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Dr Ali Hilal Al-Bayatti

Job: Associate Professor in Cyber Security

Faculty: Technology

School/department: School of Computer Science and Informatics

Research group(s): Cyber Technology Institute (CTI) (Software Technology Research Laboratory (STRL))

Address: De Montfort University, The Gateway, Leicester, LE1 9BH, United Kingdom

T: +44 (0)116 207 8586

E: alihmohd@dmu.ac.uk

W: http://www.tech.dmu.ac.uk/~alihmohd/

 

Personal profile

Dr. Ali Al-Bayatti is a Senior Lecturer in Intelligent Transportation systems at Software Technology Research Laboratory, a research institute established within De Montfort University, Leicester, UK. , his research deals with vehicular (e.g. Vehicular Ad hoc Networks), Cyber Security (e.g. Security Management) and smart technologies (e.g. Context-aware Systems) that promote collective intelligence. Applications range from promoting comfort, to enabling safety in critical scenarios. The goal of his research is to improve the effectiveness, efficiency, mobility, security and safety of transportationsystems.

Dr. Ali Al-Bayatti is currently teaching Undergraduate module ‘CTEC3604 Multi-service Networks’ in Computer Science. He is currently the programme leader for MSc Cyber Technology, MSc Software Engineering, MSc Cyber Security and MSc Professional Practice in Digital Forensics and Security.

