Search results for: Mouhammad Alkasassbeh

Authors: Mouhammad Alkasassbeh, Mohammad Almseidin

Abstract:

Network security engineers work to keep services available all the time by handling intruder attacks. Intrusion Detection System (IDS) is one of the obtainable mechanisms that is used to sense and classify any abnormal actions. Therefore, the IDS must be always up to date with the latest intruder attacks signatures to preserve confidentiality, integrity, and availability of the services. The speed of the IDS is a very important issue as well learning the new attacks. This research work illustrates how the Knowledge Discovery and Data Mining (or Knowledge Discovery in Databases) KDD dataset is very handy for testing and evaluating different Machine Learning Techniques. It mainly focuses on the KDD preprocess part in order to prepare a decent and fair experimental data set. The J48, MLP, and Bayes Network classifiers have been chosen for this study. It has been proven that the J48 classifier has achieved the highest accuracy rate for detecting and classifying all KDD dataset attacks, which are of type DOS, R2L, U2R, and PROBE.

Keywords: IDS, DDoS, MLP, KDD

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Authors: Abdullah Ababtain, Mouhammad El Hassan, Hassan Assoum, Anas Sakout

Abstract:

In this paper, a brief literature review on cavitation jets is presented in order to introduce the cavitation mechanism, strategies to assess when cavitation occurs, and the factors that influence cavitation in cavitating jets. The objectivity of the cavitation number often used to predict cavitation is also discussed. The results show that cavitation cannot be foreseen just using the cavitation number. Therefore, more efforts are needed to innovate and develop a self-resonating jet geometry that would be maintains the flow and the pressure in the cavitation condition just earlier than the flow acts on the target that would be used in such operating conditions. This study focused on a particular aspect related to improving drilling efficiency and the rate of penetration (ROP). In addition, a discussion on the methods used to measure cavitation and the factors that affect cavitation occurrence will be discussed. Two different types of cavitation nozzles were designed and tested. It has been shown that the self-resonating cavitation nozzle presents greater performance than standard non-resonating nozzle. It is thus concluded that a self-resonating cavitation jet present a high potential for improving drilling performance.

Keywords: cavitating jet, erosion, cavitation number, rate of penetration (ROP)

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Authors: Mouhammd Alkasassbeh, Alaa Lasasmeh

Abstract:

We live in an era of rapid development as a result of significant scientific growth. Like other technologies, wireless sensor networks (WSNs) are playing one of the main roles. Based on WSNs, ZigBee adds many features to devices, such as minimum cost and power consumption, and increasing the range and connect ability of sensor nodes. ZigBee technology has come to be used in various fields, including science, engineering, and networks, and even in medicinal aspects of intelligence building. In this work, we generated two main datasets, the first being based on tree topology and the second on star topology. The datasets were evaluated by three machine learning (ML) algorithms: J48, meta.j48 and multilayer perceptron (MLP). Each topology was classified into normal and abnormal (attack) network traffic. The dataset used in our work contained simulated data from network simulation 2 (NS2). In each database, the Bayesian network meta.j48 classifier achieved the highest accuracy level among other classifiers, of 99.7% and 99.2% respectively.

Keywords: IDS, Machine learning, WSN, ZigBee technology

Procedia PDF Downloads 524