Innovative machine learning techniques for security detection problems

University of Technology, Sydney. Faculty of Engineering and Information Technology.Most of the currently available network security techniques cannot cope with the dynamic and increasingly complex nature of the attacks on distributed computer systems. Therefore, an automated and adaptive defensive tool is imperative for computer networks. Alongside the existing techniques for preventing intrusions such as encryption and firewalls, Intrusion Detection System (IDS) technology has established itself as an emerging field that is able to detect unauthorized access and abuse of computer systems from both internal users and external offenders. Most of the novel approaches in this field have adopted Artificial Intelligence (AI) technologies such as Artificial Neural Networks (ANN) to improve detection performance. The true power and advantage of ANN lie in its ability to represent both linear and non-linear underlying functions and learn these functions directly from the data being modeled. However, ANN is computationally expensive due to its demanding processing power and this leads to the overfitting problem, i.e. the network is unable to extrapolate accurately once the input is outside of the training data range. These limitations challenge security systems with low detection rate, high false alarm rate and excessive computation cost. In this research, a novel Machine Learning (ML) algorithm is developed to alleviate those difficulties of conventional detection techniques used in available IDS. By implementing Adaptive Boosting and Semi-parametric radial-basis-function neural networks, this model aims at minimizing learning bias (how well the model fits the available sample data) and generalization variance (how stable the model is for unseen instances) at an affordable cost of computation. The proposed method is applied to a set of Security Detection Problems which aim to detect security breaches within computer networks. In particular, we consider two benchmarking problems: intrusion detection and anti-spam filtering. It is empirically shown that our technique outperforms other state-of-the-art predictive algorithms in both of the problems, with significantly increased detection accuracy, minimal false alarms and relatively low computation

Identifikasi Telapak Tangan menggunakan Jaringan Syaraf Tiruan Learning Vector Quantization (LVQ)

Sistem pengenalan diri (personal recognition) adalah sebuah sistem untuk mengenali identitas seseorang secara otomatis dengan menggunakan computer dengan kata sandi (password), ID card, atau PIN untuk mengidentifikasi seseorang. Namun,pengenalan diri dengan sistem tersebut memiliki beberapa kelemahan yaitu dapat dicuri dan mudah diduplikasi, memiliki kemungkinan seseorang untuk lupa dan beberapa password dapat diperkirakan sehingga dapat dimanfaatkan oleh orang-orang yang tidak bertanggungjawab. Untuk dapat mengenali seseorang secara otomatis dapat dilakukan secara komputasi, yaitu dengan menggunakan jaringan syaraf tiruan. Penelitian ini mengimplementasikan metode jaringan syaraf tiruan Learning Vector Quantization dengan objek pengenalan yaitu telapak tangan. Dalam penelitian ini model proses pengembangan perangkat lunak yang digunakan adalah Waterfall, sedangkan bahasa pemrograman yang digunakan adalah Matlab, dan sistem manajemen basis datanya adalah Microsoft Access. Keluaran dari aplikasi yang dikembangkan adalah identifikasi telapak tangan user. Dari hasil pengujian, tingkat akurasi dari aplikasi ini sebesar 74,66% dalam membedakan antar user yang satu dengan yang lain

Adaptive Multiple Experts System for personal identification using facial behaviour biometrics

Physiological and/or behavioural characteristics of humans such as face, gait and/or voice have been used in biometric recognition technology. Apart from these characteristics (which have been reported in the literature), the hypothesis of this research was to investigate if facial behaviour could be used for human identification. We analysed and proposed a multiple experts system, called Adaptive Multiple Experts System (AMES), for validating our hypothesis and analysis. We used the Japanese Female Facial Expression (JAFFE) database as it provides the facial behaviour traits for data collection. The experimental results indicate that facial behaviours may provide information about individual difference and, thus may be used as another behavioural biometric