Dr. Khaled Alrawashdeh is an assistant professor in the College of Computer Science and Mathematics at Avila University, where he teaches undergraduate courses in Cybersecurity, Computer Science, and Software Engineering. Dr. Alrawashdeh received his Ph.D. in Computer Science and Engineering from the University of Cincinnati. He also holds an M.S. degree from the Florida Institute of Technology. He is a member of the Association for the Advancement of Artificial Intelligence (AAAI), the Association for Computing Machinery (ACM), the Institute of Electrical and Electronics Engineers (IEEE).
Dr. Alrawashdeh is interested in intelligent cybersecurity systems that operate in large, nondeterministic, or only partially known domains. Most of his research centers around techniques for intelligent intrusion detection systems and decision making (planning and learning) that enable decision-support security systems and teams of agents to act intelligently in their environments and detect and prevent malicious attacks. Even if they have only incomplete knowledge of their environments, imperfect abilities to manipulate them, limited or noisy perception.
Dr. Alrawashdeh has edited several conference proceedings and published several papers in various areas of cybersecurity and artificial intelligence, including papers at IEEE ICMLA and IEEE International Conference on Big Data Security on Cloud where his paper received the best presentation award. He also reviewed chapters for two books:
- Springer book “Generative Adversarial Learning: Architectures and Applications”.
- Springer book ”Large-scale Learning from Data Streams in Evolving Environments”.
Dr. Alrawashdeh is regularly participating in scholarly peer reviews for high-ranking journals in the rea of cybersecurity and artificial intelligence. He reviewed over 50 journals such as IEEE Access, ACM Transactions on Reconfigurable Technology and Systems, and Springer International Journal of Information Security. He is a member of the International Workshop on Big Data Analytics for Cyber Intelligence and Defense (BDA4CID 2018, IEEE Big Data 2018). He also has over 14 years of professional experience in automation, cybersecurity, and computer networking.
Khaled is passionate about helping students and young researchers to get a good start in their careers in cybersecurity and computer science and create an enjoyable learning environment.
1. Arawashdeh, K. & Goldsmith, S. (2020a). Optimizing Deep Learning Based Intrusion Detection Systems Defense Against White-Box and Backdoor Adversarial Attacks Through a Genetic Algorithm, In 2020 ieee international applied imagery pattern recognition workshop (aipr), 2020. IEEE.
2. Arawashdeh, K. & Goldsmith, S. (2020b). Defending Deep Learning Based Anomaly Detection Systems Against White-Box Adversarial Examples and Backdoor Attacks, In 2020 ieee international symposium on technology and society (istas). IEEE.
3. Alrawashdeh, K. & Purdy, C. (2018a). Fast Activation Function Approach for Deep Learning Based Online Anomaly Intrusion Detection, In 2018 ieee 4th international conference on big data security on cloud (bigdatasecurity), ieee international conference on high performance and smart computing (hpsc), and ieee international conference on intelligent data and security (ids). IEEE.
4. Arawashdeh, K. & Purdy, C. (2018a). Ransomware Detection Using Limited Precision Deep Learning Structure in FPGA, In National Aerospace and electronics conference (naecon), 2018 national. IEEE.
5. Arawashdeh, K. & Purdy, C. (2018b). Fast Hardware Assisted Online Learning Using Unsupervised Deep Learning Structure for Anomaly Detection, In International conference on information and computer technologies (icict 2018). IEEE.
6. Lockhart. J, K., Arawashdeh & Purdy, C. (2018). Verification of Random Number Generators for Embedded Machine Learning, In National Aerospace and electronics conference (naecon) , 2018. IEEE.
7. Alrawashdeh, K. & Purdy, C. (2017). Reducing calculation requirements in FPGA implementation of deep learning algorithms for online anomaly intrusion detection, In National Aerospace and electronics conference (naecon), 2017 ieee national. IEEE.
8. Alrawashdeh, K. & Purdy, C. (2016). Toward an Online Anomaly Intrusion Detection System Based on Deep Learning, In 2016 ieee 15th international conference on machine learning and application (icmla). IEEE