Article Per Year (5 Year)
p-Index From 2021 - 2026
0.562
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Parvathy, Palaniraj Rajidurai
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Random forest algorithm with hill climbing algorithm to improve intrusion detection at endpoint and network Sekar, Satheesh Kumar; Parvathy, Palaniraj Rajidurai; Pinjarkar, Latika; Latha, Raman; Sathish, Mani; Reddy, Munnangi Koti; Murugan, Subbiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp134-142

Abstract

Cloud computing is a framework that enables end users to connect highly effective services and applications over the internet effortlessly. In the world of cloud computing, it is a critical problem to deliver services that are both safe and dependable. The best way to lessen the damage caused by entry into this environment is one of the primary security concerns. The fundamental advantage of a cooperative approach to intrusion detection system (IDS) is a superior vision of an action of network attack. This paper proposes a random forest (RF) algorithm with a hill-climbing algorithm (RFHC) to improve intrusion detection at the endpoint and network. Initially, it is used for feature selection, and the next process is to separate the intrusions detection. The feature selection is maintained by the hill climbing (HC) algorithm that chooses the best features. Then, we utilize the RF algorithm to separate the intrusion efficiently. The experimental results depict that the RFHC mechanism reached more acceptable results regarding recall, precision, and accuracy than a baseline mechanism. Moreover, it minimizes the miss detection ratio and enhances the intrusion detection ratio.
Intrusion detection and prevention using Bayesian decision with fuzzy logic system Sekar, Satheeshkumar; Parvathy, Palaniraj Rajidurai; Gupta, Gopal Kumar; Rajagopalan, Thiruvenkadachari; Basavaraddi, Chethan Chandra Subhash Chandra Basappa; Padmanaban, Kuppan; Murugan, Subbiah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp1200-1208

Abstract

Nowadays, intrusion detection and prevention method has comprehended the notice to decrease the effect of intruders. denial of service (DoS) is an attack that formulates malicious traffic is distributed into an exacting network device. These attackers absorb with a valid network device, the valid device will be compromised to insert malicious traffic. To solve these problems, the Bayesian decision model with a fuzzy logic system based on intrusion detection and prevention (BDFL) is introduced. This mechanism separates the DoS packets based on the type of validation, such as packet and flow validation. The BDFL mechanism uses a fuzzy logic system (FLS) for validating the data packets. Also, the key features of the algorithm are excerpted from data packets and categorized into normal, doubtful, and malicious. Furthermore, the Bayesian decision (BD) decide two queues as malicious and normal. The BDFL mechanism is experimental in a network simulator environment, and the operations are measures regarding DoS attacker detection ratio, delay, traffic load, and throughput.
Intrusion detection in clustering wireless network by applying extreme learning machine with deep neural network algorithm Parvathy, Palaniraj Rajidurai; Sekar, Satheeshkumar; Tidke, Bharat; Jyothi, Rudraraju Leela; Sujatha, Venugopal; Shanmugathai, Madappa; Murugan, Subbiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp887-896

Abstract

Nowadays, intrusion detection systems (IDSs) have growingly come to be considered as an important method owing to their possible to expand into a key factor, which is crucial for the security of wireless networks. In wireless network, when there is a thousand times more traffic, the effectiveness of normal IDS to identify hostile network intrusions is decreased by an average factor. This is because of the exponential growth in network traffic. This is due to the decreased number of possibilities to discover the intrusions. This is because there are fewer opportunities to see possible risks. We intend an extreme learning machine with deep neural network (DNN) algorithm-based intrusion detection in clustering (EIDC) wireless network. The main objective of this article is to detect the intrusion efficiently and minimize the false alarm rate. This mechanism utilizes the extreme learning machine (ELM) with a deep neural network algorithm for optimizing the weights of input and hidden node biases to deduce the network output weights. Simulation outcomes illustrate that the EIDC mechanism not only assures a better accuracy for detection, considerably minimizes an intrusion detection time, and shortens the false alarm rate.
Co-Authors Basavaraddi, Chethan Chandra Subhash Chandra Basappa Gupta, Gopal Kumar Jyothi, Rudraraju Leela Latha, Raman Murugan, Subbiah Padmanaban, Kuppan Pinjarkar, Latika Rajagopalan, Thiruvenkadachari Reddy, Munnangi Koti Sathish, Mani Sekar, Satheesh Kumar Sekar, Satheeshkumar Shanmugathai, Madappa Sujatha, Venugopal Tidke, Bharat