Browsing by Author "Ayad, Jenan"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Encryption of color images utilizing cascading 3D chaotic maps with S-Box algorithms
    (2023-08-25) Ayad, Jenan; Ali, Guma; Ullah, Waheed; Robert, Wamusi
    The fundamental characteristics of chaos, including sensitivity to beginning conditions and unpredictability, render it a prime candidate for cryptographic applications. This research introduces an encryption methodology for effective and safe image encryption. The encryption system has two ciphering phases and a substitution phase. This study proposes a method for key creation. The design of a pseudo-random number generator utilized for key generation is founded on chaotic algorithms. The chaotic map will be employed in encryption systems owing to its superior security. NIST tests are employed to assess the randomness of the proposed PRNG sequences. The subsequent part presents a security study of the suggested picture encryption approach to evaluate its efficacy. The statistical analysis now confirms that the technique is secure and efficient for encrypting both basic and complicated images, whether in monochrome or color. Through a comparison with previous chaotic investigations, it is evident that our method is competitive with earlier efforts.
  • Thumbnail Image
    Item
    Securing the internet of wetland things (IoWT) using machine and deep learning methods: a survey
    (Mesopotamian journal of Computer Science, 2025-02-03) Ali, Guma; Wamusi, Robert; Mijwil, Maad M.; Sallam, Malik; Ayad, Jenan; Adamopoulos, Ioannis
    Wetlands are essential ecosystems that provide ecological, hydrological, and economic benefits. However, human activities and climate change are degrading their health and jeopardizing their long-term sustainability. To address these challenges, the Internet of Wetland Things (IoWT) has emerged as an innovative framework integrating advanced sensing, data collection, and communication technologies to monitor and manage wetland ecosystems. Despite its potential, the IoWT faces substantial security and privacy risks, compromising its effectiveness and hindering adoption. This survey explores integrating machine learning (ML) and deep learning (DL) techniques as solutions to address the security threats, vulnerabilities, and challenges inherent in IoWT ecosystems. The survey examines findings from 231 sources, encompassing peer-reviewed journal articles, conference papers, books, book chapters, and websites published between 2020 and 2025. It consolidates insights from prominent platforms such as the Springer Nature, Emerald Insight, ACM Digital Library, Frontiers, Wiley Online Library, SAGE, Taylor & Francis, IGI Global, Springer, ScienceDirect, MDPI, IEEE Xplore Digital Library, and Google Scholar. Machine learning and DL methods have proven highly effective in detecting adversarial attacks, identifying anomalies, recognizing intrusions, and uncovering man-in-the-middle attacks, which are crucial in securing systems. These techniques also focus on detecting phishing, malware, and DoS/DDoS attacks and identifying insider and advanced persistent threats. They help detect botnet attacks and counteract jamming and spoofing efforts, ensuring comprehensive protection against a wide range of cyber threats. The survey examines case studies and the unique requirements and constraints of IoWT systems, such as limited energy resources, diverse sensor networks, and the need for real-time data processing. It also proposes future directions, such as developing lightweight, energy-efficient algorithms that operate effectively within the constrained environments typical of IoWT applications. Integrating ML and DL methods strengthens IoWT security while protecting and preserving wetlands through intelligent and resilient systems. These findings offer researchers and practitioners valuable insights into the current state of IoWT security, helping them drive and shape future advancements in the field.