Prashant Pranav

Jul 26, 2024 | nature.com | Abhinav Sinha |Sandip Dutta |Prashant Pranav |Deepshikha Kumari

The constantly changing nature of cyber threats presents unprecedented difficulties for people, institutions, and governments across the globe. Cyber threats are a major concern in today’s digital world like hacking, phishing, malware, and data breaches. These can compromise anyone’s personal information and harm the organizations. An intrusion detection system plays a vital responsibility to identifying abnormal network traffic and alerts the system in real time if any malicious activity is detected. In our present research work Artificial Neural Networks (ANN) layers are optimized with the execution of Spider Monkey Optimization (SMO) to detect attacks or intrusions in the system. The developed model SMO-ANN is examined using publicly available dataset Luflow, CIC-IDS 2017, UNR-IDD and NSL -KDD to classify the network traffic as benign or attack type. In the binary Luflow dataset and the multiclass NSL-KDD dataset, the proposed model SMO-ANN has the maximum accuracy, at 100% and 99%, respectively.

Jul 10, 2024 | onlinelibrary.wiley.com | Purushottam Singh |Mesra Ranchi India |Prashant Pranav |Shamama Anwar

CONFLICT OF INTEREST STATEMENT The authors declare no conflicts of interest. REFERENCES 1 Opoku, Samuel King. A robust cryptographic system using neighborhood-generated keys. arXiv preprint arXiv:1209.2738. 2012. 2. Cyber security, cyber threats, implications and future perspectives: a review. Authorea Preprints. 2022. 3, . Resource-efficient common randomness and secret-key schemes. Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete Algorithms.

Apr 16, 2024 | onlinelibrary.wiley.com | Ankita Kumari |Purushottam Singh |Prashant Pranav |Mesra Ranchi India

CONFLICT OF INTEREST STATEMENT The authors declare that they do not have any conflict of interest. REFERENCES 1, . Conservation of energy in wireless sensor network by preventing denial of sleep attack. Procedia Comput. Sci. 2015; 45: 370-379. 2, , , , , . Trust-based attack and defense in wireless sensor networks: a survey. Wirel. Commun. Mob. Comput. 2020;(2020): 1-20. 3, , , , , . The sleep deprivation attack in sensor networks: analysis and methods of defense. Int. J. Distrib. Sens. Netw.

Mar 12, 2024 | mdpi.com | Purushottam Singh |Sandip Dutta |Prashant Pranav

1. IntroductionGenerative Adversarial Networks (GANs) have become the avant-garde of deep learning, pushing the frontiers of what machines can imagine. At its core, a GAN is a contest of wits between two neural networks—the generator, which strives to produce realistic data, and the discriminator, which endeavors to distinguish between genuine data and the fabrications of the generator.