Evaluation of machine learning algorithms for intrusion detection system M Almseidin, M Alzubi, S Kovacs, M Alkasassbeh Intelligent Systems and Informatics (SISY), 2017 IEEE 15th International …, 2017 | 251 | 2017 |
Detecting Distributed Denial of Service Attacks Using Data Mining Techniques M Alkasassbeh, G Al-Naymat, A Hassanat, M Almseidin (IJACSA) International Journal of Advanced Computer Science and Applications …, 2016 | 192 | 2016 |
An efficient reinforcement learning-based Botnet detection approach M Alauthman, N Aslam, M Alkasassbeh, S Khan, KKR Choo Journal of Network and Computer Applications 150 (15), 102479, 2020 | 94 | 2020 |
Network fault detection with Wiener filter-based agent M Alkasassbeh, M Adda Journal of Network and Computer Applications 32 (4), 824-833, 2009 | 67 | 2009 |
Prediction of PM10 and TSP Air Pollution Parameters Using Artificial Neural Network Autoregressive, External Input Models: A Case Study in Salt, Jordan M Alkasassbeh, AF Sheta, H Faris, H Turabieh Middle-East Journal of Scientific Research 14 (7), 999-1009,, 2013 | 55 | 2013 |
An Empirical Evaluation For The Intrusion Detection Features Based On Machine Learning And Feature Selection Methods M Alkasassbeh Journal of Theoretical and Applied Information Technology 95 (22), 2017 | 53 | 2017 |
An Anomaly Based Approach for DDoS Attacks Detection in Cloud Environment A Rawashdeh, M Al-Kasassbeh, M Hawari International Journal of Computer Applications in Technology 57 (4), 2018 | 49 | 2018 |
Handbook of computer networks and cyber security BB Gupta, GM Perez, DP Agrawal, D Gupta Springer 10, 978-3, 2020 | 47 | 2020 |
Towards Generating Realistic SNMP-MIB Dataset for Network Anomaly Detection M Alkasassbeh, G Al-Naymat, E Al-Hawari International Journal of Computer Science and Information Security 14 (No. 9 …, 2016 | 46 | 2016 |
Intensive Pre-Processing of KDD Cup 99 for Network Intrusion Classification Using Machine Learning Techniques I Obeidat, N Hamadneh, M Alkasassbeh, M Almseidin, MI AlZubi International Journal of Interactive Mobile Technologies (iJIM) 13 (1), pp …, 2019 | 43 | 2019 |
Feature selection using a machine learning to classify a malware M Alkasassbeh, S Mohammed, M Alauthman, A Almomani Handbook of computer networks and cyber security, 889-904, 2020 | 42 | 2020 |
Analysis of mobile agents in network fault management M Alkasassbeh, M Adda Journal of Network and Computer Applications 31 (4), 699-711, 2008 | 41 | 2008 |
Machine Learning Methods for Network Intrusion Detection M AlKasassbeh, mohammad Almseidi ICCCNT 2018 - THE 20th INTERNATIONAL CONFERENCE ON COMPUTING, COMMUNICATION …, 2018 | 40 | 2018 |
A Novel Hybrid Method for Network Anomaly Detection Based on Traffic Prediction and Change Point Detection M Alkasassbeh Journal of Computer Sciences 14 (2), 153--162, 2018 | 36 | 2018 |
Color-based object segmentation method using artificial neural network ABA Hassanat, M Alkasassbeh, M Al-Awadi, AA Esra'a Simulation Modelling Practice and Theory 64, 3-17, 2016 | 36 | 2016 |
Artificial Neural Networks for Surface Ozone Prediction: Models and Analysis H Faris, M Alkasassbeh, A Rodan Polish Journal of Environmental Studies 23 (2), 2014 | 35 | 2014 |
Review: Phishing Detection Approaches AMA Zuraiq, M Alkasassbeh The 2nd International Conference on New Trends in Computing Sciences, 27-32, 2019 | 34* | 2019 |
Phishing Detection Based on Machine Learning and Feature Selection Method M Almseidin, AMA Zuraiq, M Alkasassbeh, N Alnidami International Journal of Interactive Mobile Technologies (iJIM) 13 (12), 2019 | 33 | 2019 |
Using machine learning methods for detecting network anomalies within SNMP-MIB dataset G Al-Naymat, M Alkasassbeh, E Al-Hawari Int. J. Wireless and Mobile Computing 15 (1), 67-76, 2018 | 33 | 2018 |
Colour-based lips segmentation method using artificial neural networks ABA Hassanat, M Alkasassbeh, M Al-awadi, AA Esra'a Information and Communication Systems (ICICS), 2015 6th International …, 2015 | 33 | 2015 |