Wassim El-Hajj

Two factor authentication using mobile phones

This paper describes a method of implementing two factor authentication using mobile phones. The proposed method guarantees that authenticating to services, such as online banking or ATM machines, is done in a very secure manner. The proposed system involves using a mobile phone as a software token for One Time Password generation. The generated One Time Password is valid for only a short user-defined period of time and is generated by factors that are unique to both, the user and the mobile device itself. Additionally, an SMS-based mechanism is implemented as both a backup mechanism for retrieving the password and as a possible mean of synchronization. The proposed method has been implemented and tested. Initial results show the success of the proposed method.

Survey of Security Issues in Cognitive Radio Networks

Cognitive Radio (CR) is a novel technology that promises to solve the spectrum shortage problem by allowing secondary users to coexist with primary users without causing interference to their communication. Although the operational aspects of CR are being explored vigorously, its security aspects have gained little attention. In this paper, a brief overview of the CR technology is provided followed by a detailed analysis of the security attacks targeting Cognitive Radio Networks (CRNs) along with the corresponding mitigation techniques. We categorize the attacks with respect to the layer they target starting from the physical layer and moving up to the transport layer. An evaluation of the suggested countermeasures is presented along with other solutions and augmentations to achieve a secure and trusted CRN.

A Large Scale Arabic Sentiment Lexicon for Arabic Opinion Mining

Most opinion mining methods in English rely successfully on sentiment lexicons, such as English SentiWordnet (ESWN). While there have been efforts towards building Arabic sentiment lexicons, they suffer from many deficiencies: limited size, unclear usability plan given Arabic’s rich morphology, or nonavailability publicly. In this paper, we address all of these issues and produce the first publicly available large scale Standard Arabic sentiment lexicon (ArSenL) using a combination of existing resources: ESWN, Arabic WordNet, and the Standard Arabic Morphological Analyzer (SAMA). We compare and combine two methods of constructing this lexicon with an eye on insights for Arabic dialects and other low resource languages. We also present an extrinsic evaluation in terms of subjectivity and sentiment analysis.

Implementation of a Covert Channel in the 802.11 Header

Covert channels are an immense cause of security concern because they can be used to pass malicious messages. The messages could be in form of computer virus, spy programs, terrorist messages, etc. Most available techniques proposed covert channels that use the upper layers of the OSI model. In this paper, we discuss a novel covert channel in the data link layer dedicated to wireless local area networks. Depending on the configuration of the network, the covert channel uses either sequence control or initial vector fields, or both of them. We present also some measurements to protect the proposed covert channel against steganalysis processes and sniffing attack. Finally, the performance of proposed covert channel is compared with the available common TCP/IP covert channels regarding the offered bandwidth and the imperceptibly.

TARP: trust-aware routing protocol

Security is a critical issue in a mobile ad hoc network (MANET). In most of the previous protocols security is an added layer above the routing protocol. We propose a Trust-Aware Routing Protocol (TARP) for secure-trusted routing in mobile ad hoc networks. In TARP, security is inherently built into the routing protocol where each node evaluates the trust level of its neighbors based on a set of attributes and determines the route based on these attributes. This paper evaluates the proposed TARP protocols on two important attributes, the battery power and the software configuration. A secure route between a source and destination is established based on a confidence level prescribed by a user or application in terms of these attributes. Our performance evaluation shows that TARP is a robust and adaptive trust routing algorithm that reacts quickly and effectively to the dynamics of the network while still finding the shortest path to the destination. TARP is able to improve security and at the same time reduce the total routing traffic sent and received in the network by directing the traffic based on the requested sender attributes.

A Fuzzy-Based Hierarchical Energy Efficient Routing Protocol for Large Scale Mobile Ad Hoc Networks (FEER)

A mobile Ad-Hoc network (MANET) is a collection of autonomous arbitrarily located wireless mobile hosts, in which an infrastructure is absent. In this paper we propose a fuzzy-based hierarchical energy efficient routing scheme (FEER) for large scale mobile ad-hoc networks that aims to maximize the networks lifetime. Each node in the network is characterized by its residual energy, traffic, and mobility. We develop a fuzzy logic controller that combines these parameters, keeping in mind the synergy between them. The value obtained, indicates the importance of a node and it is used in network formation and maintenance. We compare our approach to another energy efficient hierarchical protocol based on the dominating set (DS) idea. Our simulation shows that our design out performs the DS approach in prolonging the network lifetime.

