Security / Cybersecurity

Cybersecurity is the practice of protecting digital systems, networks, and data from unauthorized access, disruption, and cyberattacks. In today’s connected world, it is essential for safeguarding privacy, ensuring business continuity, and maintaining public trust.

At the Academy of Technology (AOT), cybersecurity research focuses on designing advanced cryptographic systems, secure network protocols, intrusion detection methods, and threat intelligence solutions. Faculty and students develop strategies to defend against threats such as malware, phishing, ransomware, and distributed denial-of-service (DDoS) attacks. Research emphasizes creating security mechanisms that are efficient, scalable, and adaptable to evolving attack techniques. Special attention is given to securing cloud computing, Internet of Things (IoT), and distributed systems. This includes work on secure authentication, end-to-end encryption, and data privacy in resource-constrained environments. Machine learning models are applied to detect anomalies in network traffic, enabling early detection and prevention of potential breaches.

AOT’s well-equipped networking and security laboratories aim to support research in penetration testing, digital forensics, and secure software development. Future research directions include quantum-safe cryptography, blockchain-based security frameworks, and AI-powered cyber defense systems.

Researchers:

Mr. Subhashis Das (CSE Department)

Projects:

Attack Generation for Smart Grid Network Using GAN Simulator

Problem Description: Smart grids are increasingly vulnerable to cyberattacks due to their interconnected nature. Existing intrusion detection datasets are limited and outdated, hindering effective threat detection. Incorporating Generative Adversarial Networks (GANs) allows the generation of diverse, realistic attack scenarios, enabling robust IDS models to better detect evolving threats and enhance smart grid cybersecurity.

Focus of Research:

Utilizes GANs to generate realistic and diverse cyberattacks, creating synthetic datasets for training robust anomaly detection systems in smart grids.
Evaluates how effectively GAN-generated attacks can bypass detection, aiming to enhance the resilience of cybersecurity models.

Publications:

S. Das, A. Pal and S. Dasgupta, Attack Generation for Smart Grid Network Using GAN Simulator, 2024 4th International Conference on Computer, Communication, Control & Information Technology (C3IT), Hooghly, India, 2024, pp. 1-7. https://10.1109/C3IT60531.2024.10829480.