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All Journal International Journal of Advances in Applied Sciences Indonesian Journal of Electrical Engineering and Computer Science
Prathima Mabel John
Dayananda Sagar College of Engineering
Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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An approach for slow distributed denial of service attack detection and alleviation in software defined networks Prathima Mabel John; Rama Mohan Babu Kasturi Nagappasetty
Indonesian Journal of Electrical Engineering and Computer Science Vol 25, No 1: January 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v25.i1.pp404-413

Abstract

Over the last few years, the need for programmable networks has captured the interest of industrialists and academicians. It has led to the development of a paradigm called software defined network (SDN). It separates the network intelligence into the control plane and forwarding logic into the data plane. This architecture gives scope to various security issues of which denial of service (DoS) is the most common and challenging to detect. This paper focuses on the detection and mitigation of a slow DoS attack called Slowloris on Apache2 server in SDN based networks. The proposed solution is called Slowloris detection and mitigation mechanism (SDMM). Mininet, an emulator, and SimpleHTTPServer are used for simulation and the same is implemented using Zodiac FX OpenFlow switch, Ryu controller and Apache2 server. SDMM algorithm detects and mitigates prolonged Slowloris attack in typical networks as well as in slow networks with low bandwidth and high delay in 240-280s with an accuracy of 100% and 98% respectively. It uses expectation of burst size as a key factor for detection.
Hybrid deep learning and ensemble learning approach for high accuracy thyroid disease classification Shuriya Balusamy; Balajishanmugam Vivekanadhan; Prathima Mabel John; Sushma Sunil Bhosle
International Journal of Advances in Applied Sciences Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i1.pp303-312

Abstract

Thyroid disease is a common endocrine disorder affecting the thyroid gland, a small butterfly-shaped organ at the base of the neck. According to the World Health Organization (WHO), nearly one billion people worldwide are affected by thyroid-related conditions. Conventional diagnostic methods, such as thyroid scans and function tests, are often costly, time-consuming, and complex for clinicians to interpret. To overcome these limitations, this study introduces a novel temporal conditional-Markov random field (TC MRF) framework for early detection and classification of thyroid disease. The multi-modality images computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound (US) are collected from the ImageNet database and preprocessed using contrast stretching adaptive Gaussian star (CSAGS) filter to improve image clarity. The enhanced images are then processed over a convolutional neural network (CNN) for feature extraction. These features are classified using a random forest (RF) model to determine whether the thyroid condition is normal or abnormal. The proposed TC MRF achieves a classification accuracy of 98.27% and F1-score of 96.05%. The TC-MRF enhances the total accuracy range of 6.30%, 4.11%, and 5.36% better than naive Bayes, multilayer perceptron (MLP), and decision tree, respectively.
Co-Authors Balajishanmugam Vivekanadhan Rama Mohan Babu Kasturi Nagappasetty Shuriya Balusamy Sushma Sunil Bhosle