An Intelligent Classification Model for Phishing Email Detection

Adwan Yasin and Abdelmunem Abuhasan

College of Engineering and Information Technology, Arab American University, Palestine

ABSTRACT

Phishing attacks are one of the trending cyber-attacks that apply socially engineered messages that are communicated to people from professional hackers aiming at fooling users to reveal their sensitive information, the most popular communication channel to those messages is through users’ emails. This paper presents an intelligent classification model for detecting phishing emails using knowledge discovery, data mining and text processing techniques. This paper introduces the concept of phishing terms weighting which evaluates the weight of phishing terms in each email. The pre-processing phase is enhanced by applying text stemming and Word Net ontology to enrich the model with word synonyms. The model applied the knowledge discovery procedures using five popular classification algorithms and achieved a notable enhancement in classification accuracy; 99.1% accuracy was achieved using the Random Forest algorithm and 98.4% using J48, which is –to our knowledge- the highest accuracy rate for an accredited data set. This paper also presents a comparative study with similar proposed classification techniques.

KEYWORDS

Phishing, data mining, email classification, Random Forest, J48.

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A Benchmark for Designing Usable and Secure Text-Based Captchas

Suliman A. Alsuhibany

Computer Science Department, College of Computer, Qassim University, Buridah, Saudi Arabia

ABSTRACT

An automated public Turing test to distinguish between computers and humans known as CAPTCHA is a widely used technique on many websites to protect their online services from malicious users. Two fundamental aspects of captcha considered in various studies in the literature are robustness and usability. A widely accepted standard benchmark, to guide the text-based captcha developers is not yet available. So this paper proposes a benchmark for designing usable-secure text-based captchas based on a community driven evaluation of the usability and security aspects. Based on this benchmark, we develop four new text based captcha schemes, and conduct two separate experiments to evaluate both the security and usability perspectives of the developed schemes. The result of this evaluation indicates that the proposed benchmark provides a basis for designing usable-secure text-based captchas.

KEYWORDS

Text-Based CAPTCHA, security, usability, benchmark

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Performance Analysis Of The Neighbor Weight Trust Determination Algorithm In Manets

Ali Abu Romman1and Hussein Al-Bahadili2

1King Hussein Faculty for Computing Sciences, Princess Sumaya University for

Technology, Amman, Jordan

2Faculty of Information Technology, University of Petra, Amman, Jordan

ABSTRACT

Mobile ad-hoc networks (MANETs) are susceptible to attacks by malicious nodes that could easily bring down the whole network. Therefore, it is important to have a reliable mechanism for detecting and isolating malicious nodes before they can do any harm to the network. One of the possible mechanisms is by using trust-based routing protocols. One of the main requirements of such protocols is to have a cost-effective trust determination algorithm. This paper presents the performance analysis of a recently developed trust determination algorithm, namely, the neighbor-weight trust determination (NWTD) algorithm. The performance of the algorithm is evaluated through simulation using the MANET simulator (MANSim). The simulation results demonstrated the reliability and effectiveness of the algorithm in identifying and isolating any maliciously behaving node(s) in a timely manner.

KEYWORDS

NWTD; trust determination; trust-based routing protocols; malicious node; MANET; MANSim.

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Email Spam Classification Using Hybrid Approach of RBF Neural Network and Particle Swarm Optimization

Mohammed Awad1and Monir Foqaha2
1Department of Computer Systems Engineering, Arab American University-Jenin,
Palestine
2Department of Computer Science, Arab American University-Jenin, Palestine

ABSTRACT

Email is one of the most popular communication media in the current century; it has become an effective and fast method to share and information exchange all over the world. In recent years, emails users are facing problem which is spam emails. Spam emails are unsolicited, bulk emails are sent by spammers. It consumes storage of mail servers, waste of time and consumes network bandwidth.Many methods used for spam filtering to classify email messages into two groups spam and non-spam. In general, one of the most powerful tools used for data classification is Artificial Neural Networks (ANNs); it has the capability of dealing a huge amount of data with high dimensionality in better accuracy. One important type of ANNs is the Radial Basis Function Neural Networks (RBFNN) that will be used in this work to classify spam message. In this paper, we present a new approach of spam filtering technique which combines RBFNN and Particles Swarm Optimization (PSO) algorithm (HC-RBFPSO). The proposed approach uses PSO algorithm to optimize the RBFNN parameters, depending on the evolutionary heuristic search process of PSO. PSO use to optimize the best position of the RBFNN centers c. The Radii r optimize using K-Nearest Neighbors algorithmand the weights w optimize using Singular Value Decomposition algorithm within each iterative process of PSO depending the fitness (error) function. The experiments are conducted on spam dataset namely SPAMBASE downloaded from UCI Machine Learning Repository. The experimental results show that our approach is performed in accuracy compared with other approaches that use the same dataset.

KEYWORDS

Email Spam, Classification, Radial Basis Function Neural Networks, Particles Swarm Optimization.

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Proactive Detection of Ddos Attacks In Publish-Subscribe Networks

Bander Alzahrani1, Vassilios Vassilakis2, Mohammed Alreshoodi3, Fawaz Alarfaj4

and Ahmed Alhindi5

1Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah,

Saudi Arabia

2School of Computing, Engineering and Mathematics, University of Brighton, Brighton,

United Kingdom

3College of Computer, Qassim University, Buraydah, Saudi Arbaia

4Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Saudi Arabia

5College of Computers and Information Systems, Umm Al-Qura University, Makkah,

Saudi Arabia

ABSTRACT

Abstract. Information centric networking (ICN) using architectures such as Publish-Subscribe Internet Routing Paradigm (PSIRP) or Publish-Subscribe Internet Technology (PURSUIT) has been proposed as an important candidate for the Internet of the future. ICN is an emerging research area that proposes a transformation of the current host centric Internet architecture into an architecture where information items are of primary importance. This change allows network functions such as routing and locating to be optimized based on the information items themselves. The Bloom filter based content delivery is a source routing scheme that is used in the PSIRP/PURSUIT architectures. Although this mechanism solves many issues of today’s Internet such as the growth of the routing table and the scalability problems, it is vulnerable to distributed denial-of-service (DDoS) attacks. In this paper, we present a new content delivery scheme that has the advantages of Bloom filter based approach while at the same time being able to prevent DDoS attacks on the forwarding mechanism. Our security analysis suggests that with the proposed approach, the forwarding plane is able to resist attacks such as DDoS with very high probability.

KEYWORDS

Distributed denial-of-service attack; information centric network; Bloom filter.

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