C. N. S. Vinoth Kumar

Virtual Machines (VMs) serve as dynamic execution environments that trade-off workload isolation, performance, and elastic scalability in the cloud. However, the flexibility of VMs which allows for efficiency also makes them susceptible to stealthy and adaptive cyber threats such as resource exhaustion, privilege escalation, and lateral movement. In such environments, the traditional signature- and heuristic-based defenses often encounter difficulties, resulting in high false-positive rates and low-rank under changing attack conditions. To mitigate these limitations, we present a flexible defense system which combines feature extraction, anomaly detection, classification and mitigation in a single pipeline. The system consists of an Adaptive Feature Encoder for concise behavior representation, a Density-Aware Clustering for anomaly detection, a Transformer–Boosting Classifier for timely threat identification, and a Dynamic Mitigation Controller for prompt decision making at runtime, and with low overhead. Experiments on benchmark VM telemetry datasets (ToN-IoT and CSE-CIC-IDS2018) indicate that VMShield provides 99.8% accuracy, 99.7% precision, 99.6% F1-score, and reduces false positives by 35% compared to state-of-the-art baselines. Stress testing ensures scalability, keeping detection latency at ~ 240 ms and overhead under 7%. By integrating the accuracy with operational resilience, proposed adaptive and scalable protection framework offers a practical defense to protect the cloud-hosted VMs from the emerging adversarial threats.

Neuromarketing is an emerging field that combines neuroscience with marketing to gain insights into unconscious consumer behaviour. Traditional methods like surveys often fail to capture real-time emotional and cognitive responses. To address this gap, this study employs EEG signal analysis to predict favourable and unfavourable consumer reactions to advertisements and products. The theoretical foundation is based on the dual-process theory, which distinguishes between fast, emotional decision-making (System 1), and slow, rational thinking (System 2). EEG markers such as alpha, beta, and theta bands are used to assess attention, engagement, and decision conflict. EEG data was collected using a single-channel Neurosky Mindwave headset from 14 participants aged 18–22. A total of 80 ads were shown, categorised by product and design type. Subject-dependent and subject-independent analyses were conducted. In the SD study, Naïve Bayes and SVM classifiers achieved a maximum accuracy of 0.62. In the SI analysis, SVM showed strong performance across product and gender-based classification. A deep learning model also produced comparable accuracy. These findings demonstrate the potential of EEG-based neuromarketing to provide deeper insights into consumer behaviour, with possible implications for both commercial and clinical applications.

Cyberthreat detection and classification are crucial fields aiming to develop intelligent systems capable of real-time identification and categorization of various cyberthreats such as malware, phishing, social engineering, and ransomware. Detection...

The advanced persistent threat (APT) attack is the most frequent and seriously damaging one that can be launched against the target system. This type of malicious activity is deliberate and focused, with specific objectives in mind. The rise of this attack technique poses a significant challenge to the information security systems of various businesses, governments, and organizations. The techniques of utilizing deep learning (DL) or machine learning (ML) methods to evaluate abnormal behaviours and network traffic signs for preventing and detecting APT attacks are becoming popular in recent times. Though, the APT attack detection technique utilizes behaviour evaluation and analysis methods for confronting various problems owing to the absence of distinctive data of attack drives. So, the experimental outcomes display better recognition, it does not bring higher effectiveness in practice. Therefore, this study develops a novel Defence against Advanced Persistent Threat Attack Detection Feature Selection with Deep Learning (DAPTAD-FSDL) Method. The key objective of the DAPTAD-FSDL approach lies in the robust defence against the detection of APT attacks. In the presented DAPTAD-FSDL model, Z-score normalization is performed to change the raw data into a compatible format. Then, the presented DAPTAD-FSDL technique accomplishes the feature selection process using the grey wolf optimizer (GWO) algorithm. Besides, the attention with CNN-BiLSTM model is employed for the defence against the detection of APT attacks. Finally, a multi-objective improved golden jackal optimization algorithm (MIGJOA) is exploited for the optimal hyperparameter adjustment of the A-CNN-BiLSTM model. The experimental analysis of the DAPTAD-FSDL algorithm is tested employing the APT NSL-KDD dataset. The simulation outcomes described the supremacy of the DAPTAD-FSDL technique under different measures.