cyber-physical systems are composed of heterogeneous distributed systems and mostly depends on the internet, ICT services and products. The usage of ICT products and the services gives the opportunity of less expensive data collection, intelligent control and decision systems using automated data mining tools. Cyber-physical systems become exposed to the internet and the public networks as it has integrated to the ICT networks for easy automated options. Cyber-attacks can lead functional failure, blackouts, energy theft, data theft etc. and this will be critical security concern of Cyber-physical systems. At present, the mobile devices are replacing the pc environment and become a key element of Internet of Things. Therefore, it is essential to develop such a malware detection engine that will identify the mobile malware and reduce the spreading of the malicious code through mobile devices. This research work will identify the malware by incorporating semi-supervised approach and deep learning. The original and significant contributions are to propose an effective malware detection model by incorporating semi-supervised approach and deep learning, to implement the model using parallel processing and to evaluate the performance of the model using recent dataset. Here we have used the permission and the API call as the features. The proposed method has been tested on the real mobile malware data set and it shows improvement in accuracy. The Experimental results show that the deep learning along with semi-supervised method will be an effective way to identify the malware and it outperforms other detection methods.
Shaila Sharmeen; Shamsul Huda; Jemal Abawajy. Identifying Malware on Cyber Physical Systems by incorporating Semi-Supervised Approach and Deep Learning. IOP Conference Series: Earth and Environmental Science 2019, 322, 012012 .
AMA StyleShaila Sharmeen, Shamsul Huda, Jemal Abawajy. Identifying Malware on Cyber Physical Systems by incorporating Semi-Supervised Approach and Deep Learning. IOP Conference Series: Earth and Environmental Science. 2019; 322 (1):012012.
Chicago/Turabian StyleShaila Sharmeen; Shamsul Huda; Jemal Abawajy. 2019. "Identifying Malware on Cyber Physical Systems by incorporating Semi-Supervised Approach and Deep Learning." IOP Conference Series: Earth and Environmental Science 322, no. 1: 012012.
Wireless multimedia sensor networks (WMSNs) are capable of collecting multimedia events, such as traffic accidents and wildlife tracking, as well as scalar data. As a result, WMSNs are receiving a great deal of attention both from industry and academic communities. However, multimedia applications tend to generate high volume network traffic, which results in very high energy consumption. As energy is a prime resource in WMSN, an efficient routing algorithm that effectively deals with the dynamic topology of WMSN but also prolongs the lifetime of WMSN is required. To this end, we propose a routing algorithm that combines dynamic cluster formation, cluster head selection, and multipath routing formation for data communication to reduce energy consumption as well as routing overheads. The proposed algorithm uses a genetic algorithm (GA)-based meta-heuristic optimization to dynamically select the best path based on the cost function with the minimum distance and the least energy dissipation. We carried out an extensive performance analysis of the proposed algorithm and compared it with three other routing protocols. The results of the performance analysis showed that the proposed algorithm outperformed the three other routing protocols.
Addisalem Genta; D. K. Lobiyal; Jemal H. Abawajy; K.Lobiyal. Energy Efficient Multipath Routing Algorithm for Wireless Multimedia Sensor Network. Sensors 2019, 19, 3642 .
AMA StyleAddisalem Genta, D. K. Lobiyal, Jemal H. Abawajy, K.Lobiyal. Energy Efficient Multipath Routing Algorithm for Wireless Multimedia Sensor Network. Sensors. 2019; 19 (17):3642.
Chicago/Turabian StyleAddisalem Genta; D. K. Lobiyal; Jemal H. Abawajy; K.Lobiyal. 2019. "Energy Efficient Multipath Routing Algorithm for Wireless Multimedia Sensor Network." Sensors 19, no. 17: 3642.
