Cyber threats are increasingly frequent in today’s world, posing challenges for organizations and individuals to protect their data from cybercriminals. On the other hand, Generative Artificial Intelligence (GAI) technology offers an efficient way to automatically address these issues with the help of AI models and algorithms. It can work on more critical security aspects where human intervention is required and handle everyday threat situations autonomously. This research paper explores GAI in enhancing cybersecurity by leveraging AI Models and algorithms. GAI can autonomously address common security issues, detect novel threats, and augment human intervention in critical security aspects. Moreover, this research study also highlights autonomous security enhancements, improved security posture against emerging threats, anomaly detection, and threat response. Besides this, we have discussed the GAI limitations, such as occasional incorrect results, expensive training, and the potential for misuse by malicious actors for illegal activities. This research study also provides valuable insights into the balanced adoption of GAI in cybersecurity, ensuring effective threat migration without compromising system integrity.

Generative Artificial Intelligence (Gen-AI) is a new advancement that has revolutionized the concepts of Natural Language Processing (NLP) and Large Language Model (LLM). This change impacts various aspects of life, stimulating industry, education, and healthcare progression. This survey presents the potential applications of Gen-AI across various sectors, highlighting the risks and opportunities. Some of the most pressing challenges include ethical consideration, the rise of disinformation (including deepfakes), concerns over Intellectual Property (IP) rights, cybersecurity risks, bias and discrimination. The survey also covers the fundamental models of Gen-AI, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and transformers. These frameworks are extremely important in various sectors, including medical imaging, drug discovery, and personalized medicine, and offer valuable insights into the future of technological advancements in the scientific community. The study contributes substantially by exploring positive elements and addressing the challenges of adequately deploying Gen-AI models. Using these insights, we hope to provide a comprehensive knowledge of the potential challenges and complexities associated with the widespread implementation of artificial intelligence technologies.

The rapid growth of Internet of Things (IoT) deployments has intensified the need for real-time data analytics using lightweight machine learning models that can operate under strict energy, latency, and reliability constraints. While existing studies largely focus on model compression and inference optimization, they often overlook the impact of unreliable wireless communication on learning performance. This paper presents a comprehensive cross-layer framework that integrates lightweight ensemble learning with PHY–MAC layer-aware IoT network simulation to evaluate the end-to-end performance of real-time data analytics in resource-constrained environments. We investigate three data handling strategies–Baseline transmission, Duty-Cycle-aware reporting, and a proposed Rolling Window aggregation method–combined with multiple MAC protocols (TDMA, CSMA, and DutyCycleMAC) and PHY channel models (Ideal, AWGN, and Rayleigh). Extensive MATLAB-based simulations quantify key Quality-of-Service (QoS) metrics, including packet delivery ratio (PDR), latency, jitter, throughput, and energy consumption, alongside machine learning metrics such as accuracy, F1-score, and inference time. Results show that TDMA consistently achieves the highest PDR (up to 1.0 under Ideal PHY), while the proposed Rolling Window variant improves robustness and energy efficiency without sacrificing predictive accuracy. The findings demonstrate that communication-aware data aggregation is critical for deploying reliable and energy-efficient lightweight AI in real-world IoT systems.

The dynamic and constantly evolving landscape of cyber threats demands innovative methods capable of adapting to the complex relationships and structures inherent in network data. Traditional methods often struggle to adequately capture the intricacies of dynamic networks, especially in terms of evolving temporal dynamics and multiscale dependencies. The proposed solution, Enhanced V-GCN, combines the structural insights of Graph Convolutional Networks (GCNs) with the temporal modeling capabilities of Variational Autoencoders (VAEs), further augmented by multiscale convolutions and attention mechanisms. Multiscale convolutions enable the model to aggregate information across broader neighborhood ranges, while attention mechanisms prioritize the most critical nodes and edges, dynamically adapting to changes within the network. This enhanced approach allows V-GCN to effectively capture both nodal and structural patterns, significantly improving performance in node classification tasks. The Enhanced V-GCN model has demonstrated superior performance in node classification, outperforming baseline models with an accuracy of 98.00%, precision of 97.93%, recall of 98%, and an F1-score of 97.92%, indicating robust classification capabilities and exceptional generalization across diverse network structures.

An Aggregation-Aware Energy-Efficient Bit-Mapping Medium Access Control Protocol (AABMP) is proposed for efficient data transmission in Wireless Sensor Networks and the Internet of Things. The main objective of the proposed method is to effectively manage the data transmission from sensor nodes to cluster heads in an energy-efficient manner. AABMP aggregates the data by estimating the mean value of a sliding window of the previous few samples and calculates the deviation of the current reading with respect to mean value. The deviation aware method is proposed for the transmission decision from sensor node to cluster head node. The deviation is predicted for real-time dataset using various machine learning methods. Based on performance evaluations, the most effective ML method is integrated with the bit-mapping MAC protocol in an energy-efficient manner. The aggregation-aware ML approach reduces the number of transmitted packets by efficiently identifying redundant data. The proposed approach is evaluated using the Intel LAB dataset (https://db.csail.mit.edu/labdata/labdata.html) of 55 real sensor nodes. The proposed method demonstrates practical applicability for energy-efficient data transmission in IoT-oriented WSN deployments. For the efficient analysis, the precise number of transmitted packet is estimated based on transmission probability. The algorithm is proposed for the estimation of transmission probability. For performance analysis, we have compared multiple ML methods to determine the most suitable one. This optimal method is then integrated with the bit-mapping-based energy-efficient piggybacking scheme. AABMP is also compared with existing MAC protocols. The results demonstrate its superiority in terms of energy savings across both worst-case and best-case scenarios.