Work place: Faculty of Science, Al-Azhar University, Cairo, Egypt
Research Interests: Pattern Recognition, Computer Vision, Nature Inspired Computing, Cyber Security, Artificial Intelligence
Heba F. Eid is an Associate Professor at Faculty of Science, Al-Azhar University, Egypt. She received her Ph.D. degree in Network Intrusion Detection and M.S. degree in Distributed database systems, both from Faculty of Science, Al-Azhar University, Egypt. Her research interests include multi-disciplinary environment involving computational intelligence, pattern recognition, computer vision, bio-inspired computing and cyber security. Dr. Heba has served as a reviewer for various international journals and a program committee member of several international conferences.
DOI: https://doi.org/10.5815/ijmsc.2022.04.03, Pub. Date: 8 Oct. 2022
Data security has become a significant issue nowadays with the increase of information capacity and its transmission rate. The most common and widely used techniques in the data security fields is cryptography. Cryptography is the process of concealing and transmitting data in an appropriate format, so that only authorized people can access and process it. The main goal of the cryptographic process is protecting data from being hijacked and altered. This paper proposes an algorithm for encrypting data through the use of Deoxyribo Nucleic Acid (DNA) sequence and four-dimensional hyper chaotic system. Whereby, the hyper chaotic system is applied to generate a binary sequence which is later passed to a permutation function for the key generation of the first level encryption. The proposed encryption algorithm includes several intermediate steps, which are binary-coded form and the generation of arbitrary keys. Experimental results were analyzed by calculating encryption time, key generation time, histogram and correlation coefficient entropy. Furthermore, the proposed text encryption algorithm is implemented on two different students’ datasets to improve the security of educational systems. Finally, experimental and comparative studies have shown that, the proposed encryption algorithm reported a uniform encrypted text distribution and correlation coefficient values nearer to ‘0’, which are close to the theoretical optimal value.[...] Read more.
DOI: https://doi.org/10.5815/ijcnis.2022.02.06, Pub. Date: 8 Apr. 2022
With the integration of cloud computing approaches in the healthcare systems, medical images are now processed and stored remotely on third-party servers. For such digital medical image data, privacy, protection, and security must be maintained by using image encryption methods. The aim of this paper is to design and apply a robust medical encryption framework to enhance the protection of medical image transformation and the patient information confidentiality. The proposed Framework encrypt the digital medical images using DNA computation and hyperchaotic RKF-45 random sequence approach. For which, the DNA computation is enhanced by applying hyperchaoticRKF-45 random key to the different Framework phases. The simulation results on different medical images were measured with various security analyses to prove the proposed framework randomness and coherent. Simulation results showed the ability of the hyperchaotic DNA encryption framework to withstand multiple electronic attacks with high performance compared to its counterparts of encryption algorithms. Finally, simulation and comparative studies have shown that, the proposed cryptography framework reported UACI and NPCR values 33.327 and 99.603 respectively, which are near to the theoretical optimal value.[...] Read more.
DOI: https://doi.org/10.5815/ijigsp.2019.03.03, Pub. Date: 8 Mar. 2019
One of the most difficult challenges in machine learning is the data attribute selection process. The main disadvantages of the classical optimization algorithms based attribute selection are local optima stagnation and slow convergence speed. This makes bio¬-inspired optimization algorithm a reliable alternative to alleviate these drawbacks. Whale optimization algorithm (WOA) is a recent bio-inspired algorithm, which is competitive to other swarm based algorithms. In this paper, a modified WOA algorithm is proposed to enhance the basic WOA performance. Furthermore, a wrapper attribute selection algorithm is proposed by integrating information gain as a preprocessing initialization phase. Experimental results based on twenty mathematical optimization functions demonstrate the stability and effectiveness of the modified WOA when compared to the basic WOA and the other three well-known algorithms. In addition, experimental results on nine UCI datasets show the ability of the novel wrapper attribute selection algorithm in selecting the most informative attributes for classification tasks.[...] Read more.
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