Conventional isolated cyber–physical systems (CPS) based industrial networks are increasingly being integrated with modern corporate information technology (IT) network. Therefore, cyber-attacks on CPS are increasing enormously and this could result in a massive damage to the machines themselves or the humans who interact with them. Malware has been one of the major source of attacks and threats to the CPS networks and computer systems. The high growth and the variety of malware variants such as Internet worms, Trojan horses and computer viruses requires periodic update of the database. Traditional malware system fulfil this requirement by manual effort from the experts though signature generation. However manual update could result into potential drawback for integrity and availability of services provided by CPS systems and protection in real-time. Machine learning technique is a natural choice to address the malware challenge for CPSs, since it can easily model and discover the underlying patterns from large-scale data sets. This paper introduces intelligent models and algorithms that can extract behavioural features and inherent attack patterns from the existing malware data, then integrates the behavioural indicators into the detection system. The main contribution of the paper is that the proposed models do not require periodic manual effort to update the database of the detection engine. We have introduced semi-supervised models using unsupervised learning including independent component analysis (ICA), global K-means clustering and multivariate exponentially weighted moving average (MEWMA) for extracting behavioural indicators which clusters the malware. Then the extracted geometric information of the clusters and hoteling T2 of the behavioural indicators from MEWMA are incorporated into the database of existing detection system which are used with support vector machine (SVM) based supervised system. This enables the detection system to update the dynamic behavioural patterns of new malware automatically. The performances of developed semi-supervised models have been verified using malware data for both static and dynamic characteristics of malware. The summary of our experimental results demonstrate that the combination of unsupervised and supervised learning can successfully extracts behavioural indicators automatically from new malware. Performance comparison from experimental results summarize that the semi-supervised models can detect more accurately than the existing supervised models where accuracies are increased up to 100% for SVM and random forest based semi-supervised models.
Shamsul Huda; Jemal Abawajy; Baker Al-Rubaie; Lei Pan; Mohammad Mehedi Hassan. Automatic extraction and integration of behavioural indicators of malware for protection of cyber–physical networks. Future Generation Computer Systems 2019, 101, 1247 -1258.
AMA StyleShamsul Huda, Jemal Abawajy, Baker Al-Rubaie, Lei Pan, Mohammad Mehedi Hassan. Automatic extraction and integration of behavioural indicators of malware for protection of cyber–physical networks. Future Generation Computer Systems. 2019; 101 ():1247-1258.
Chicago/Turabian StyleShamsul Huda; Jemal Abawajy; Baker Al-Rubaie; Lei Pan; Mohammad Mehedi Hassan. 2019. "Automatic extraction and integration of behavioural indicators of malware for protection of cyber–physical networks." Future Generation Computer Systems 101, no. : 1247-1258.
Cloud computing offers software as a service (SaaS), infrastructure as a service (IaaS), and platform as service (PaaS) on pay-as-you-go model over the Internet. Although Cloud have been attractive to businesses and other domains to accommodate their increasing demand for computational power on demand bases, the high energy consumption of Cloud data centers has recently become a serious issue. The high energy consumption not only causes the energy wastes and system instability but also generates low return on the investment (ROI) and adverse effects on the environment. Therefore, it is extremely necessary to reduce energy consumption while meeting the quality of service (QoS). This chapter presents a fine-grained energy consumption model and analyzes its effectiveness in energy consumption of data centers.
Zhou Zhou; Jemal H. Abawajy; Fangmin Li. Analysis of Energy Consumption Model in Cloud Computing Environments. Smart and Sustainable Planning for Cities and Regions 2019, 195 -215.
AMA StyleZhou Zhou, Jemal H. Abawajy, Fangmin Li. Analysis of Energy Consumption Model in Cloud Computing Environments. Smart and Sustainable Planning for Cities and Regions. 2019; ():195-215.
Chicago/Turabian StyleZhou Zhou; Jemal H. Abawajy; Fangmin Li. 2019. "Analysis of Energy Consumption Model in Cloud Computing Environments." Smart and Sustainable Planning for Cities and Regions , no. : 195-215.
