Hesham N. Elmahdy

Bio: Hesham N. Elmahdy is an academic researcher from Cairo University. The author has contributed to research in topics: Wireless sensor network & Network packet. The author has an hindex of 10, co-authored 42 publications receiving 325 citations. Previous affiliations of Hesham N. Elmahdy include Ain Shams University.

Human Ear Recognition Using Geometrical Features Extraction

Abstract: The biometrics recognition has been paid more attention by people with the advancement of technology nowadays. The human ear is a perfect source of data for passive person identification. Ear seems to be a good candidate solution since ear is visible, their images are easy to take and structure of ear does not change radically over time. Ear satisfies biometric characteristic (universality, distinctiveness, permanence and collectability). In this paper we presented a new algorithm for ear recognition based on geometrical features extraction like (shape, mean, centroid and Euclidean distance between pixels). Firstly, we made a pre-processing phase by making all images have the same size. Then we used the snake model to detect the ear, and we applied median filter to remove noise, also we converted the images to binary format. After that we used canny edge and made some enhancement on the image, largest boundary is calculated and distance matrix is created then we extracted the image features. Finally, the extracted features were classified by using nearest neighbor with absolute error distance. This method is invariant to scaling, translation and rotation. The experimental results showed that the proposed approach gives better results and obtained over all accuracy almost 98%.

Flower Pollination Optimization Algorithm for Wireless Sensor Network Lifetime Global Optimization

Abstract: As wireless sensor networks still struggling to extend its lifetime, nodesclustering and nomination, or selection of cluster head node are proposed as solution. LEACH protocol is one of the oldest remarkable clustering approaches that aim to cluster the networks nodes and randomly elects a cluster head for each cluster. It selects cluster heads but it is not responsible for proper clustering formation. In this paper we use the Flower Pollination Optimization Algorithm (FPOA) to propose a WSN energy aware clustering formation model based on the intra-cluster distances. The objective is to achieve the global optimization for WSN lifetime. Simulation results and performance analysis show that applying flower pollination optimization on WSNs clustering is more efficient. It is effectively balance power utilization of each sensor node and hence extends WSN lifetime comparatively with the classical LEACH approach.

A New Approach for Evaluating Intrusion Detection System

Abstract: The anomaly based intrusion detection system (IDS) is widely used based on different machine learning algorithms. The IDS is usually evaluated by its ability to make accurate predictions of attacks. In case of the binary classifier IDS four possible outcomes are possible. Attacks correctly predicted as attacks (TP), or incorrectly predicted as normal (FP). Normal correctly predicted as normal (TN), or incorrectly predicted as attack (FN). However, in case of multi classifier, when a class of attack is incorrectly predicted as another class of attack, it could not be any of the existing four instances. In this paper, a new approach is proposed to evaluate the anomaly based IDS. A new proposed metric F-score per Cost (FPC) is a one value calculated for each attack predictor. A new instance misclassification of attack class “MC” is proposed to represent the cases of wrong predicted attacks as another attack class. Based on the five instances a numerical evaluation can apply different measures to quantify the performance of IDS. In order to test the effectiveness of the proposed approach, three competitors of the “KDD CUP’99” competition are selected to measure their results by the proposed metrics. The results show that it was effective to add the MC instance. It achieves deep understanding of the IDS performance, and makes it more accurate to compare different intrusion detection systems and reflects the trade-off between the harmonic mean of the sensitivity, precision of the IDS and the misclassification paid against its detection accuracy.

A survey on human detection surveillance systems for Raspberry Pi

Abstract: Building reliable surveillance systems is critical for security and safety. A core component of any surveillance system is the human detection model. With the recent advances in the hardware and embedded devices, it becomes possible to make a real-time human detection system with low cost. This paper surveys different systems and techniques that have been deployed on embedded devices such as Raspberry Pi. The characteristics of datasets, feature extraction techniques, and machine learning models are covered. A unified dataset is utilized to compare different systems with respect to accuracy and performance time. New enhancements are suggested, and future research directions are highlighted.

Routing Wireless Sensor Networks based on Soft Computing Paradigms: Survey

Abstract: Wireless Sensor Networks (WSNs) are defined as dy namic, self-deployed, highly constrained structured network. Its high computational environment with limited and controlled transmission range, processing, as well as limited energy sources. The sever power constraints strongly affect the existence of act ive nodes and hence the network lifetime. In order to prolong the network life time we have to overco me the scarcity in energy resources and preserve the processing of the sensor nodes as long as possible. Power management approaches efficiently reduce thesensor nodes energy consumption individually in each sensor node and the adaptive efficient routing technique has greatly appeals a great attention in research. The potential paradigms of soft-computing (SC) highly addressed their adaptability and compati bility to overwhelm the complex challenges in WSNs. This paper is introducing and surveying some of the Soft Computing proposed routing models for WSNs that optimally prolongs its life time.

