Ismael Jannoud

On preventing ARP poisoning attack utilizing Software Defined Network (SDN) paradigm

In this work, SDN has been utilized to alleviate and eliminate the problem of ARP poisoning attack. This attack is the underlying infrastructure for many other network attacks, such as, man in the middle, denial of service and session hijacking. In this paper we propose a new algorithm to resolve the problem of ARP spoofing. The algorithm can be applied in two different scenarios. The two scenarios are based on whether a network host will be assigned a dynamic or a static IP address. We call the first scenario SDN_DYN; the second scenario is called SDN_STA. For the evaluation process, a physical SDN-enabled switch has been utilized with Ryu controller. Our results show that the new algorithm can prevent ARP spoofing and other attacks exploiting it.

Capacity of Large-Scale Wireless Networks Under Jamming: Modeling and Analyses

Distributed jamming has important applications not only in the military context but also in the civilian context, where spectrum sharing is increasingly used and inadvertent jamming becomes a reality. In this paper, we derive the capacity bounds of wireless networks in the presence of jamming. We show that when the density of jammers is higher than that of target nodes by a certain threshold, the capacity of wireless networks approaches zero as the numbers of target nodes and jammers go to infinity. This is true even when the total power of target nodes is much higher than that of the jammers. We provide the optimal communication schemes to achieve the capacity bounds. We also describe the power efficiency of wireless networks, showing that there is an optimal target node density for power-efficient network operation. Our results can provide guidance for designing optimal wireless networking protocols that have to deal with large-scale distributed jamming.

The Impact of 16-bit and 32-bit ASNs Coexistence on the Accuracy of Internet AS Graph

Modeling Internet structure as an autonomous system (AS) graph has attracted researchers over years. AS graph model demonstrates the power-law distribution of the Internet. It also demonstrates the relationship between the cluster coefficient and the small-world structure of the Internet. To obtain an accurate AS graph model, the data used to generate the graph should be massive and correct. In this work, we studied the correctness of the data that is utilized to generate Internet AS graph. We conducted an experiment to measure the popularity of 32-bit AS numbers (ASNs) in the Internet. We examined the impact of the special purpose 16-bit reserved ASN AS23456 on the accuracy of the AS graph. To this end, we proposed a cleaning algorithm to correct the conflict that AS23456 produces. Our results show that the current existing method of mapping 16-bit and 32-bit ASNs reduces the fidelity of the constructed AS graph to various graph parameters.

Software Project Management: Resources Prediction and Estimation Utilizing Unsupervised Machine Learning Algorithm

Software project effort estimation is a major process in software development cycle. This process helps in decision making in resource allocation and distribution. In this work, a new effort estimation clustering method based on estimation maximization soft-clustering unsupervised machine learning algorithm is proposed. This model classifies any software project into one of four categories. An enterprise will accept to develop a software project if this project is clustered into a class that requires resources equal or less than the enterprises resources. The new model helps in decision making process in one hand and helps consumers in assigning projects to a developing enterprise in the other hand. COCOMO dataset has been used to implement, deploy and test the model. The propose model has been compared with K-means algorithm to show the differences between soft and hard clustering. The paper results show that soft-clustering has the ability to estimate efforts like any supervised machine learning algorithms.

On Understanding Centrality in Directed Citation Graph

Modeling complex networks as directed/undirected graphs is considered one of the most common methods in network science. Citation graph is a directed graph of scientific published papers. This graph has been studied massively in the past decade. Citation graph can be utilized to study relationships between authors and papers. It can be used to study the characteristics of citation network to demonstrate the growth model, graph type and to predicted hot new topics. In this paper, we attempt to study the relationship between popularity of a paper and the publication date. The purpose of this study is to demonstrate the relation between paper quality and hot topics. Betweenness metric has been used to measure the popularity of a published paper. Moreover, a comparison between betweenness and citation count (node degree) has been conducted to show that papers may have a small citation count, however, they may have a great impact in research field. We have generated a directed citation graph by crawling paper information from CiteSeerx. Our study shows that date of publication is important to write a popular paper. However, high quality papers get opportunity to be popular regardless the date of publication.

Studying the Impact of 32-Bits AS Numbers on Constructing Internet AS Graphs

Modeling Internet structure as an autonomous system (AS) graph is considered one of the most common methods to study the Internet. AS-graph is constructed by utilizing ASes from the Internet. The AS number was 16-bit address. It reduced the size of ASes scope in one hand and made the AS graph small and easy to be produced and modeled. However, the number of ASes has exceeded the 16-bits limits. The 32-bit addressing has emerged as a method to tackle this problem. This increasing in the AS number scope converted the AS graph into a massive graph with unpredicted number of ASes. In this paper, we attempt to study the impact of 32-bits addresses on the AS graph. Graph parameters have been utilized to measure this impact. To this end, we have constructed two AS-graphs, a 16-bits-AS graph and a full AS graph. We have compared these graphs according to cluster coefficient, betweenness centrality, node degree and average shortest path. Our results demonstrated that the 32-bits ASes are popular. Moreover, these ASes have an effect on reducing the value of the global cluster coefficient and increasing the average shortest path. We observed that the number of vertexes that connect 16-bits and 32-bits ASes is small and requires more inferring.