Publications and outputs 

  • From Conventional to State-of-the-Art IoT Access Control Models
    From Conventional to State-of-the-Art IoT Access Control Models Al-Bayatti, Ali Hilal; Malik, Ahmad Kamran; Khan, Sarmadullah; Emmanuel, N.; Zafar, S.; Khattack, H.A.; Raza, B.; Alassafi, M.O.; Alfakheeh, A.S.; Alqarni, M.A. The advent in Online Social Networks (OSN) and Internet of Things (IoT) has created a new world of collaboration and communication between people and devices. The domain of internet of things uses billions of devices (ranging from tiny sensors to macro scale devices) that continuously produce and exchange huge amounts of data with people and applications. Similarly, more than a billion people are connected through social networking sites to collaborate and share their knowledge. The applications of IoT such as smart health, smart city, social networking, video surveillance and vehicular communication are quickly evolving people’s daily lives. These applications provide accurate, information-rich and personalized services to the users. However, providing personalized information comes at the cost of accessing private information of users such as their location, social relationship details, health information and daily activities. When the information is accessible online, there is always a chance that it can be used maliciously by unauthorized entities. Therefore, an effective access control mechanism must be employed to ensure the security and privacy of entities using OSN and IoT services. Access control refers to a process which can restrict user’s access to data and resources. It enforces access rules to grant authorized users an access to resources and prevent others. This survey examines the increasing literature on access control for traditional models in general, and for OSN and IoT in specific. Challenges and problems related to access control mechanisms are explored to facilitate the adoption of access control solutions in OSN and IoT scenarios. The survey provides a review of the requirements for access control enforcement, discusses several security issues in access control, and elaborates underlying principles and limitations of famous access control models. We evaluate the feasibility of current access control models for OSN and IoT and provide the future development direction of access control for the same open access article
  • A Machine-Learning-Based Approach to Predict the Health Impacts of Commuting in Large Cities: Case Study of London
    A Machine-Learning-Based Approach to Predict the Health Impacts of Commuting in Large Cities: Case Study of London Raj Theeng Tamang, M.; Sharif, M.S.; Al-Bayatti, Ali Hilal; Alfakeeh, A.S.; Omar Alsayed, A. The daily commute represents a source of chronic stress that is positively correlated with physiological consequences, including increased blood pressure, heart rate, fatigue, and other negative mental and physical health effects. The purpose of this research is to investigate and predict the physiological effects of commuting in Greater London on the human body based on machine-learning approaches. For each participant, the data were collected for five consecutive working days, before and after the commute, using non-invasive wearable biosensor technology. Multimodal behaviour, analysis and synthesis are the subjects of major efforts in computing field to realise the successful human–human and human–agent interactions, especially for developing future intuitive technologies. Current analysis approaches still focus on individuals, while we are considering methodologies addressing groups as a whole. This research paper employs a pool of machine-learning approaches to predict and analyse the effect of commuting objectively. Comprehensive experimentation has been carried out to choose the best algorithmic structure that suit the problem in question. The results from this study suggest that whether the commuting period was short or long, all objective bio-signals (heat rate and blood pressure) were higher post-commute than pre-commute. In addition, the results match both the subjective evaluation obtained from the Positive and Negative Affect Schedule and the proposed objective evaluation of this study in relation to the correlation between the effect of commuting on bio-signals. Our findings provide further support for shorter commutes and using the healthier or active modes of transportation. open access article
  • A Fuzzy-Logic Approach to Dynamic Bayesian Severity Level Classification of Driver Distraction Using Image Recognition
    A Fuzzy-Logic Approach to Dynamic Bayesian Severity Level Classification of Driver Distraction Using Image Recognition Fasanmade, Adebamigbe; He, Ying; Al-Bayatti, Ali Hilal; Morden, J.N.; Aliyu, Suleiman; Alfakeeh, A.S.; Alsayed, A.O. Detecting and classifying driver distractions is crucial in the prevention of road accidents. These distractions impact both driver behavior and vehicle dynamics. Knowing the degree of driver distraction can aid in accident prevention techniques, including transitioning of control to a level 4 semi- autonomous vehicle, when a high distraction severity level is reached. Thus, enhancement of Advanced Driving Assistance Systems (ADAS) is a critical component in the safety of vehicle drivers and other road users. In this paper, a new methodology is introduced, using an expert knowledge rule system to predict the severity of distraction in a contiguous set of video frames using the Naturalistic Driving American University of Cairo (AUC) Distraction Dataset. A multi-class distraction system comprises the face orientation, drivers’ activities, hands and previous driver distraction, a severity classification model is developed as a discrete dynamic Bayesian (DDB). Furthermore, a Mamdani-based fuzzy system was implemented to detect multi- class of distractions into a severity level of safe, careless or dangerous driving. Thus, if a high level of severity is reached the semi-autonomous vehicle will take control. The result further shows that some instances of driver’s distraction may quickly transition from a careless to dangerous driving in a multi-class distraction context. open access article
