Salah Taamneh, Ph.D

Associate Professor

Abu Dhabi Campus

+971 2 6133286

salah.taamneh@aau.ac.ae

Education

PhD Computer Science, University of Houston - Texas, USA

MSc Computer Science, Prairie View A&M University - Texas, USA

BSc Computer Science, Jordan University of Science and Technology

Research Interests

  • Intelligent Transportation Systems (ITS)

  • Parallel and Distributed Computing

  • Cybersecurity

  • Affective Computing

  • Applications of Artificial Intelligence (AI)

Selected Publications

  • Aljammal, A. H., Al-Oqily, I., Obiedat, M., Qawasmeh, A., Taamneh, S., & Wedyan, F. I. (2024). Anomaly intrusion detection using machine learning-IG-R based on NSL-KDD dataset. Bulletin of Electrical Engineering and Informatics, 13(6), 4466-4474.
  • Aljammal, A. H., Qawasmeh, A., Taamneh, S., Wedyan, F., Obiedat, M., & Salameh, H. B. (2024, June). Assessing cybersecurity awareness among the hashemite university students in terms of computer usage. In 2024 16th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) (pp. 1-6). IEEE.
  • Hasan, M. T., Alghamdi, H., Taamneh, S., Manser, M., Wunderlich, R., Tsiamyrtzis, P., & Pavlidis, I. (2023). Investigating cardiovascular activation of young adults in routine driving. IEEE Transactions on Affective Computing, 15(3), 769-786.
  • Mughaid, A., AlZu’bi, S., Hnaif, A., Taamneh, S., Alnajjar, A., & Elsoud, E. A. (2022). An intelligent cyber security phishing detection system using deep learning techniques. Cluster Computing, 25(6), 3819-3828.
  • Taamneh, S., Al-Hami, M. T., Bani-Salameh, H., & Abdallah, A. E. (2021). A robust distributed clustering of large data sets on a grid of commodity machines. Data, 6(7), 73.
  • Taamneh, S., Qawasmeh, A., & Aljammal, A. H. (2020). Parallel and fault-tolerant k-means clustering based on the actor model. Multiagent and Grid Systems, 16(4), 379-396.
  • Taamneh, S., & Taamneh, M. M. (2021). A machine learning approach for building an adaptive, real-time decision support system for emergency response to road traffic injuries. International journal of injury control and safety promotion, 28(2), 222-232.
  • Alkheder, S., Taamneh, M., & Taamneh, S. (2017). Severity prediction of traffic accident using an artificial neural network. Journal of Forecasting, 36(1), 100-108.
  • Taamneh, S., Tsiamyrtzis, P., Dcosta, M., Buddharaju, P., Khatri, A., Manser, M., ... & Pavlidis, I. (2017). A multimodal dataset for various forms of distracted driving. Scientific data, 4(1), 170110.
  • Pavlidis, I., Dcosta, M., Taamneh, S., Manser, M., Ferris, T., Wunderlich, R., ... & Tsiamyrtzis, P. (2016). Dissecting driver behaviors under cognitive, emotional, sensorimotor, and mixed stressors. Scientific reports, 6(1), 25651.

Teaching Courses

Article

Anomaly intrusion detection using machine learning-IG-R based on NSL-KDD dataset

Published in: Bulletin of Electrical Engineering and Informatics

Dec 01, 2024

AH Aljammal I Al-Oqily M Obiedat A Qawasmeh S Taamneh FI Wedyan

Cybersecurity is challenging for security guards because of the rising quantity, variety, and frequency of attacks and malicious activities in cyberspace. Intrusion attacks are among the most common types of cyberspace attacks. Therefore, an intrusion detection system (IDS) is in high demand to accurately detect and mitigate their impact. In this paper, an anomaly IDS using machine learning and information gain-rank (IG-R) is proposed to improve the detection accuracy of intrusions. The network security lab-knowledge discovery dataset (NSL-KDD) is used to train and test the proposed IDS. Initially, the information gain (IG) algorithm and Ranker are used to evaluate, rank and reduce the number of selected instances from 41 instances to only 6 instances. Furthermore, many classifiers have been tested and evaluated; such as adaptive boosting (AdaBoostM1), random forest, J48, and naïve Bayes to choose the best performance classifier to be used in the detection process. After applying the IG-R and testing the suggested classifiers, the results showed that the random forest classifier has the best performance over the tested classifiers with TPR, FPR, and accuracy of 99.7%, 0.3%, and 99.7%, respectively, and is recommended to be used in the detection process.