Optimizing Complex Cluster Formation in MANETs Using SAT/ILP Techniques

Over the course of the last decade, there have been several improvements in the performance of Integer Linear Programming (ILP) and Boolean Satisfiability (SAT) solvers. These improvements have encouraged the application of SAT and ILP techniques in modeling complex engineering problems. One such problem is the Clustering Problem in Mobile Ad-Hoc Networks (MANETs). The Clustering Problem in MANETs consists of selecting the most suitable nodes of a given MANET topology as clusterheads, and ensuring that regular nodes are connected to clusterheads such that the lifetime of the network is maximized. This paper proposes the development of an improved ILP formulation of the Clustering Problem. Additionally, various enhancements are implemented in the form of extensions to the improved formulation, including the establishment of intra-cluster communication, multihop connections and the enforcement of coverage constraints. The improved formulation and enhancements are implemented in a tool designed to visually create network topologies and cluster them using state-of-the art Generic ILP and SAT solvers. Through this tool, feasibility of using the proposed formulation and enhancements in a real-life practical environment is assessed. It is observed that the Generic ILP solvers, CPLEX, and SCIP, are able to handle large network topologies, while the 0–1 SAT-based ILP solver, BSOLO, is effective at handling the smaller scale networks. It is also observed that while these enhanced formulations enable the generation of complex network solutions, and are suitable for small scale networks, the time taken to generate the corresponding solution does not meet the strict requirements of a practical environment.

A fuzzy-based virtual backbone routing for large-scale MANETs

Virtual backbone-based routing is a promising approach for enhancing the routing efficiency in MANETs. To establish communication in the network, the virtual backbone nodes have to be connected. Connected Dominating Sets (CDS) are the earliest structures proposed as candidates for virtual backbones in Ad Hoc networks. In this paper, we propose a Fuzzy-based Virtual Backbone (FVB) routing scheme for large-scale mobile Ad Hoc networks that aims to maximise the networks lifetime. Each node in the network is characterised by its Residual Energy (RE), traffic and mobility. We develop a fuzzy logic controller that combines these parameters, keeping in mind the synergy between them. The value obtained indicates the importance of a node and it is used in network formation and maintenance. We compare our approach to another energy-efficient hierarchical protocol based on the Dominating Set (DS) idea. Our simulation shows that our design out performs the DS approach in prolonging the network lifetime.

On Detecting Port Scanning using Fuzzy Based Intrusion Detection System

Intrusion detection is a mechanism used to detect various attacks on a wired or wireless network. Port scanning is one of the dangerous attacks that intrusion detection tries to detect. Snort, a famous network intrusion detection system (NIDS), detects a port scanning attack by combining and analyzing various traffic parameters. Because these parameters cannot be easily combined using a mathematical formula, fuzzy logic can be used to combine them; fuzzy logic can also reduce the number of false alarms. This paper presents a novel approach, based on fuzzy logic, to detect port scanning attacks. A fuzzy logic controller is designed and integrated with Snort in order to enhance the functionality of port scanning detection. Experiments are carried out in both wired and wireless networks. The results show that applying fuzzy logic adds to the accuracy of determining bad traffic. Moreover, it gives a level of degree for each type of port scanning attack.

Emotion Recognition from Text Based on Automatically Generated Rules

With the growth of the Internet community, textual data has proven to be the main tool of communication in human-machine and human-human interaction. This communication is constantly evolving towards the goal of making it as human and real as possible. One way of humanizing such interaction is to provide a framework that can recognize the emotions present in the communication or the emotions of the involved users in order to enrich user experience. For example, by providing insights to users for personal preferences and automated recommendations based on their emotional state. In this work, we propose a framework for emotion classification in English sentences where emotions are treated as generalized concepts extracted from the sentences. We start by generating an intermediate emotional data representation of a given input sentence based on its syntactic and semantic structure. We then generalize this representation using various ontologies such as Word Net and Concept Net, which results in an emotion seed that we call an emotion recognition rule (ERR). Finally, we use a suite of classifiers to compare the generated ERR with a set of reference ERRs extracted from a training set in a similar fashion. The used classifiers are k-nearest neighbors (KNN) with handcrafted similarity measure, Point Mutual Information (PMI), and PMI with Information Retrieval (PMI-IR). When applied on different datasets, the proposed approach significantly outperformed the existing state-of-the art machine learning and rule-based classifiers with an average F-Score of 84%.