  • A Multi-User, Single-Authentication Protocol for Smart Grid Architectures
    A Multi-User, Single-Authentication Protocol for Smart Grid Architectures Khan, Sarmadullah; Alfakeeh, Ahmed; Al-Bayatti, Ali Hilal In a smart grid system, the utility server collects data from various smart grid devices. These data play an important role in the energy distribution and balancing between the energy providers and energy consumers. However, these data are prone to tampering attacks by an attacker, while traversing from the smart grid devices to the utility servers, which may result in energy disruption or imbalance. Thus, an authentication is mandatory to efficiently authenticate the devices and the utility servers and avoid tampering attacks. To this end, a group authentication algorithm is proposed for preserving demand–response security in a smart grid. The proposed mechanism also provides a fine-grained access control feature where the utility server can only access a limited number of smart grid devices. The initial authentication between the utility server and smart grid device in a group involves a single public key operation, while the subsequent authentications with the same device or other devices in the same group do not need a public key operation. This reduces the overall computation and communication overheads and takes less time to successfully establish a secret session key, which is used to exchange sensitive information over an unsecured wireless channel. The resilience of the proposed algorithm is tested against various attacks using formal and informal security analysis. open access article
  • An Accurate Ensemble Classifier for Medical Volume Analysis: Phantom and Clinical PET Study
    An Accurate Ensemble Classifier for Medical Volume Analysis: Phantom and Clinical PET Study Sharif, Mhd Saeed; Abbod, M.; Al-Bayatti, Ali Hilal; Amira, Abbes; Alfakeeh, Ahmed; Sanghera, B. The predominant application of positron emission tomography (PET) in the field of oncology and radiotherapy and the significance of medical imaging research have led to an urgent need for effective approaches to PET volume analysis and the development of accurate and robust volume analysis techniques to support oncologists in their clinical practice, including diagnosis, arrangement of appropriate radiotherapy treatment, and evaluation of patients’ response to therapy. This paper proposes an efficient optimized ensemble classifier to tackle the problem of analysis of squamous cell carcinoma in patient PET volumes. This optimized classifier is based on an artificial neural network (ANN), fuzzy C-means (FCM), an adaptive neuro-fuzzy inference system (ANFIS), K-means, and a self-organizing map (SOM). Four ensemble classifier machines are proposed in this study. The first three are built using a voting approach, an averaging technique, and weighted averaging, respectively. The fourth, novel ensemble classifier machine is based on the combination of a modified particle swarm optimization (PSO) approach and weighted averaging. Experimental National Electrical Manufacturers Association and International Electrotechnical Commission (NEMA IEC) body phantom and clinical PET studies of participants with laryngeal squamous cell carcinoma are used for the evaluation of the proposed approach. Superior results were achieved using the new optimized ensemble classifier when compared with the results from the investigated classifiers and the non-optimized ensemble classifiers. The proposed approach identified the region of interest class (tumor) with an average accuracy of 98.11% in clinical datasets of patients with laryngeal tumors. This system supports the expertise of clinicians in PET tumor analysis. The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. open access journal
  • Use of smartphones for ensuring vulnerable road user safety through path prediction and early warning: An in-depth review of Capabilities, Limitations and their applications in Cooperative Intelligent Transport Systems
    Use of smartphones for ensuring vulnerable road user safety through path prediction and early warning: An in-depth review of Capabilities, Limitations and their applications in Cooperative Intelligent Transport Systems Vourgidis, Ioannis; Maglaras, Leandros; Alfakeeh, Ahmed S.; Al-Bayatti, Ali Hilal; Ferrag, Mohamed Amine The field of cooperative intelligent transport systems and more specifically pedestrians to vehicles could be characterized as quite challenging, since there is a broad research area to be studied, with direct positive results to society. Pedestrians to vehicles is a type of cooperative intelligent transport system, within the group of early warning collision/safety system. In this article, we examine the research and applications carried out so far within the field of pedestrians to vehicles cooperative transport systems by leveraging the information coming from vulnerable road users’ smartphones. Moreover, an extensive literature review has been carried out in the fields of vulnerable road users outdoor localisation via smartphones and vulnerable road users next step/movement prediction, which are closely related to pedestrian to vehicle applications and research. We identify gaps that exist in these fields that could be improved/extended/enhanced or newly developed, while we address future research objectives and methodologies that could support the improvement/development of those identified gaps. open access article
  • Use Of Smartphones for Ensuring Vulnerable Road User Safety through Path Prediction and Early Warning: An In-Depth Review of Capabilities, Limitations and Their Applications in Cooperative Intelligent Transport Systems