Novel computing paradigm realized by cloud computing and virtualization technologies paved the way for commoditization of computing resources. Clouds and their federation made inexhaustible computing resources leveraging ample scope in producing opportunities and productivity with provision for on-demand resources in pay as you go fashion. With the wealth of resources, big data and big data stream processing (BDSP), big data analytics became a reality now. Two stakeholders such as cloud service providers and cloud users are mainly affected if the cloud infrastructure fails to deliver intended services to the satisfaction end users. Resource optimization has been an active research topic in cloud computing to overcome this problem. It is more so with the emergence of Software Defined Networking (SDN). Resource reservation and dynamic resource allocation are two approaches found in the literature. Dynamic resource allocation is highly preferred optimization problem considered in this paper. BDSP needs highly reliable and automated resource optimization in the context of increased big data streaming workloads to be processed by real-world applications. In this paper, we proposed a methodology for SDN enabled BDSP in public cloud for resource optimization. We defined a mathematical model and proposed an algorithm to achieve it. CloudSimSDN is used to build a prototype application that demonstrates proof of the concept. Our experimental results reveal the utility of SDN based approach for resource optimization in a cloud in the presence of BDSP by decoupling data forwarding and network controlling.
Ahmed Al-Mansoori; Jemal Abawajy; Morshed Chowdhury. SDN enabled BDSP in public cloud for resource optimization. Wireless Networks 2018, 1 -11.
AMA StyleAhmed Al-Mansoori, Jemal Abawajy, Morshed Chowdhury. SDN enabled BDSP in public cloud for resource optimization. Wireless Networks. 2018; ():1-11.
Chicago/Turabian StyleAhmed Al-Mansoori; Jemal Abawajy; Morshed Chowdhury. 2018. "SDN enabled BDSP in public cloud for resource optimization." Wireless Networks , no. : 1-11.
Zhou Zhou; Jemal Abawajy; Morshed Chowdhury; Zhigang Hu; Keqin Li; Hongbing Cheng; Abdulhameed A. Alelaiwi; Fangmin Li. Minimizing SLA violation and power consumption in Cloud data centers using adaptive energy-aware algorithms. Future Generation Computer Systems 2018, 86, 836 -850.
AMA StyleZhou Zhou, Jemal Abawajy, Morshed Chowdhury, Zhigang Hu, Keqin Li, Hongbing Cheng, Abdulhameed A. Alelaiwi, Fangmin Li. Minimizing SLA violation and power consumption in Cloud data centers using adaptive energy-aware algorithms. Future Generation Computer Systems. 2018; 86 ():836-850.
Chicago/Turabian StyleZhou Zhou; Jemal Abawajy; Morshed Chowdhury; Zhigang Hu; Keqin Li; Hongbing Cheng; Abdulhameed A. Alelaiwi; Fangmin Li. 2018. "Minimizing SLA violation and power consumption in Cloud data centers using adaptive energy-aware algorithms." Future Generation Computer Systems 86, no. : 836-850.
Energy efficiency is one of the critical challenges in wireless sensor networks because the nodes in such networks have limited resources. Therefore, they should be managed efficiently in order to exploit the network's functionality for a longer period of time. Topology control mechanisms can help the nodes to leverage their resources efficiently. Several topology control protocols for WSNs have been proposed to decrease energy consumption of the nodes and increase the network capacity. Leveraging a lower transmission range can help the nodes to mitigate their energy consumptions. In this paper, we propose a topology control protocol based on learning automaton, which is named LBLATC. The learning automaton of every sensor node chooses the proper transmission range of the node using the reinforcement signal which is produced by the learning automaton of neighbor sensor nodes. The simulation runs carried out to verify the performance of the proposed protocol. It acts on average 15% better than current state-of-art in term of selecting a proper transmission range.
Mahmood Javadi; Habib Mostafaei; Morshed U. Chowdhurry; Jemal H. Abawajy. Learning automaton based topology control protocol for extending wireless sensor networks lifetime. Journal of Network and Computer Applications 2018, 122, 128 -136.
AMA StyleMahmood Javadi, Habib Mostafaei, Morshed U. Chowdhurry, Jemal H. Abawajy. Learning automaton based topology control protocol for extending wireless sensor networks lifetime. Journal of Network and Computer Applications. 2018; 122 ():128-136.
Chicago/Turabian StyleMahmood Javadi; Habib Mostafaei; Morshed U. Chowdhurry; Jemal H. Abawajy. 2018. "Learning automaton based topology control protocol for extending wireless sensor networks lifetime." Journal of Network and Computer Applications 122, no. : 128-136.