Dr. Islam Tharwat

Assistant Professor

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Dr. Islam Tharwat Abdel Halim received his B.Sc. and M.Sc. degrees and his Ph.D. degree in computer and systems engineering from the Faculty of Engineering, Ain Shams University, Cairo, Egypt. He has authored many research articles in various refereed conferences and journals, including IEEE Intelligent Transportation Systems Magazine, Computer Networks (Elsevier), and Wireless Networks (Springer).

His current research interests include IoT, Fog/Edge computing, mobile and wireless networks with emphasis on security and performance evaluation in vehicular communication, and 5G networks. Also, he is a senior member of the IEEE and is currently appointed as IEEE Computer Society Region 8 Coordinator as well as the chair of the awareness and customer advocacy in the Distinguished Visitor Program (DVP), IEEE Computer Society.

In addition, he is the founder and currently recorded as the chair of the IEEE Egypt Section Consultants Network and the vice-chair of ACM Cairo Chapter. Furthermore, he is a member of several specialized technical committees, including the IEEE Computer Society special technical community on Blockchain.

He has over 20 years of experience in the field of information technology and digital transformation. He is a member of the Digital Transformation Committee at Nile University and participated in the development of the digital transformation training plan for the Egyptian government

  • His Ph.D. thesis was granted the best thesis award.
  • He has obtained several accredited certifications from major global IT companies such as Cisco, Microsoft, VMware, and IBM.
  • He contributed to the establishment and organization of several local and international conferences in the field of computer engineering and sciences, serving as a session chair, invited speaker, program committee member, and editor for various international conferences and journals.
Recent Publications

HSAS-MD Analyzer: A Hybrid Security Analysis System Using Model-Checking Technique and Deep Learning for Malware Detection in IoT Apps

Established Internet of Things (IoT) platforms suffer from their inability to determine whether an IoT app is secure or not. A security analysis system (SAS) is a protective shield against any attack that breaks down data privacy and security. Its main task focuses on detecting malware and verifying app behavior. There are many SASs implemented in various IoT applications. Most of them build on

Artificial Intelligence
Software and Communications
Research Tracks
  • Cybersecurity.
  • Software and Systems.
  • Wireless Networks
  • Computer Security
  • Cloud Computing and IoT