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@article{184194,
        author = {Amit Rameshchandra Bramhecha and Amit Kumar Upadhyay},
        title = {A Review on Xception-Kronecker Forward Fractional Net Architectures for Intrusion Detection in Cloud Environments},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {4},
        pages = {586-596},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=184194},
        abstract = {Intrusion detection for cloud computing has been an imperative research area with the growing cyber-attacks in the form of zero-day exploits, distributed denial-of-service (DDoS) attacks, and advanced persistent threats (APTs) that compromise confidentiality, integrity, and availability of information. Traditional intrusion detection systems (IDS), such as signature-based and traditional machine learning techniques, tend to fall short of scalability, high-dimensional traffic, false alarms, and limited flexibility. To overcome these shortcomings, recent developments have centered on deep learning and hybrid approaches with a specific focus on Xception-Kronecker Forward Fractional Net (XKFF-Net) architectures. This review gathers the literature from 2020 to 2025, emphasizing the importance of depth wise separable convolutions in Xception for fast feature extraction, Kronecker-based parameter compression for the sake of resource efficiency, and fractional-order learning for capturing intricate traffic patterns. Comparative evaluations on benchmarking datasets like NSL-KDD, CIC-IDS2017, UNSW-NB15, and CSE-CIC-IDS2018 show XKFF-Net's high potential to produce better detection accuracy, reduced false positives, and enhanced real-time adaptability. Promising results notwithstanding, challenges in dataset imbalance, adversarial robustness, and computational complexity persist. The review highlights XKFF-Net as a prime contender for next-generation IDS, promoting scalable, lightweight, and smart security solutions for cloud computing platforms.},
        keywords = {Cloud Computing, Intrusion Detection System, Advanced Persistent Threats, XKFF-Net.},
        month = {September},
        }