    Use Of Smartphones for Ensuring Vulnerable Road User Safety through Path Prediction and Early Warning: An In-Depth Review of Capabilities, Limitations and Their Applications in Cooperative Intelligent Transport Systems Vourgidis, Ioannis; Maglaras, Leandros; Alfakeeh, Ahmed S.; Al-Bayatti, Ali Hilal; Ferrag, Mohamed Amine The field of cooperative intelligent transport systems and more specifically pedestrians to vehicles could be characterized as quite challenging, since there is a broad research area to be studied, with direct positive results to society. Pedestrians to vehicles is a type of cooperative intelligent transport system, within the group of early warning collision/safety system. In this article, we examine the research and applications carried out so far within the field of pedestrians to vehicles cooperative transport systems by leveraging the information coming from vulnerable road users’ smartphones. Moreover, an extensive literature review has been carried out in the fields of vulnerable road users outdoor localisation via smartphones and vulnerable road users next step/movement prediction, which are closely related to pedestrian to vehicle applications and research. We identify gaps that exist in these fields that could be improved/extended/enhanced or newly developed, while we address future research objectives and methodologies that could support the improvement/development of those identified gaps. open access article
  • AlphaLogger: Detecting Motion-based Side-Channel Attack Using Smartphone Keystrokes
    AlphaLogger: Detecting Motion-based Side-Channel Attack Using Smartphone Keystrokes Javed, A.R.; Baker, T.; Asim, M.; Beg, M.O.; Al-Bayatti, Ali Hilal Due to the advancement in technologies and excessive usability of smartphones in various domains (e.g., mobile banking), smartphones became more prone to malicious attacks.Typing on the soft keyboard of a smartphone produces different vibrations, which can be abused to recognize the keys being pressed, hence, facilitating side-channel attacks. In this work, we develop and evaluate AlphaLogger - an Android-based application that infers the alphabet keys being typed on a soft keyboard. AlphaLogger runs in the background and collects data at a frequency of 10Hz/sec from the smartphone hardware sensors (accelerometer, gyroscope and magnetometer ) to accurately infer the keystrokes being typed on the soft keyboard of all other applications running in the foreground. We show a performance analysis of the different combinations of sensors. A thorough evaluation demonstrates that keystrokes can be inferred with an accuracy of 90.2% using accelerometer, gyroscope, and magnetometer. The file attached to this record is the author's final peer reviewed version
  • Variance Ranking for Multi-Classed Imbalanced Datasets: A Case Study of One-Versus-All
    Variance Ranking for Multi-Classed Imbalanced Datasets: A Case Study of One-Versus-All Ebenuwa, Solomon H.; Sharif, Mhd Saeed; Al-Nemrat, Ameer; Al-Bayatti, Ali Hilal; Alalwan, Nasser; Alzahrani, Ahmed Ibrahim; Alfarraj, Osama Imbalanced classes in multi-classed datasets is one of the most salient hindrances to the accuracy and dependable results of predictive modeling. In predictions, there are always majority and minority classes, and in most cases it is difficult to capture the members of item belonging to the minority classes. This anomaly is traceable to the designs of the predictive algorithms because most algorithms do not factor in the unequal numbers of classes into their designs and implementations. The accuracy of most modeling processes is subjective to the ever-present consequences of the imbalanced classes. This paper employs the variance ranking technique to deal with the real-world class imbalance problem. We augmented this technique using one-versus-all re-coding of the multi-classed datasets. The proof-of-concept experimentation shows that our technique performs better when compared with the previous work done on capturing small class members in multi-classed datasets.
  • Agent-based Negotiation Approach for Feature Interactions in Smart Home Systems using Calculus of the Context-aware Ambient
    Agent-based Negotiation Approach for Feature Interactions in Smart Home Systems using Calculus of the Context-aware Ambient Alfakeeh, Ahmed S.; Al-Bayatti, Ali Hilal; Siewe, Francois; Baker, Thar Smart Home Systems (SHSs) provide several services which are tailored to different residents’ preferences. As a result, SHSs are highly exposed to undesirable interactions, known as feature interactions (FIs). FIs might occur as a result of a conflict in services’ goals or a conflict with residents’ preferences. Previous studies have proposed solutions based on applying priorities, in which some services or preferable features are disabled in favour of other services. Alternatively, the agent-based negotiation approach (ABNA) utilises agents and applies negotiation, enabling services with contrary features to work simultaneously. ABNA avoids applying priority between services or house residents’ preferences whenever a space for a compromise exists. The mechanism of ABNA is based on the use of a hierarchy of features based on their contribution to the function of the service or on the importance of these features to house residents. To achieve a compromise between conflicting services, ABNA models services and residents by using agents, and implements a negotiation algorithm that allows services with conflicting features to work simultaneously. This paper presents a description of ABNA with a formal specification of ABNA in the Calculus of Context-aware Ambient (CCA). This enables the formal analysis of ABNA by using the execution environment of CCA. The file attached to this record is the author's final peer reviewed version.

