Secure cloud computing: leveraging GNN and leader K-means for intrusion detection optimization.

Over the past two decades, cloud computing has experienced exponential growth, becoming a critical resource for organizations and individuals alike. However, this rapid adoption has introduced significant security challenges, particularly in intrusion detection, where traditional systems often struggle with low detection accuracy and high processing times. To address these limitations, this research proposes an optimized Intrusion Detection System (IDS) that leverages Graph Neural Networks and the Leader K-means clustering algorithm.

EDSSR: a secure and power-aware opportunistic routing scheme for WSNs.

Motivated by the pivotal role of routing in Wireless Sensor Networks (WSNs) and the prevalent security vulnerabilities arising from existing protocols, this research tackles the inherent challenges of securing WSNs. Many current WSN routing protocols prioritize computational efficiency but lack robust security measures, making them susceptible to exploitation by malicious actors. The prevalence of reactive protocols, chosen for their lower bandwidth consumption, exacerbates security concerns, as proactive alternatives require more resources for maintaining network routes.

Implementation of a novel secured authentication protocol for cyber security applications.

Robust verification protocols are crucial for maintaining the security and reliability of sensitive information due to the increasing complexity of cyber-attacks. This paper introduces a novel 5G Secure Handover Protocol aimed at addressing security and effectiveness issues encountered in existing systems. The proposed protocol is robust against various attacks, including de-synchronization, replay, man-in-the-middle (MITM), denial of services (DoS), and jamming, ensures perfect forward key secrecy, safeguarding communication confidentiality.

Enhanced Prairie Dog Optimization with Differential Evolution for solving engineering design problems and network intrusion detection system.

This paper introduces a novel hybrid optimization algorithm, PDO-DE, which integrates the Prairie Dog Optimization (PDO) algorithm with the Differential Evolution (DE) strategy. This research aims to develop an algorithm that efficiently addresses complex optimization problems in engineering design and network intrusion detection systems. Our method enhances the PDO's search capabilities by incorporating the DE's principal mechanisms of mutation and crossover, facilitating improved solution exploration and exploitation.

An efficient brain tumor detection and classification using pre-trained convolutional neural network models.

In cases of brain tumors, some brain cells experience abnormal and rapid growth, leading to the development of tumors. Brain tumors represent a significant source of illness affecting the brain. Magnetic Resonance Imaging (MRI) stands as a well-established and coherent diagnostic method for brain cancer detection.

A deep facial recognition system using computational intelligent algorithms.

The development of biometric applications, such as facial recognition (FR), has recently become important in smart cities. Many scientists and engineers around the world have focused on establishing increasingly robust and accurate algorithms and methods for these types of systems and their applications in everyday life. FR is developing technology with multiple real-time applications.