Multi-sensor fusion based on multiple classifier systems for human activity identification

Multimodal sensors in healthcare applications have been increasingly researched because it facilitates automatic and comprehensive monitoring of human behaviors, high-intensity sports management, energy expenditure estimation, and postural detection. Recent studies have shown the importance of multi-sensor fusion to achieve robustness, high-performance generalization, provide diversity and tackle challenging issue that maybe difficult with single sensor values. The aim of this study is to propose an innovative multi-sensor fusion framework to improve human activity detection performances and reduce misrecognition rate. The study proposes a multi-view ensemble algorithm to integrate predicted values of different motion sensors. To this end, computationally efficient classification algorithms such as decision tree, logistic regression and k-Nearest Neighbors were used to implement diverse, flexible and dynamic human activity detection systems. To provide compact feature vector representation, we studied hybrid bio-inspired evolutionary search algorithm and correlation-based feature selection method and evaluate their impact on extracted feature vectors from individual sensor modality. Furthermore, we utilized Synthetic Over-sampling minority Techniques (SMOTE) algorithm to reduce the impact of class imbalance and improve performance results. With the above methods, this paper provides unified framework to resolve major challenges in human activity identification. The performance results obtained using two publicly available datasets showed significant improvement over baseline methods in the detection of specific activity details and reduced error rate. The performance results of our evaluation showed 3% to 24% improvement in accuracy, recall, precision, F-measure and detection ability (AUC) compared to single sensors and feature-level fusion. The benefit of the proposed multi-sensor fusion is the ability to utilize distinct feature characteristics of individual sensor and multiple classifier systems to improve recognition accuracy. In addition, the study suggests a promising potential of hybrid feature selection approach, diversity-based multiple classifier systems to improve mobile and wearable sensor-based human activity detection and health monitoring system. - 2019, The Author(s).This research is supported by University of Malaya BKP Special Grant no vote BKS006-2018.Scopu

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. However, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems have introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network and application security for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to effectively secure the IoT ecosystem. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory novelty to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML methods and recent advances in DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions. 1998-2012 IEEE.Qatar Foundation;Qatar National Research FundScopu

Identifying the influential spreaders in multilayer interactions of online social networks

Online social networks (OSNs) portray a multi-layer of interactions through which users become a friend, information is propagated, ideas are shared, and interaction is constructed within an OSN. Identifying the most influential spreaders in a network is a significant step towards improving the use of existing resources to speed up the spread of information for application such as viral marketing or hindering the spread of information for application like virus blocking and rumor restraint. Users communications facilitated by OSNs could confront the temporal and spatial limitations of traditional communications in an exceptional way, thereby presenting new layers of social interactions, which coincides and collaborates with current interaction layers to redefine the multiplex OSN. In this paper, the effects of different topological network structure on influential spreaders identification are investigated. The results analysis concluded that improving the accuracy of influential spreaders identification in OSNs is not only by improving identification algorithms but also by developing a network topology that represents the information diffusion well. Moreover, in this paper a topological representation for an OSN is proposed which takes into accounts both multilayers interactions as well as overlaying links as weight. The measurement results are found to be more reliable when the identification algorithms are applied to proposed topological representation compared when these algorithms are applied to single layer representations

The rise of ransomware and emerging security challenges in the Internet of Things

With the increasing miniaturization of smartphones, computers, and sensors in the Internet of Things (IoT) paradigm, strengthening the security and preventing ransomware attacks have become key concerns. Traditional security mechanisms are no longer applicable because of the involvement of resource-constrained devices, which require more computation power and resources. This paper presents the ransomware attacks and security concerns in IoT. We initially discuss the rise of ransomware attacks and outline the associated challenges. Then, we investigate, report, and highlight the state-of-the-art research efforts directed at IoT from a security perspective. A taxonomy is devised by classifying and categorizing the literature based on important parameters (e.g., threats, requirements, IEEE standards, deployment level, and technologies). Furthermore, a few credible case studies are outlined to alert people regarding how seriously IoT devices are vulnerable to threats. We enumerate the requirements that need to be met for securing IoT. Several indispensable open research challenges (e.g., data integrity, lightweight security mechanisms, lack of security software's upgradability and patchability features, physical protection of trillions of devices, privacy, and trust) are identified and discussed. Several prominent future research directions are provided

Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey and Future Directions

Unmanned aerial vehicles (UAVs) have recently rapidly grown to facilitate a wide range of innovative applications that can fundamentally change the way cyber-physical systems (CPSs) are designed. CPSs are a modern generation of systems with synergic cooperation between computational and physical potentials that can interact with humans through several new mechanisms. The main advantages of using UAVs in CPS application is their exceptional features, including their mobility, dynamism, effortless deployment, adaptive altitude, agility, adjustability, and effective appraisal of real-world functions anytime and anywhere. Furthermore, from the technology perspective, UAVs are predicted to be a vital element of the development of advanced CPSs. Therefore, in this survey, we aim to pinpoint the most fundamental and important design challenges of multi-UAV systems for CPS applications. We highlight key and versatile aspects that span the coverage and tracking of targets and infrastructure objects, energy-efficient navigation, and image analysis using machine learning for fine-grained CPS applications. Key prototypes and testbeds are also investigated to show how these practical technologies can facilitate CPS applications. We present and propose state-of-the-art algorithms to address design challenges with both quantitative and qualitative methods and map these challenges with important CPS applications to draw insightful conclusions on the challenges of each application. Finally, we summarize potential new directions and ideas that could shape future research in these areas. 1998-2012 IEEE.Qatar UniversityScopu

Recent advances in mobile touch screen security authentication methods: A systematic literature review