View all Ali Hilal Al-Bayati's publications and outputs

Key research outputs

 

Research interests/expertise

  • Intelligent transpiration
  • Vehicular Ad hoc Networks
  • Mobile Computing
  • Wireless Computing
  • Context-aware Systems
  • Pervasive Computing
  • Computer/Mobile Security.

Areas of teaching

CTEC3604 Multi-service Networks (30 credit).

Qualifications

B.Sc. in Computer Engineering and Information Technology at the University of Technology, Iraq.

Ph.D. in Computer Science at De Montfort University, UK.  

Courses taught

Programme leader for MSc Cyber Technology, MSc Software Engineering, MSc Cyber Security and MSc Professional Practice in Digital Forensics and Security.

Current research students

Successful PhD Completion 


Dr. Khalid Alodadi “Solving Non-Line of Sight using Context-aware Systems in Vehicle Ad Hoc Networks” De Montfort University (2016). 

Dr. Ahmed Alghamdi “Features interaction: detection and resolution in Smart Homes Systems” De Montfort University (2016).

Dr. Tareq Binjammaz “GPS Integrity Monitoring for an Intelligent Transport System“ De Montfort University (2015). 

Dr. Abdullah Aldawsari “Context-aware Driving Behaviour Detection System in Vehicle Ad Hoc Networks“ De Montfort University (2015).

Dr. Yasser Almajed “Privacy Management in Data Warehousing” De Montfort University (2015). Dr. Fahad Alqahtani “E-commerce Customer Anonymity and Fair Exchange Protocol for Digital Contents” De Montfort University (2015). 

Dr. Abdulmalik Alhammad “Intelligent Parking Systems in Vehicle Ad Hoc Networks” De Montfort University (2015).

Dr. Hani Alquhayz “Security Management System for 4G Heterogenous Networks“ De Montfort University (2015).

Dr. Mussab Aswad “Crash Detection Model Using Dynamic Bayesian Networks” De Montfort University (2014). 

Dr. Mafawez Alharbi “Context-aware PLE Architecture”, De Montfort University (2014). 

Dr. Laila Alhimale “Fall Detection Algorithm for Video Images”, De Montfort University (2013).

Dr. Saif Al-Sultan “Context Aware Driving Behaviour Model for VANET”, De Montfort University (2013). 

Dr. Awatef Rahuma “Semantically Enhanced Image Tagging System”, De Montfort University (2013).

Dr. Moath Al-Doori “Directional Routing Technique in Vehicle Ad hoc Networks, De Montfort University (2011). 

Dr. Muhammed Khan “A Co-Evolutionary Framework to Reducing the Gap between and Information Technology (2011).


Current PhD Students (Main Supervisor) 

Mr. Dennis Bohmlander "Innovative Crash-sensing Architectures - A new approach in contactless vehicle crash detection" De Montfort University. 

Mr. Raphael Riebl "Perfomance Testing methodology for Vehicle Ad hoc Networks" De Montfort University.

Mr. Sadir Fadhil "Context-aware overtaking assistant system. De Montfort University.

Mr. Nawaf Alqabandi “Context-aware Intrusion Detection system using Artificial intelligence in VANET” De Montfort University.

 

Successful MSc Completion 

Mr. Shadman Salah (2014). 

Mr. Ahmed Malik (2013) “Factors effecting Delivering Insulin for diabetic patients using Bayesian Networks”

Mr. Anjanna Silva (2013) “Car Polling System”

Mr. Anas Alsharif (2012) “Automated Taxi Dispatch System (Taxi Business) De Montfort University.

Mr. Mahran Alsubee (2012) “Intelligent Car Parking System - A case of City of Medina, Saudi Arabia” De Montfort University.

Mr. Salman Alenezi (2012) “The Lines Between Augmented Reality and Virtual Reality” De Montfort University. 

Mr. Uqonna Ekwueme (2011)“Intelligent Car Parking Schemes” De Montfort University.

Mrs. Nada Al-Fakih (2011) “Cloud Based Personal Health Record” De Montfort University.

Mrs. Entisar Alshirf (2011) “Selection of Computer Programming Languages for Developing Distributed Systems. De Montfort University.

Mrs. Ohud Almutairi (2011) “Designing an Effective Intersection Collision Warning System: An Investigation into Important Criteria” De Montfort University. 

