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    Performance Comparison of Defense Mechanisms Against TCP SYN Flood DDoS Attack
    (ICUMT, 2014) Kolahi, Samad; Alghalbi, Amro A.; Alotaibi, Abdulmohsen F.; Ahmed, Saarim S.; Lad, Divyesh; Unitec Institute of Technology
    The TCP SYN DDoS attack and defense prevention mechanisms is studied. Each prevention mechanism has some exclusive pros and cons over the others. In this paper, we have compared various defence mechanisms for preventing potential TCP SYN DDoS attacks. Router based TCP Intercept is found to provide the best defense while Anti DDoS Guardian gave the worst defese.
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    Biologically Inspired Techniques for Data Mining: A Brief Overview of Particle Swarm Optimization for KDD. Alam
    (Information Science Reference, 2014-02-02) Shafiq, Alam; Gillian, Dobbie; Koh, Yun Sing; Rehman, Saeed; Unitec Institute of Technology; University of Auckland
    Knowledge Discovery and Data (KDD) mining helps uncover hidden knowledge in huge amounts of data. However, recently, different researchers have questioned the capability of traditional KDD techniques to tackle the information extraction problem in an efficient way while achieving accurate results when the amount of data grows. One of the ways to overcome this problem is to treat data mining as an optimization problem. Recently, a huge increase in the use of Swarm Intelligence (SI)-based optimization techniques for KDD has been observed due to the flexibility, simplicity, and extendibility of these techniques to be used for different data mining tasks. In this chapter, the authors overview the use of Particle Swarm Optimization (PSO), one of the most cited SI-based techniques in three different application areas of KDD, data clustering, outlier detection, and recommender systems. The chapter shows that there is a tremendous potential in these techniques to revolutionize the process of extracting knowledge from big data using these techniques
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    Security of Wireless Devices using Biological-Inspired RF Fingerprinting Technique
    (IGI Global, 2014-11-23) Rehman, Saeed; Alam, Shafiq; Ardekani, Iman; Unitec Institute of Technology; University of Auckland
    Radio Frequency (RF) fingerprinting is a security mechanism inspired by biological fingerprint identification systems. RF fingerprinting is proposed as a means of providing an additional layer of security for wireless devices. RF fingerprinting classification is performed by selecting an “unknown” signal from the pool, generating its RF fingerprint, and using a classifier to correlate the received RF fingerprint witheach profile RF fingerprint stored in the database. Unlike a human biological fingerprint, RF fingerprint of a wireless device changes with the received Signal to Noise Ratio (SNR) and varies due to mobility of the transmitter/receiver and environment. The variations in the features of RF fingerprints affect the classification results of the RF fingerprinting. This chapter evaluates the performance of the KNN and neural network classification for varying SNR. Performance analysis is performed for three scenarios that correspond to the situation, when either transmitter or receiver is mobile, and SNR changes from low to high or vice versa.