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About me

I finished my PhD at the Division of Cybersecurity, Abertay University, Dundee, Scotland. I did both my undergraduate and masters degrees in Computer Science from Computer Science Department, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt.

Currently, I am lecturing at the Computer Science Department, Faculty of Computer and Information Sciences, Ain Shams University.

Intrusion Detection Systems Using Machine Learning And Deep Learning Techniques (PhD)

The thesis proposes an empirical investigation of ML and DL algorithms to detect known and unknown attacks in general- and special-purpose networks. The thesis further investigates how ML and DL algorithms can learn from a limited amount of data while retaining high accuracy.

To this effect, a special-purpose IoT dataset is generated and evaluated against six ML techniques. The challenges and limitations of identifying anomalies in special-purpose networks are identified and discussed.

In an attempt to reduce the need for large training datasets, this thesis investigates the utilisation of Few-Shot learning paradigm to train IDS using a limited amount of data. For this purpose, Siamese networks are used and evaluated in three scenarios.

This thesis further investigates the use of autoencoders to detect zero-day attacks. The zero-day attack detection experiments highlight the problem of discriminating benign-mimicking attacks. To overcome this challenge, an additional layer of feature abstraction is proposed; to improve accuracy through the cumulative aggregation of network traffic.

The results of this research demonstrate the effectiveness of the proposed approaches for IDS development. Siamese networks demonstrate their ability to learn from limited data. The proposed autoencoder models exhibit their potential to detect zero-day attacks. Finally, the significance of flow aggregation features in discriminating benign-mimicking attacks is demonstrated.

Supervised By:

- Dr. Natalie Coull

- Dr. Ethan Bayne

- Dr. Salma ElSayed

- Dr. Xavier Bellekens (external advisor)

Intelligent techniques for Protein Secondary structure prediction (MSc)

The thesis presents a study on five different approaches used for predicting the protein secondary structure given only the primary amino acid sequence. These approaches are: Case Based Reasoning, Artificial Neural Networks, Decision Tables, Decision Trees and Bayes Networks. Two different datasets are used with different sequence lengths and with proper distribution among different amino acids. In Case Based Reasoning, 8 different experiments are conducted resulting in prediction accuracy of 88%.In ANN, 1024 experiments are conducted using different computation parameters resulting in accuracy of 68%, 81% and 86% for predicting alpha, beta and alpha and beta together respectively.Then for the statistical techniques, ZeroR is used to determine the baseline accuracy for the other three. Eight experiments are conducted for each of the Decision Tree, Decision Table and Bayes Network. The accuracies reach 70%, 71% and 75% respectively. The discussed experiments reached a prediction accuracy of 88% for maximum and 75% on average. MATLAB, WEKA and myCBR are used in implantation.

Supervised By:

- Prof. Abdel-Badeeh Salem

- Prof. Mohamed Roushdy

- Dr. Wael Khalifa