All Outputs (6)

TPAAD: two‐phase authentication system for denial of service attack detection and mitigation using machine learning in software‐defined network. (2024)
Journal Article
NISA, N., KHAN, A.S., AHMAD, Z. and ABDULLAH, J. 2024. TPAAD: two-phase authentication system for denial of service attack detection and mitigation using machine learning in software-defined network. International journal of network management [online], Early View, article number e2258. Available from: https://doi.org/10.1002/nem.2258

Software-defined networking (SDN) has received considerable attention and adoption owing to its inherent advantages, such as enhanced scalability, increased adaptability, and the ability to exercise centralized control. However, the control plane of... Read More about TPAAD: two‐phase authentication system for denial of service attack detection and mitigation using machine learning in software‐defined network..

MS-ADS: multistage spectrogram image-based anomaly detection system for IoT security. (2023)
Journal Article
AHMAD, Z., KHAN, A.S., ZEN, K. and AHMAD, F. 2023. MS-ADS: multistage spectrogram image-based anomaly detection system for IoT security. Transactions on emerging telecommunications technologies [online], 34(8), article number e4810. Available from: https://doi.org/10.1002/ett.4810

The innovative computing idea of Internet-of-Things (IoT) architecture has gained tremendous popularity over the last decade, resulting in an exponential increase in the connected devices and the data processed in the IoT networks. Since IoT devices... Read More about MS-ADS: multistage spectrogram image-based anomaly detection system for IoT security..

Blockchain-based multifactor authentication for future 6G cellular networks: a systematic review. (2022)
Journal Article
ASIM, J., KHAN, A.S., SAQIB, R.M., ABDULLAH, J., AHMAD, Z., HONEY, S., AFZAL, S., ALQAHTANI, M.S. and ABBAS, M. 2022. Blockchain-based multifactor authentication for future 6G cellular networks: a systematic review. Applied sciences [online], 12(7), article number 3551. Available from: https://doi.org/10.3390/app12073551

There are continued advances in the internet and communication fields regarding the deployment of 5G-based applications. It is expected that by 2030, 6G applications will emerge as a continued evolution of the mobile network. Blockchain technology is... Read More about Blockchain-based multifactor authentication for future 6G cellular networks: a systematic review..

Lightweight multifactor authentication scheme for NextGen cellular networks. (2022)
Journal Article
KHAN, A.S., JAVED, Y., SAQIB, R.M., AHMAD, Z., ABDULLAH, J., ZEN, K., ABBASI, I.A. and KHAN, N.A. 2022. Lightweight multifactor authentication scheme for NextGen cellular networks. IEEE access [online], 10, pages 31273-31288. Available from: https://doi.org/10.1109/access.2022.3159686

With increased interest in 6G (6th Generation) cellular networks that can support intelligently small-cell communication will result in effective device-to-device (D2D) communication. High throughput requirement in 5G/6G cellular technology requires... Read More about Lightweight multifactor authentication scheme for NextGen cellular networks..

Anomaly detection using deep neural network for IoT architecture. (2021)
Journal Article
AHMAD, Z., KHAN, A.S., NISAR, K., HAIDER, I., HASSAN, R., HAQUE, M.R., TARMIZI, S. and RODRIGUES, J.J.P.C. 2021. Anomaly detection using deep neural network for IoT architecture. Applied sciences [online], 11(15), article number 7050. Available from: https://doi.org/10.3390/app11157050

The revolutionary idea of the internet of things (IoT) architecture has gained enormous popularity over the last decade, resulting in an exponential growth in the IoT networks, connected devices, and the data processed therein. Since IoT devices gene... Read More about Anomaly detection using deep neural network for IoT architecture..

A spectrogram image-based network anomaly detection system using deep convolutional neural network. (2021)
Journal Article
KHAN, A.S., AHMAD, Z., ABDULLAH, J., AHMAD, F. 2021. A spectrogram image-based network anomaly detection system using deep convolutional neural network. IEEE access [online], 9, pages 87079-87093. Available from: https://doi.org/10.1109/ACCESS.2021.3088149

The dynamics of computer networks have changed rapidly over the past few years due to a tremendous increase in the volume of the connected devices and the corresponding applications. This growth in the network's size and our dependence on it for all... Read More about A spectrogram image-based network anomaly detection system using deep convolutional neural network..