Mrs. Ruqayah Aljameel (2011) “The Application’s Usability Evaluation of Web-based Geographic Information System for Pst Office Webs” De Montfort University. 

Mr. Khalid Shaban (2011) “Evaluating Mobile Application Performance and Power Consumption Trough Model-Driven Engineering Methodology” De Montfort University.

Mrs. Laila Elgamel (2011) “ Selection of Programming Languages for Developing Distributed Systems” De Montfort University.

Mr. Ahmed Alghamdi (2010) “Feasibility of Separating Control/Data in 802.11 Family” De Montfort University. 

Mr. Mafawez Alharbi (2010) “Mobile Lecture” De Montfort University. 

Mr. Abdulkariem Alqarni (2010) “Global Intelligent Parking Schemes” De Montfort University. 

Mr. Abdullah Algashami (2010) “Good Practice for Effective E-assessment” De Montfort University. 

Mr. Sharaf Alzhrani (2010) “Intelligent Application for Car Hiring (Mileage Tracking Application)” De Montfort University.

Mr. Anas Alsharif (2010) “Automated Taxi Dispatch System” De Montfort University. 

Mrs. Asma Alothaim (2010) “Location Finder and Weather Forecast Application” De Montfort University. 

Mr. Fauwaz Alshammari “Risk Management in Software Development Projects” De Montfort University.

Mr. Ali Almiman “Security Survey in VoIP” De Montfort University. Mr. Thamir Alghamdi “Comparison of Two Parking Management Systems” De Montfort University.

Professional esteem indicators

Journal Article Reviewing
Networking [IET] 2016. Vehicular Communications [Elsevier] 2014, 2015, 2016. Sensors [MDPI] 2016. Frontiers of Information Technology & Electronic Engineering [Springer] 2014, 2015. The Journal of Engineering [IET] 2014, 2015; Journal of Advances in Engineering Software [Elsevier]; Journal of Network & Computer Applications 2013 [Elsevier]; Computer & Electrical Engineering Journal (CEE) 2011, 2012 [Elsevier]; International Journal of Ad Hoc Ubiquitous Computing (IJAHUC) 2010

Program Committee Member / Other Conference & Workshop Reviewing
IEEE 83rd Vehicular Technology Conference: VTC2016-Spring 15–18 May 2016, Nanjing, ChinaIEEE Vehicular Technology Conference: VTC2015-Spring 11–14 May 2015, Glasgow, ScotlandInternational Conference on Computer, Communications, and Control Technology (I4CT) 2014Springer based Applied Electromagnetic International Conference (APPEIC) 2014Renewable Energy and Green Technology International Conference (REEGETECH) 2014International Symposium on Technology Management and Emerging Technologies (ISTMET) 2014International Conference on Communication and Computer Engineering, focusing on Industrial and Manufacturing Theory and Applications of Electronics, Communications, Computing, and Information Technology (ICOCOE) 2014IEEE Symposium on Computer Application & Industrial Electronics (ISCAIE) 2013IEEE International RF and Microwave Conference (RFM) 2013IEEE Symposium on Industrial Electronics and Applications (ISIEA) 2013IEEE Conference on Wireless Sensors (ICWiSE) 2013IEEE Symposium on Computer & Informatics (ISCI) 2013IEEE Asia-Pacific Conference on Applied Electromagnetic (APACE ) 2012IEEE Symposium on Wireless Technology & Applications (ISWTA) 2012, 2013IEEE Symposium on Industrial Electronic & Applications (ISIEA ) 2012ICNS Networking and Services 2008, 2009, 2010, 2011, 2012, 2013, 2014International Symposium on Innovation in Information & Communication Technology (ISIICT) 2011, 2012, 2013, 2014International Conference on Computer Science & Information Technology (ICCSIT) 2011, 2012, 2013, 2014 International Symposium on Wireless Pervasive Computing (ISWPC) 2010 Member of the Steering committee of the International Online Workshop on writing a research paper (IOW-WRP) 2011AIRCC Worldwide Conferences

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