Secure Medical Data Transmission Model for IoT-Based Healthcare Systems

Abstract: Due to the significant advancement of the Internet of Things (IoT) in the healthcare sector, the security, and the integrity of the medical data became big challenges for healthcare services applications. This paper proposes a hybrid security model for securing the diagnostic text data in medical images. The proposed model is developed through integrating either 2-D discrete wavelet transform 1 level (2D-DWT-1L) or 2-D discrete wavelet transform 2 level (2D-DWT-2L) steganography technique with a proposed hybrid encryption scheme. The proposed hybrid encryption schema is built using a combination of Advanced Encryption Standard, and Rivest, Shamir, and Adleman algorithms. The proposed model starts by encrypting the secret data; then it hides the result in a cover image using 2D-DWT-1L or 2D-DWT-2L. Both color and gray-scale images are used as cover images to conceal different text sizes. The performance of the proposed system was evaluated based on six statistical parameters; the peak signal-to-noise ratio (PSNR), mean square error (MSE), bit error rate (BER), structural similarity (SSIM), structural content (SC), and correlation. The PSNR values were relatively varied from 50.59 to 57.44 in case of color images and from 50.52 to 56.09 with the gray scale images. The MSE values varied from 0.12 to 0.57 for the color images and from 0.14 to 0.57 for the gray scale images. The BER values were zero for both images, while SSIM, SC, and correlation values were ones for both images. Compared with the state-of-the-art methods, the proposed model proved its ability to hide the confidential patient’s data into a transmitted cover image with high imperceptibility, capacity, and minimal deterioration in the received stego-image.

On the Feasibility of Deep Learning in Sensor Network Intrusion Detection

Abstract: In this letter, we present a comprehensive analysis of the use of machine and deep learning (DL) solutions for IDS systems in wireless sensor networks (WSNs). To accomplish this, we introduce restricted Boltzmann machine-based clustered IDS (RBC-IDS), a potential DL-based IDS methodology for monitoring critical infrastructures by WSNs. We study the performance of RBC-IDS, and compare it to the previously proposed adaptive machine learning-based IDS: the adaptively supervised and clustered hybrid IDS (ASCH-IDS). Numerical results show that RBC-IDS and ASCH-IDS achieve the same detection and accuracy rates, though the detection time of RBC-IDS is approximately twice that of ASCH-IDS.

Elements Of Style

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Video Coding for Streaming Media Delivery on the Internet

Abstract: We provide an overview of an architecture of today's Internet streaming media delivery networks and describe various problems that such systems pose with regard to video coding. We demonstrate that based on the distribution model (live or on-demand), the type of the network delivery mechanism (unicast versus multicast), and optimization criteria associated with particular segments of the network (e.g., minimization of distortion for a given connection rate, minimization of traffic in the dedicated delivery network, etc.), it is possible to identify several models of communication that may require different treatment from both source and channel coding perspectives. We explain how some of these problems can be addressed using a conventional framework of temporal motion-compensated, transform-based video compression algorithm, supported by appropriate channel-adaptation mechanisms in client and server components of a streaming media system. Most of these techniques have already been implemented in RealNetworks(R) RealSystem(R) 8 and its RealVideo(R) 8 codec, which we use throughout the paper to illustrate our results.

Detection of advanced persistent threat using machine-learning correlation analysis

Abstract: As one of the most serious types of cyber attack, Advanced Persistent Threats (APT) have caused major concerns on a global scale APT refers to a persistent, multi-stage attack with the intention to compromise the system and gain information from the targeted system, which has the potential to cause significant damage and substantial financial loss The accurate detection and prediction of APT is an ongoing challenge This work proposes a novel machine learning-based system entitled MLAPT, which can accurately and rapidly detect and predict APT attacks in a systematic way The MLAPT runs through three main phases: (1) Threat detection, in which eight methods have been developed to detect different techniques used during the various APT steps The implementation and validation of these methods with real traffic is a significant contribution to the current body of research; (2) Alert correlation, in which a correlation framework is designed to link the outputs of the detection methods, aims to identify alerts that could be related and belong to a single APT scenario; and (3) Attack prediction, in which a machine learning-based prediction module is proposed based on the correlation framework output, to be used by the network security team to determine the probability of the early alerts to develop a complete APT attack MLAPT is experimentally evaluated and the presented system is able to predict APT in its early steps with a prediction accuracy of 848%