My research work belongs to a couple of fields, I became interested in during my graduate studies. Previously in my masters I got interested in image processing resulting in a thesis with the title of image compression of facial photographs. Currently and during my Ph.D. studies I got interested in different aspects of computer security. Precisely, my dissertation is concerned with intrusion detection systems and applying concepts of artificial immune systems to perform detection. I am looking forward in continuing my research in both security and image processing.

Immunity-based Intrusion detection Systems
Currently developed intrusion detection systems (IDS) are showing promising results. Danger theory, which is an artificial immune system based methodology, is being tested to improve the performance of immunity-based IDSs. It builds on the idea that detecting intrusions is not only associated with foreignness but also being dangerous. Immune based systems build on two concepts: adaptive and innate immunity. The adaptive immunity (acquired immunity) is similar to anomaly detection where with exposure to different antigens the acquired immune system learns to identify different pathogens and respond to them more effectively [9]. The adaptive immune system is organized around two classes of cells: T cells and B cells [6] and its functionality is related to the Danger theory. Innate immunity is meant to protect the body from birth and attacks antigens right away although it has not been exposed to pathogens before. It has two different actions: rapid action which lasts from four minutes to four hours performed by macrophages. There is also a medium to slow action performed via inflammation or by natural killer (NK) cells [9]. Innate system is the first line of protection and when it fails, an infection is established and the acquired immunity starts to develop. The cells of the innate immune system are numerous, including natural killer (NK) cells, dendritic cells (DCs) [5].

Image Compression
An image compression algorithm that is tailored to compress gray-scale facial photographs was developed [1]. The proposed technique treats blocks in the head region differently and adapts itself to the local nature of the face region. Block Truncation Coding (BTC) [2][3][7][8] and Tree Structured Vector Quantization (TSVQ) [4] techniques were previously used to globally compress the head region. The BTC method produced high fidelity reconstructed images but with low compression ratios. The TSVQ technique, on the other hand, is known for its high compression ratios but low visual quality of the reconstructed facial features. The proposed technique first locates the head-shoulder region and then locally processes blocks in the head region and encodes active blocks containing edges with the BTC technique. It encodes inactive blocks having low intensity variations between its pixels with the TSVQ technique. The cheeks and the forehead regions are therefore encoded using the TSVQ technique whereas the eyes regions are encoded using the BTC technique. It, therefore, takes advantage of the merits of both the BTC and the TSVQ methods to achieve a high quality and high compression ratios of the images. Three compression techniques are compared, which is applying the BTC technique globally, the TSVQ technique globally, and the combined BTC/TSVQ technique locally to the head region. At a threshold value (equal to 50) and a block size 2x2, the average compression ratio of the proposed technique is 1.8. It compresses a 92x112x8-bit (10304 bytes in total) facial photograph to a size of around 5000 bytes. From experimental results, the proposed combined method is found to yield better image quality than the TSVQ technique at the same codeword length and block size. Its compression ratio is also higher than that of the BTC technique at the same block size.

References
[1] Amer, Suhair H. Image Compression of Facial photographs based on BTC/TSVQ Local Processing. Master Thesis. Computer Science Department, The American University in Cairo. 2000. http://www.aucegypt.edu/academic/gradstudies/theses/9900.htm
[2] Dasarathy, Belur V., Image Data Compression: Block Truncation Coding. IEEE Computer Society Press, Los Angelus, 1995.
[3] Delp, E. J., and Mitchell, O. R., Image Compression Using Block Truncation Coding, IEEE Trans. Comm., Vol. COM-27, pp. 1335-1342, Sept. 1979.
[4] Gray, R. M., Cosman, P. C., and Riskin, E. A., Image Compression and Tree-Structured Vector Quantization, Image and Text Compression, Kluwer Academic Press, Norwell, MA. 1992.
[5] Greensmith, J., Aickelin, U. and Twycross, J. Articulation and Clarification of the Dentric Cell Algorithm, Proceedings of the 5th International Conference on Artificial Immune Systems. (ICARIS 2006) LNCS 4163, pp 404-417. Oeiras, Prtugal.
[6] Kim J, Greensmith J, Twycross J and Aickelin U. Malicious Code Execution Detection and Response Immune System inspired by the Danger Theory, Adaptive and Resilient Computing Security Workshop (ARCS 2005), Santa Fe, USA
[7] Nasiopoulos, Pnos and Ward, Rabab K., Image Compression for Facial Photographs, http://pella.eng.auth.gr/workshop/papers/p_18_2.html. (accessed September 1999)
[8] Nasiopoulous, Panos, Ward, Rabab K., and Morse, Daryl J., ?Adaptive Compression Coding?, IEEE Trans. Comm., Vol. COM-39, No. 8, pp. 1245-1254, Aug. 1991.
[9] Pagnoni, Anastasia and Visconti, Andrea. An innate immune system for the protection of computer networks. ACM International Conference Proceeding Series; Vol. 92 archive Proceedings of the 4th international symposium on Information and communication technologies. 2005.