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All Journal International Journal of Electrical and Computer Engineering International Journal of Applied Sciences and Smart Technologies
K. Srinivas
VR Siddhartha Engineering College
Computer Science & IT Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering

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A comprehensive verification of the header format and bandwidth utilization to detect distributed denial of service attack in vehicular ad hoc network Kaurav, Arun Singh; Srinivas, K.
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6538-6550

Abstract

Vehicular ad hoc network (VANET) is a promising technology for controlling traffic on roads. Nowadays, heavy traffic is a major issue, and the presence of attackers exacerbates the situation. The most important challenge in VANET is its security from malicious vehicles. In order to defend against distributed denial of service DDoS attacks, we propose a comprehensive verification header format bandwidth detection (CVHB) in VANET. The behavior of a DDoS attack is unknown for all the other normal nodes in network. The header format of packer contains all the information of nodes that are actively participating in routing. The attacker infection probability measured by ???????? and ???????? or (???????? > 0.9). If both the parameters are high means attacker presence confirm in network. The CVHB scheme checks the packet header format of the attacker node, and only the attacker is one of the nodes whose sequence number is frequently changing. So, CVHB blocks the flooding of unwanted packets that consume the limited bandwidth of a wireless link and identify packets that contain no useful information. To measure the performance of the network, the basic performance metrics that are used are dropping percentage, packet delivery ratio (PDR), throughput and delay. The result of CVHB is showing improvement as compared to multilayer distributed self-organizing maps (MSOM) in VANET.
Automated Detection of Spine Deformities: Advancing Orthopedic Care with Convolutional Neural Networks Pratap, Deepesh; Sinha, Saran; Kumari, A. Charan; Srinivas, K.
International Journal of Applied Sciences and Smart Technologies Volume 06, Issue 2, December 2024
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24071/ijasst.v6i2.9280

Abstract

This paper proposes Spine-CNN, a deep learning model for the detection of spinal deformities that can assist orthopedic doctors as a reliable tool for diagnosis. This technology promises to dramatically simplify the diagnostic process, freeing valuable time, and resources for healthcare professionals. To achieve this objective, a dataset of spine deformity X-ray images was curated from the PhysioNet database. The Spine-CNN was specially designed for detecting the spine deformity by incorporating features to leverage its ability to extract intricate features from radiographic images and by fine tuning the hyperparameters to properly train the model. Model performance was evaluated using standard metrics. Results from the Spine-CNN demonstrated promising performance in detecting spinal deformities. The model achieved an accuracy of 74%, with precision, recall, and F1-score values of 77%, 70%, and 73% respectively. Specifically, this research work introduces a Spine-CNN that underscore the potential of deep learning techniques to revolutionize diagnostic practices in orthopedic medicine, leading to improved treatment outcomes and patient care. Keywords: Computer-aided detection, Convolutional neural network, Image classification, Spine Deformation, X-ray imaging
Co-Authors Kaurav, Arun Singh Kumari, A. Charan Pratap, Deepesh Sinha, Saran