Sheroz Khan

This paper suggests a novel one-dimensional (1D) map to address the limitations of traditional chaotic 1D maps. In contrast to traditional 1D maps, the proposed map has three control parameters a, μ, and c, allowing it to exhibit chaotic behavior over a wide range of values. The dynamic behavior of the new 1D map was analyzed using well-known numerical methods, including the bifurcation diagram and Lyapunov exponent. Both tests showed their complex and diverse behavior. In addition, a novel image encryption scheme was devised using the new function as its pseudorandom number generator. Rigorous statistical testing was applied to the proposed encryption algorithm. The mean square error (MSE) and peak signal-to-noise ratio (PSNR) results, in addition to subjecting 28 images to number of pixels change rate (NPCR) and unified average changing intensity (UACI) tests demonstrated the robustness of the system. The results of this study demonstrate the effectiveness of the new 1D map for use in secure image cryptography applications, providing a more robust and secure alternative to traditional chaotic 1D maps.

In fifth Generation (5G) networks, protection from internal attacks, external breaches, violation of confidentiality, and misuse of network vulnerabilities is a challenging task. Various approaches, especially deep-learning (DL) prototypes, have been adopted in order to counter such challenges. For 5G network defense, DL module are recommended here in order to symptomize suspicious NetFlow data. This module behaves as a virtual network function (VNF) and is placed along a 5G network. The DL module as a cyber threat-symptomizing (CTS) unit acts as a virtual security scanner along the 5G network data analytic function (NWDAF) to monitor the network data. When the data were found to be suspicious, causing network bottlenecks and let-downs of end-user services, they were labeled as “Anomalous”. For the best proactive and adaptive cyber defense system (PACDS), a logically organized modular approach has been followed to design the DL security module. In the application context, improvements have been made to input features dimension and computational complexity reduction with better response times and accuracy in outlier detection. Moreover, key performance indicators (KPIs) have been proposed for security module placement to secure interslice and intraslice communication channels from any internal or external attacks, also suggesting an adaptive defense mechanism and indicating its placement on a 5G network. Among the chosen DL models, the CNN model behaves as a stable model during behavior analysis in the results. The model classifies botnet-labeled data with 99.74% accuracy and higher precision.