Browsing by Author "Ahmed, Jameel"

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    Data Encryption Scheme Based On Adaptive System
    (IEEE, 2020-10-06) Najam, Shaheryar; Rehman, Mujeeb Ur; Ahmed, Jameel
    World has evolved into digital hub, lots of digital data such as images, videos, text and audio signal are transmitted over the internet. Due to increase in this multimedia communication, there has been a rapid increase in requirement of encryption of these data. Various encryption scheme has been proposed to make these multimedia communications more and more secure. In this paper, a new approach of adaptive system has been presented. Adaptive system previously used for prediction, noise removal and system identification has been deployed to encrypt data and results have shown, adaptive system can be used to encrypt signal with high rate of randomness in encrypted signal. Furthermore, randomness can be enhanced by changing the desire signal.
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    Detecting the Security Level of Various Cryptosystems Using Machine Learning Models
    (IEEE, 2020-12-22) Shafique, Arslan; Ahmed, Jameel; Boulila, Wadii; Ghandorh, Hamzah; Ahmad, Jawad; Rehman, Mujeeb Ur
    With recent advancements in multimedia technologies, the security of digital data has become a critical issue. To overcome the vulnerabilities of current security protocols, researchers tend to focus their efforts on modifying existing protocols. Over the last few decades, though, several proposed encryption algorithms have been proven insecure, leading to major threats against important data. Using the most appropriate encryption algorithm is a very important means of protection against such attacks, but which algorithm is most appropriate in any particular situation will also be dependent on what sort of data is being secured. However, testing potential cryptosystems one by one to find the best option can take up an important processing time. For a fast and accurate selection of appropriate encryption algorithms, we propose a security level detection approach for image encryption algorithms by incorporating a support vector machine (SVM). In this work, we also create a dataset using standard encryption security parameters, such as entropy, contrast, homogeneity, peak signal to noise ratio, mean square error, energy, and correlation. These parameters are taken as features extracted from different cipher images. Dataset labels are divided into three categories based on their security level: strong, acceptable, and weak. To evaluate the performance of our proposed model, we have performed different analyses (f1-score, recall, precision, and accuracy), and our results demonstrate the effectiveness of this SVM-supported system.
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    Noise-Resistant Image Encryption Scheme for Medical Images in the Chaos and Wavelet Domain
    (IEEE, 2021-04-07) Shafique, Arslan; Ahmed, Jameel; Rehman, Mujeeb Ur; Hazzazi, Mohammad Mazyad
    In this paper, a noise-resistant image encryption scheme is proposed. We have used a cubic-logistic map, Discrete Wavelet Transform (DWT), and bit-plane extraction method to encrypt the medical images at the bit-level rather than pixel-level. The proposed work is divided into three sections; In the first and the last section, the image is encrypted in the spatial domain. While the middle section of the proposed algorithm is devoted to the frequency domain encryption in which DWT is incorporated. As the frequency domain encryption section is a sandwich between the two spatial domain encryption sections, we called it a ”sandwich encryption.” The proposed algorithm is lossless because it can decrypt the exact pixel values of an image. Along with this, we have also gauge the proposed scheme's performance using statistical analysis such as entropy, correlation, and contrast. The entropy values of the cipher images generated from the proposed encryption scheme are more remarkable than 7.99, while correlation values are very close to zero. Furthermore, the number of pixel change rate (NPCR) and unified average change intensity (UACI) for the proposed encryption scheme is higher than 99.4% and 33, respectively. We have also tested the proposed algorithm by performing attacks such as cropping and noise attacks on enciphered images, and we found that the proposed algorithm can decrypt the plaintext image with little loss of information, but the content of the original image is visible.
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    Signal Analysis and Anomaly Detection of IoT-Based Healthcare Framework
    (IEEE, 2021-04-07) Nawaz, Menaa; Ahmed, Jameel; Abbas, Ghulam; Rehman, Mujeeb Ur
    In the past few years, the idea of trading information while multiple devices are connected and communicating with each other, has grabbed huge attention. For processing this large amount of information, data connectivity and data analytics techniques have become essential for making decisions in variety of areas. Applying Internet of Things (IoT) solutions in healthcare systems is one of the rising areas that researchers are interested in. The usage of IoT technology has brought convenience for patients and physicians by real-time monitoring and managing healthcare and patient information. Corporations are in competition with each other to create IoT based devices with specific cloud interface but with least capability to analyse the data. In this research, an implementation of IoT based healthcare system using bio-medical sensors has been presented. This paper also aims to provide the analysis of cloud data acquired through biomedical sensors using signal analysis techniques for anomaly detection.