Mohammad Alauthman, Nauman Aslam, Mouhammd Al-Kasassbeh, Suleman Khan, Ahmad Al-Qerem, Kim-Kwang Raymond Choo, " An efficient reinforcement learning-based Botnet detection approach " , "Journal of Network and Computer Applications",Vol.150,No., Elsevier, USA, 01/15/2020
Abstract:
The use of bot malware and botnets as a tool to facilitate other malicious cyber activities (e.g. distributed denial of service attacks, dissemination of malware and spam, and click fraud). However, detection of botnets, particularly peer-to-peer (P2P) botnets, is challenging. Hence, in this paper we propose a sophisticated traffic reduction mechanism, integrated with a reinforcement learning technique. We then evaluate the proposed approach using real-world network traffic, and achieve a detection rate of 98.3%. The approach also achieves a relatively low false positive rate (i.e. 0.012%).
Ayoub Alsarhan, Abdel-Rahman Al-Ghuwairi, Islam T Almalkawi, Mohammad Alauthman, Ahmed Al-Duba, " Machine Learning-Driven Optimization for Intrusion Detection in Smart Vehicular Networks " , "Wireless Personal Communications",Vol.Online first,No., Springer, Netherlands, 09/08/2020
Abstract:
An essential element in the smart city vision is providing safe and secure journeys via intelligent vehicles and smart roads. Vehicular ad hoc networks (VANETs) have played a significant role in enhancing road safety where vehicles can share road information conditions. However, VANETs share the sam
Ahmad Al-Nawasrah, Ammar Ali Almomani, Samer Atawneh, Mohammad Alauthman, " A Survey of Fast Flux Botnet Detection With Fast Flux Cloud Computing " , "International Journal of Cloud Applications and Computing ",Vol.10,No., IGI Global, USA, 07/01/2020
Abstract:
A botnet refers to a set of compromised machines controlled distantly by an attacker. Botnets are considered the basis of numerous security threats around the world. Command and control (C&C) servers are the backbone of botnet communications, in which bots send a report to the botmaster, and the lat
Kamal Alieyan, Ammar Almomani, Mohammed Anbar, Mohammad Alauthman, Rosni Abdullah, BB Gupta, " DNS rule-based schema to botnet detection " , "Enterprise Information Systems ",Vol.14,No., Taylor & Francis, United Kingdom , 07/25/2019
Abstract:
Botnets are considered a serious issue today. They have several negative economic impacts as well. Such impacts are affecting organizations and individuals. Recent botnets–such as Zeus and Citadel’s Conficker–use the Domain Name System (DNS) to avoid detection. These botnets use the DNS server to pr
Ahmad Al-Qerem, Mohammad Alauthman, Ammar Almomani, BB Gupta, " IoT transaction processing through cooperative concurrency control on fog–cloud computing environment " , "Soft Computing",Vol.24,No., Springer, Berlin Heidelberg, 04/01/2020
Abstract:
In cloud–fog environments, the opportunity to avoid using the upstream communication channel from the clients to the cloud server all the time is possible by fluctuating the conventional concurrency control protocols. Through the present paper, the researcher aimed to introduce a new variant of the
Ammar Almomani, Mohammad Alauthman, Mohammed Alweshah, O Dorgham, Firas Albalas, " A comparative study on spiking neural network encoding schema: implemented with cloud computing " , "Cluster Computing",Vol.22,No., springer, Netherlands , 06/15/2019
Abstract:
Spiking neural networks (SNN) represents the third generation of neural network models, it differs significantly from the early neural network generation. The time is becoming the most important input. The presence and precise timing of spikes encapsulate have a meaning such as human brain behavior.
Mohammad Alauthman, Nauman Aslam, Li Zhang, Rafe Alasem, M Alamgir Hossain, " A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks " , "Neural Computing and Applications",Vol.29,No., Springer , London, 06/04/2018
Abstract:
In recent years, Botnets have been adopted as a popular method to carry and spread many malicious codes on the Internet. These malicious codes pave the way to execute many fraudulent activities including spam mail, distributed denial-of-service attacks and click fraud. While many Botnets are set up
Ammar Almomani, Mohammad Alauthman, Firas Albalas, O Dorgham, Atef Obeidat, " An online intrusion detection system to cloud computing based on NeuCube algorithms " , "International Journal of Cloud Applications and Computing ",Vol.8,No., IGI-Global, USA, 04/02/2018
Abstract:
This article describes how as network traffic grows, attacks on traffic become more complicated and harder to detect. Recently, researchers have begun to explore machine learning techniques with cloud computing technologies to classify network threats. So, new and creative ways are needed to enhance
Smadi, S. , Alauthman, M. , Almomani, O. , Saaidah, A. , Alzobi, F., " Application layer denial of services attack detection based on stacknet " , "International Journal of Advanced Trends in Computer Science and Engineering,",Vol.9,No., Warse, india, 06/30/2020
Abstract:
Denial of Services (DoS) Attack is one of the most advanced attacks targeting cybercriminals. The DoS attack is designed to reduce the performance of network devices by performing their intended functions. In addition, the confidentiality, reliability and quality of data can be compromised by DoS at
Ammar Almomani, Ahmad Al-Nawasrah, Mohammad Alauthman, Mohammed Azmi Al-Betar, Farid Meziane , " Botnet detection used fast-flux technique, based on adaptive dynamic evolving spiking neural network algorithm " , "International Journal of Ad Hoc and Ubiquitous Computing",Vol.36,No., inderscience, United Kingdom , 01/28/2021
Abstract:
A botnet refers to a group of machines. These machines are controlled distantly by a specific attacker. It represents a threat facing the web and data security. Fast-flux service network (FFSN) has been engaged by bot herders for cover malicious botnet activities. It has been engaged by bot herders
Ayoub Alsarhan, Mohammad Alauthman, Esra’a Alshdaifat, Abdel-Rahman Al-Ghuwairi and Ahmed Al-Dubai , " Machine Learning-driven optimization for SVM-based intrusion detection system in vehicular ad hoc networks " , "Journal of Ambient Intelligence and Humanized Computing",Vol.12,No., springer, Germany, 02/24/2021
Abstract:
Machine learning (ML) driven solutions have been widely used to secure wireless communications Vehicular ad hoc networks (VANETs) in recent studies. Unlike existing works, this paper applies support vector machine (SVM) for intrusion detection in VANET. The structure of SVM has many computation adva
Rizwan Hamid Randhawa; Nauman Aslam; Mohammad Alauthman; Husnain Rafiq; Frank Comeau, " Security Hardening of Botnet Detectors using Generative Adversarial Networks " , "IEEE Access",Vol.Early Access,No., IEEE Access, United States, 05/25/2021
Abstract:
Machine learning (ML) based botnet detectors are no exception to traditional ML models when it comes to adversarial evasion attacks. The datasets used to train these models have also scarcity and imbalance issues. We propose a new technique named Botshot, based on generative adversarial networks (GA
Ammar Almomani, Mohammad Alauthman, Mohd Taib Shatnawi, Mohammed Alweshah, Ayat Alrosan, Waleed Alomoush, Brij B. Gupta, " Phishing Website Detection With Semantic Features Based on Machine Learning Classifiers: A Comparative Study " , "International Journal on Semantic Web and Information Systems (IJSWIS)",Vol.18,No., IGI, USA, 03/22/2022
