arXiv subjects

Venugopal K R

Publications and source records attributed to Venugopal K R.

6 recordsLinked to original sources

arXiv2026

DCBA: Detection of Collaborative Black-Hole Attacks in Connected Dominated Set using Baiting Process

Mobile Ad-hoc Network (MANET) is temporary and dynamic network topology, wherein nodes are mobile in nature and distributed randomly in a network area. In MANET, nodes cooperate with each other to operate and forward data through multihop communication between source and destination. MANET is exposed to different types of attacks due to absence of central administration. However, some nodes decline to cooperate, misbehaves and appears to be malicious affecting network functionality and connectivity. Providing security and identifying malicious node has become one of the challenging research topics in MANET. Black-hole attack is considered to be most popular attack that degrades the overall network performance. Black-hole node falsely advertises the shortest path to destination intentionally to disrupt the network communication resulting in packet drop. In collaborative black-hole attacks, multiple black-hole nodes cooperate and launch attacks in order to degrade network reliability. In this article we propose a Lightweight technique to detect and isolate Collaborative Black-Hole attacks (LW-CBH) by enhancing existing AODV routing protocol. In this scheme a timer based baiting process and reverse tracing setup is used to detect malicious node through control status message in MAC layer which are Reply Sequence (R-SEQ) and Code Sequence (C-SEQ) message of connected dominated set of nodes. However existing AODV routing protocol fails to detect malicious node during dynamic topology changing in MANET. Simulation of proposed technique is performed using discrete event simulator tool NS-2.35. The simulation results are evaluated for throughput, packet delivery ratio, average end-to-end delay and normalized routing overhead.

cs.CR↗

arXiv2023

A Novel SLCA-UNet Architecture for Automatic MRI Brain Tumor Segmentation

Brain tumor is deliberated as one of the severe health complications which lead to decrease in life expectancy of the individuals and is also considered as a prominent cause of mortality worldwide. Therefore, timely detection and prediction of brain tumors can be helpful to prevent death rates due to brain tumors. Biomedical image analysis is a widely known solution to diagnose brain tumor. Although MRI is the current standard method for imaging tumors, its clinical usefulness is constrained by the requirement of manual segmentation which is time-consuming. Deep learning-based approaches have emerged as a promising solution to develop automated biomedical image exploration tools and the UNet architecture is commonly used for segmentation. However, the traditional UNet has limitations in terms of complexity, training, accuracy, and contextual information processing. As a result, the modified UNet architecture, which incorporates residual dense blocks, layered attention, and channel attention modules, in addition to stacked convolution, can effectively capture both coarse and fine feature information. The proposed SLCA UNet approach achieves good performance on the freely accessible Brain Tumor Segmentation (BraTS) dataset, with an average performance of 0.845, 0.845, 0.999, and 8.1 in terms of Dice, Sensitivity, Specificity, and Hausdorff95 for BraTS 2020 dataset, respectively.

eess.IV↗

arXiv2014

FPGA Based Efficient Multiplier for Image Processing Applications Using Recursive Error Free Mitchell Log Multiplier and KOM Architecture

The Digital Image processing applications like medical imaging, satellite imaging, Biometric trait images etc., rely on multipliers to improve the quality of image. However, existing multiplication techniques introduce errors in the output with consumption of more time, hence error free high speed multipliers has to be designed. In this paper we propose FPGA based Recursive Error Free Mitchell Log Multiplier (REFMLM) for image Filters. The 2x2 error free Mitchell log multiplier is designed with zero error by introducing error correction term is used in higher order Karastuba-Ofman Multiplier (KOM) Architectures. The higher order KOM multipliers is decomposed into number of lower order multipliers using radix 2 till basic multiplier block of order 2x2 which is designed by error free Mitchell log multiplier. The 8x8 REFMLM is tested for Gaussian filter to remove noise in fingerprint image. The Multiplier is synthesized using Spartan 3 FPGA family device XC3S1500-5fg320. It is observed that the performance parameters such as area utilization, speed, error and PSNR are better in the case of proposed architecture compared to existing architectures

cs.AR↗

arXiv2014

QoS group based optimal retransmission medium access protocol for wireless sensor networks

This paper presents, a Group Based Optimal Retransmission Medium Access (GORMA) Protocol is designed that combines protocol of Collision Avoidance (CA) and energy management for low-cost, short-range, low-data rate and low-energy sensor nodes applications in environment monitoring, agriculture, industrial plants etc. In this paper, the GORMA protocol focuses on efficient MAC protocol to provide autonomous Quality of Service (QoS) to the sensor nodes in one-hop QoS retransmission group and two QoS groups in WSNs where the source nodes do not have receiver circuits. Hence, they can only transmit data to a sink node, but cannot receive acknowledgement control signals from the sink node. The proposed protocol GORMA provides QoS to the nodes which work independently on predefined time by allowing them to transmit each packet an optimal number of times within a given period. Our simulation results shows that the performance of GORMA protocol, which maximize the delivery probability of one-hop QoS group and two QoS groups and minimize the energy consumption.

cs.NI↗

arXiv2013

Multiple Domain Secure Routing for Wireless Sensor Networks

Secure Transmission of data packets in Wireless Sensor Networks is an important area of Research. There is a possibility of an attacker creating security holes in the network. Hence, network security and reliability can be achieved by discovering random multiple paths using multiple domains, and forwarding data packets from the source node to the destination node. We have designed, Multiple Domain Routing with Overlap of Nodes (MDRON) and Multiple Domain Routing Without Overlap of Nodes (MDRWON) algorithms, in which packets follow multiple optimized paths simultaneously. The Special node algorithm searches the node which has maximum power and these nodes are used for transferring the packet from one domain to another domain. Simulation results using MATLAB shows that performance is better than Purely Random Propagation (PRP) and Non Repetitive Random Propagation(NRRP) Algorithms.

cs.CR↗

arXiv2013

Similarity based Dynamic Web Data Extraction and Integration System from Search Engine Result Pages for Web Content Mining

There is an explosive growth of information in the World Wide Web thus posing a challenge to Web users to extract essential knowledge from the Web. Search engines help us to narrow down the search in the form of Search Engine Result Pages (SERP). Web Content Mining is one of the techniques that help users to extract useful information from these SERPs. In this paper, we propose two similarity based mechanisms; WDES, to extract desired SERPs and store them in the local depository for offline browsing and WDICS, to integrate the requested contents and enable the user to perform the intended analysis and extract the desired information. Our experimental results show that WDES and WDICS outperform DEPTA [1] in terms of Precision and Recall.

cs.IR↗