P.Chandra Kanth
Verified @gmail.com
Assistant professor
Audisankara College of Engineeering and Technology
EDUCATION
Doctor of Philosophy
RESEARCH INTERESTS
Machine Learning, Deep Learning, Neural Networks
Scopus Publications
Scopus Publications
Varasree B, Kavithamani V, Chandrakanth P, Basi Reddy A, Padmapriya R, and Senthamil Selvan R
Elsevier BV
J.S. Prasath, V. Irine Shyja, P. Chandrakanth, Boddepalli Kiran Kumar, and Adam Raja Basha
IOS Press
Now, the Cyber security is facing unprecedented difficulties as a result of the proliferation of smart devices in the Internet of Things (IoT) environment. The rapid growth in the number of Internet users over the past two decades has increased the need for cyber security. Users have provided new opportunities for attackers to do harm. Limited security budgets leave IoT devices vulnerable and easily hacked to launch distributed denial-of-service (DDoS) attacks, with disastrous results. Unfortunately, due to the unique nature of the Internet of Things environment, most security solutions and intrusion detection systems (IDS) cannot be directly adapted to the IoT with acceptable security performance and are vulnerable to various attacks that do not benefit. In this paper we propose an optimal secure defense mechanism for DDoS in IoT network using feature optimization and intrusion detection system (OSD-IDS). In OSD-IDS mechanism, first we introduce an enhanced ResNet architecture for feature extraction which extracts more deep features from given traffic traces. An improved quantum query optimization (IQQO) algorithm for is used feature selection to selects optimal best among multiple features which reduces the data dimensionality issues. The selected features have given to the detection and classification module to classify the traffic traces are affected by intrusion or not. For this, we design a fast and accurate intrusion detection mechanism, named as hybrid deep learning technique which combines convolutional neural network (CNN) and diagonal XG boosting (CNN-DigXG) for the fast and accurate intrusion detection in IoT network. Finally, we validate the performance of proposed technique by using different benchmark datasets are BoNeSi-SlowHTTPtest and CIC-DDoS2019. The simulation results of proposed IDS mechanism are compared with the existing state-of-art IDS mechanism and analyze the performance with respects to different statistical measures. The results show that the DDoS detection accuracy of proposed OSD-IDS mechanism is high as 99.476% and 99.078% for BoNeSi-SlowHTTPtest, CICDDoS2019, respectively.
N. Subramanyam, C. Vijaya Kumar, A. Rajasekhar Reddy, and P. Chandrakanth
AIP Publishing
U. Thirupalu, A. Rajasekhar Reddy, and P. Chandrakanth
AIP Publishing
Rajaiah M., Vijaya Kumar C., Subramanyam N., and Chandra Kanth P.
AIP Publishing
Sudheer Hanumanthakari, Neha Garg, P Chandrakanth, Navdeep Dhaliwal, R Ramadevi, and Siva Sankara Babu Chinka
IEEE
The rise in the industrialization and digitalization has introduced numerous equipment. However, the equipment must be properly monitored and maintained. Any deviation leads to the occurrence of fault. Fault detection at initial conditions are highly important to maintain the stability and performance efficiency of the system. To overcome the drawbacks of the conventional systems, the proposed fault diagnosis system is implemented. This is done by incorporating artificial intelligence techniques. Here, the deep learning techniques are used for the detection of fault in the rotating machinery. The faults must be predicted earlier to avoid the overall damage in the system by protecting them from both soft and hard faults. Thus, the initial prediction of fault helps to maintain the reliability and stability of the system. In general, the faults are identified through data mining techniques proceeded with feature extraction and image processing techniques. This includes the early prediction by providing the accurate causes for the occurrence of fault in the system.
P. Chandra Kanth and M. S. Anbarasi
Springer Singapore
P. Chandrakanth and M. S. Anbarasi
Springer Singapore
As data evolves into streams, preserving privacy is a challenging task. Defining a classifier and understanding the categories of data on a dynamic data is an algorithmic task. Many algorithms have recommended using ensemble methods. Statistical ensemble methods are used in this paper to confine a considerable revision of the classifier and recommend the reliability of collecting data for preserving privacy. We conducted experiments on synthetic data and real-time data, and drawn algorithms to identify the drifts and recommendations to the classifier. The framework is experimented thoroughly and results are drawn.