Mouhammd Alkasassbeh

Network fault detection with Wiener filter-based agent

Over the last few decades, network domains have become more and more advanced in terms of their size, complexity and level of heterogeneity. Existing centralized-based network management approaches suffer from problems such as insufficient scalability, availability and flexibility, as networks become more distributed. Mobile agents (MA), upgraded with intelligence, can present a reasonable new technology that will help to achieve distributed management. These agents migrate from one node to another, accessing an appropriate subset of Management Information Base (MIB) variables from each node analysing them locally and retaining the results of this analysis during their subsequent migration. One of the network fault management tasks is fault detection, and in this paper our purpose was to carry out a statistical method based on Wiener filter to capture the abnormal changes in the behaviour of the MIB variables. Our algorithm was implemented on data obtained from two different scenarios in the laboratory, with four different fault case studies. The purpose of this is to provide the manager node with a high level of information, such as a set of conclusions or recommendations, rather than large volumes of data relating to each management task. The filtering process is carried out concurrently by each agent responsible for a particular domain and device, proving to be more scalable and efficient.

Analysis of mobile agents in network fault management

Network domains have become more and more advanced in terms of their size, complexity and the level of heterogeneity. Comprehensive fault management is the most significant challenge in network management. Fault management can help increase the availability of the network by quickly identifying the faults and then, proactively, start the recovery process. Current centralized configured network management systems suffer from problems such as insufficient scalability, availability and flexibility as networks become more distributed. Mobile agents (MAs), with integral intelligence, can present a reasonable new technology that will help to achieve distributed management, several researchers have embraced these approaches. In this paper, we introduce a general analytical model for network management client/server (CS) and MA paradigms. We express how to build up an analytical framework, which can be used to quantitatively assess the performances of the MA and CS paradigms under different scenarios. We present some numerical and experimental results that demonstrate the applicability of our proposed framework, which will be based on a combination of MA and CS schemes called Adaptive Intelligent Mobile Agent.

Detecting Distributed Denial of Service Attacks Using Data Mining Techniques

Users and organizations find it continuously challenging to deal with distributed denial of service (DDoS) attacks. . The security engineer works to keep a service available at all times by dealing with intruder attacks. The intrusion-detection system (IDS) is one of the solutions to detecting and classifying any anomalous behavior. The IDS system should always be updated with the latest intruder attack deterrents to preserve the confidentiality, integrity and availability of the service. In this paper, a new dataset is collected because there were no common data sets that contain modern DDoS attacks in different network layers, such as (SIDDoS, HTTP Flood). This work incorporates three well-known classification techniques: Multilayer Perceptron (MLP), Naive Bayes and Random Forest. The experimental results show that MLP achieved the highest accuracy rate (98.63%).

Evaluation of machine learning algorithms for intrusion detection system

Intrusion detection system (IDS) is one of the implemented solutions against harmful attacks. Furthermore, attackers always keep changing their tools and techniques. However, implementing an accepted IDS system is also a challenging task. In this paper, several experiments have been performed and evaluated to assess various machine learning classifiers based on KDD intrusion dataset. It succeeded to compute several performance metrics in order to evaluate the selected classifiers. The focus was on false negative and false positive performance metrics in order to enhance the detection rate of the intrusion detection system. The implemented experiments demonstrated that the decision table classifier achieved the lowest value of false negative while the random forest classifier has achieved the highest average accuracy rate.

Comparative Analysis of Clustering Techniques in Network Traffic Faults Classification

Ubiquitous high-speed communication networks play a crucial role in the modern life, demanding the highest level of reliability and availability. Due to the rapid growth of computer networks in terms of size, complexity and heterogeneity, the probability of network faults increases. Manual network administration is hopelessly outdated; complex automated fault diagnosis and management are essential to ensure the provision and maintenance of high quality service in computer networks. Guaranteed Service with higher levels of reliability and availability for real-time applications can be achieved with a systematic approach for real-time classification of network faults, which helps in well-informed (often-automated) decision making. In this paper we discuss three different data mining algorithms as part of the proposed solution for network fault classification: K-Means, Fuzzy C Means, and Expectation Maximization. The proposed approach can help capture abnormal behavior in communication networks, thus paving the way for real-time fault classification and management. We used datasets obtained from a network with heavy and light traffic scenarios in the router and server and built a prototype to demonstrate the network traffic fault classification under given scenarios. Our empirical results reveal that the FCM is more accurate while causing computational overhead. The other two algorithms attain almost the same performance.

Magnetic energy-based feature extraction for low-quality fingerprint images

In fingerprint recognition systems, feature extraction is an important part because of its impact on the final performance of the overall system, particularly, in the case of low-quality images, which poses significant challenges to traditional fingerprint feature extraction methods. In this work, we make two major contributions: First, a novel feature extraction method for low-quality fingerprints images is proposed, which mimics the magnetic energy when attracting iron fillings, and this method is based on image energies attracting uniformly distributed points to form the final features that can describe a fingerprint. Second, we created a new low-quality fingerprints image database to evaluate the proposed method. We used a mobile phone camera to capture the fingerprints of 136 different persons, with five samples for each to obtain 680 fingerprint images in total. To match the computed features, we used the dynamic time warping and evaluated the performance of our system based on k-nearest neighbor classifier. Further, we represent the features using their probability density functions to evaluate the method using some other classifiers. The highest identification accuracy recorded by several experiments reached 95.11% using our in-house database. The experimental results show that the proposed method can be used as a general feature extraction method for other applications.

Enhancing genetic algorithms using multi mutations

Mutation is one of the most important stages of the genetic algorithm because of its impact on the exploration of global optima, and to overcome premature convergence. There are many types of mutation, and the problem lies in selection of the appropriate type, where the decision becomes more difficult and needs more trial and error. This paper investigates the use of more than one mutation operator to enhance the performance of genetic algorithms. Novel mutation operators are proposed, in addition to two selection strategies for the mutation operators, one of which is based on selecting the best mutation operator and the other randomly selects any operator. Several experiments on some Travelling Salesman Problems (TSP) were conducted to evaluate the proposed methods, and these were compared to the well-known exchange mutation and rearrangement mutation. The results show the importance of some of the proposed methods, in addition to the significant enhancement of the genetic algorithms performance, particularly when using more than one mutation operator.