The security of the smartphone touch screen has attracted considerable attention from academics as well as industry and security experts. The maximum security of the mobile phone touch screen is necessary to protect the user's stored information in the event of loss. Previous reviews in this research domain have focused primarily on biometrics and graphical passwords while leaving out PIN, gesture/pattern and others. In this paper, we present a comprehensive literature review of the recent advances made in mobile touch screen authentication techniques covering PIN, pattern/gesture, biometrics, graphical password and others. A new comprehensive taxonomy of the various multiple class authentication techniques is presented in order to expand the existing taxonomies on single class authentication techniques. The review reveals that the most recent studies that propose new techniques for providing maximum security to smartphone touch screen reveal multi-objective optimization problems. In addition, open research problems and promising future research directions are presented in the paper. Expert researchers can benefit from the review by gaining new insights into touch screen cyber security, and novice researchers may use this paper as a starting point of their inquiry. - 2019The authors will like to acknowledge Tetfund Institutional Based Research Grant, Federal College of Education (Technical), Gombe. Tahir Musa Ibrahim is currently a logistics manager at Jos Electricity Distribution Plc, Bauchi Trading Zone, since 2014. He received his B.Sc. Computer Science degree in 2011, from a private university; Kwararafa University, Wukari, Taraba State, Nigeria. He is also currently pursuing his M.Sc. degree in Information Technology at National Open University, Nigeria. His current research topic is, recent advances on mobile touch screen security authentication methods: A systematic Literature Survey. He is also a member of International Project Management Professionals (IPMP). Shafi'i Muhammad Abdulhamid received his Ph.D. in Computer Science from Universiti Teknologi Malaysia (UTM), MSc in Computer Science from Bayero University Kano (BUK), Nigeria and a Bachelor of Technology in Mathematics/Computer Science from the Federal University of Technology Minna, Nigeria. His current research interests are in Cyber Security, Cloud computing, Soft Computing and BigData. He has published many academic papers in reputable International journals, conference proceedings and book chapters. He has been appointed as an Editorial board member for UPI JCSIT and IJTRD. He has also been appointed as a reviewer of several ISI and Scopus indexed International journals such as JNCA Elsevier, ASOC Elsevier, EIJ Elsevier, NCAA Springer, BJST Springer, & IJNS. He is a member of IEEE, IACSIT, IAENG, ISOC, Computer Professionals Registration Council of Nigeria (CPN), Cyber Security Experts Association of Nigeria (CSEAN) and Nigerian Computer Society (NCS). Presently, he is a Senior Lecturer at the Department of Cyber Security Science, Federal University of Technology Minna, Nigeria. Ala Abdusalam Alarood is currently an Assistant Professor of Computer Science in Faculty of Computer and Information Technology, University of Jeddah. He obtained his Bachelor, Masters and Ph.D. in Computer Science from Yarmok University Jordan and Universiti Teknologi Malaysia respectively. His current research interest is in the area of Information Security, Networks Security, Steganalysis, Machine Learning, and Neural Network. Haruna Chiroma is a faculty member in Federal College of Education (Technical), Gombe, Nigeria. He received B.Tech. and M.Sc. both in Computer Science from Abubakar Tafawa Balewa University, Nigeria and Bayero University Kano, respectively. He earned a Ph.D. in Artificial Intelligence from University of Malaya, Malaysia. He has published articles relevance to his research interest in international referred journals, edited books, conference proceedings and local journals. He is currently serving on the Technical Program Committee of several international conferences. His main research interest includes metaheuristic algorithms in energy modeling, decision support systems, data mining, machine learning, soft computing, human computer interaction, and social media in education, computer communications, software engineering, and information security. He is a member of the ACM, IEEE, NCS, INNS, and IAENG. Mohammed Ali Al-Garadi received Ph.D. degree from University of Malaya, Malaysia and M.Tech. degree in electronic and communication engineering from Jawaharlal Nehru Technological University, Hyderabad, India. He has published several articles in academic journals indexed in well reputed databases such as ISI-indexed and Scopus-indexed. His field of research is online social networking, text mining, deep learning, and information retrieval. Nadim Rana is a lecturer in the department of Information Systems, Jazan University Kingdom of Saudi Arabia. He is currently a Ph.D. researcher at the Faculty of Computer Science and Information Technology, University Technology Malaysia. His expertise is in Information Systems (Business Informatics), Databases and Data Mining. Amina Nuhu Muhammad currently a Graduate Assistant in the Department of Mathematics, Gombe State University. She received B.Sc. in Computer Science from Gombe State University in 2012. She is now an M.Sc. researcher in the Department of Mathematics, Abubakar Tafawa Balewa University Bauchi. Her research interest is on The Advances of Artificial Neural Network for Internet of Things. Adamu Abubakar received B.S. and M.S. degrees in Geography and Computer Science in School of Science from Bayero University Kano Nigeria in 2004 and 2006, respectively. He obtained his Ph.D. in Information Technology from the Department of Information System Kulliyyah of Information and Communication Technology, International Islamic University Malaysia; Since November 2012, he is with the Department of Computer Science School International Islamic University Malaysia currently as Assistant Professor. His current research interests include Machine Learning and Parallel computing. Khalid Haruna is currently a lecturer in the Department of Computer Science, Bayero University Kano, Nigeria. He received B.Sc. and M.Sc. degrees in Computer Science from Bayero University, Kano, Nigeria in 2011 and 2015 respectively. He has also received his PhD degree in Information Systems from the University of Malaya, Malaysia in 2018. His research interests include Recommender Systems, Internet of Things, Semantic Web, Big Data Analytics, Sentiment Analysis, Soft Set and E-Learning Systems. Lubna A. Gabralla has a Doctor of philosophy in Computer Science from Sudan University of Science & Technology, with a specilisation in Computer science: Artificial intelligence, Datamining, Machine Learning and Soft computing. She has previously held the position of Assistant Professor in the Faculty of Computer Science at Future University, Khartoum, and is also currently an Assistant Professor at the Sudan Academy for Banking Sciences, Khartoum- Sudan. She has published many publications and conference papers, including a book (Crude Oil Price Forecasting Using Machine Learning, ISBN: 978- 3-659-92,377-7).Scopu