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All Journal International Journal of Electrical and Computer Engineering International Journal of Advances in Applied Sciences Bulletin of Electrical Engineering and Informatics
Dash, Ritesh
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Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics

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Journal : International Journal of Electrical and Computer Engineering

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Trust factor validation for distributed denial of service attack detection using machine learning Jayakumar Raghvin, Manju; Bharamagoudra, Manjula R.; Dash, Ritesh
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1455-1462

Abstract

Distributed denial of service (DDoS) attacks, predicted to be 100 Gbps and greater, are expected to begin in the first quarter of 2019, with 77% of all attacks concentrated on at least two vectors. According to a Neustar Research Agency assessment, DDoS attacks are becoming more powerful and common. Among many other issues, distributed denial of service is a notable security issue. A large number of research projects have been conducted to address this issue, but their methodologies are either inaccurate or computationally expensive, making developing an effective DDoS assault detection method a critical demand of current research. A DDoS attack employs a huge number of agents or resources to carry out the attack, resulting in a large-scale attack power. The attackers use their intelligence to discover the weak system, which is then coordinated and managed remotely. The suggested detection framework uses a frequent time interval balancing module with node trust factor validation (FTIBM-NTFV) that is used to identify the DDoS attacks in the system for improving the security levels of the network. The proposed model is compared with the traditional methods and the results are analyzed that represents the proposed model is achieving better outcomes.
Optimized reactive power management system for smart grid architecture Raghvin, Manju Jayakumar; Bharamagoudra, Manjula R.; Dash, Ritesh
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i4.pp3707-3716

Abstract

The Indian power grid is an extensive and mature power system that transfers large amounts of electricity between two regions linked by a power corridor. The increased reliance on decentralized renewable energy sources (RESs), such as solar power, has led to power system instability and voltage variations. Power quality and dependability in a smart grid (SG) setting can be enhanced by the careful tracking and administration of solar energy generated by panels. This study proposes a number of reactive power regulation algorithms that take smart grids into account. When developing a kernel, debugging is a must in optimal reactive power management. In this research, a debugging primitive called physical memory protection (PMP), a security feature, is considered. Debugging in the kernel domain requires specialized tools, in contrast to the user space where we have kernel assistance. This research proposes an optimal reactive power management in smart grid using kernel debugging model (ORPM-SG-KDM) for managing the reactive power efficiently. This research achieved 98.5% accuracy in kernel debugging and 99.2% accuracy in optimal reactive power management. Kernel debugging accuracy is increased by 1.8% and 3% of reactive power management accuracy is increased.
Co-Authors Bharamagoudra, Manjula R. Bharat, Manish Chirantan, Shaswat Jayakumar Raghvin, Manju Jena, Ramakanta Murty, ASR Panda, P.C Raghvin, Manju Jayakumar Swain, Sarat Chandra