Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Distributed-Intrusion Detection System using combination of Ant Colony Optimization (ACO) and support vector machine (SVM)(Institute of Electrical and Electronics Engineers Inc., 2016) Wankhade, A.; Chandrasekaran, K.Intrusion Detection System (IDS) are playing a very substantial role in protecting computer networks. Still conventional IDS finds itself limited when it comes to distribute intrusion detection. An intruder may conceal its origin of attack by moving from node to node in a network. In order to conquer these limitations, alerts are to be exchanged and correlated in distributed intrusion detection system (DIDS) in a cooperative manner. Because of diversity of network behavior and high growth in development of new types of attacks, intrusion detection algorithm based on fast machine learning methods are of great significance to reduce the false alarm rates with high accuracy of detection rate. This work proposes using a DIDS model for data collection across the network and a hybrid method that classifies the network activities collected in the DIDS model as normal and abnormal. This hybrid method is a combination of popular machine learning algorithms Support Vector Machine (SVM) and Ant Colony Optimization (ACO) which is to be used on a model for DIDS. Also it can detect unseen attacks of intrusion with high detection rate with minimal misclassification. Experiments show that usage of hybrid method on the DIDS model is superior to that of SVM alone or ACO alone both in terms of run-Time efficiency and detection rate. © 2016 IEEE.Item Data-Driven Stillbirth Prediction and Analysis of Risk Factors in Pregnancy(Springer Science and Business Media Deutschland GmbH, 2021) Unnikrishnan, A.; Chandrasekaran, K.; Shukla, A.One of the main issues in developing countries is the lack of policies for ensuring good public health conditions in rural areas. Maternal and child health care is one such area that has not improved in developing countries. Although child health has improved noticeably over the years, infant or under-5-mortality has not become any better. There remain major knowledge gaps in our understanding of how factors such as prenatal care, antenatal care, social and economic backgrounds, living conditions and lifestyle of pregnant women and their family members affect the pregnancy outcomes. Understanding such factors that affect the poor pregnancy outcome helps in formulating plans to prevent such issues and to treat them effectively. Determining health policies will be easier from a deeper analysis of such factors involved. This paper discusses some of the key machine learning techniques to predict the pregnancy outcome as a stillbirth or not and analyze some of the factors that majorly cause stillbirth. © 2021, Springer Nature Singapore Pte Ltd.Item Towards a Federated Learning Approach for NLP Applications(Springer Science and Business Media Deutschland GmbH, 2021) Prabhu, O.S.; Gupta, P.K.; Shashank, P.; Chandrasekaran, K.; Divakarla, D.Traditional machine learning involves the collection of training data to a centralized location. This collected data is prone to misuse and data breach. Federated learning is a promising solution for reducing the possibility of misusing sensitive user data in machine learning systems. In recent years, there has been an increase in the adoption of federated learning in healthcare applications. On the other hand, personal data such as text messages and emails also contain highly sensitive data, typically used in natural language processing (NLP) applications. In this paper, we investigate the adoption of federated learning approach in the domain of NLP requiring sensitive data. For this purpose, we have developed a federated learning infrastructure that performs training on remote devices without the need to share data. We demonstrate the usability of this infrastructure for NLP by focusing on sentiment analysis. The results show that the federated learning approach trained a model with comparable test accuracy to the centralized approach. Therefore, federated learning is a viable alternative for developing NLP models to preserve the privacy of data. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Machine Learning Powered Autoscaling for Blockchain-Based Fog Environments(Springer Science and Business Media Deutschland GmbH, 2022) Martin, J.P.; Joseph, C.T.; Chandrasekaran, K.; Kandasamy, A.Internet-of-Things devices generate huge amount of data which further need to be processed. Fog computing provides a decentralized infrastructure for processing these huge volumes of data. Fog computing environments provide low latency and location-aware alternative to conventional cloud computing by placing the processing nodes closer to the end devices. Co-ordination among end devices can become cumbersome and complex with the increasing amount of IoT devices. Some of the major challenges faced while executing services in the fog environment is the resource provisioning for the user services, service placement among the fog devices and scaling of fog devices based on the current load on the network. Being a decentralized infrastructure, fog computing is vulnerable to external threats such as data thefts. This work presents a blockchain based fog framework for making autoscaling decisions with the use of machine learning techniques. Evaluation is done by performing a series of experiments that show how the services are handled by the fog framework and how the autoscaling decisions are made. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item A Stacked Model Approach for Machine Learning-Based Traffic Prediction(Springer Science and Business Media Deutschland GmbH, 2024) Divakarla, U.; Chandrasekaran, K.The application of technology for sensing, analysis, control, and communication within ground transportation is referred to as an intelligent transportation system. This system aims to enhance safety, mobility, and efficiency. Intelligent Transportation Systems (ITSs) are in the process of development and implementation, leading to improved accuracy in predicting traffic flow. The efficacy of traveler information systems, public transportation, and advanced traffic control is said to depend on these systems. In order to effectively manage and lessen traffic congestion, practical execution is essential, as evidenced by the expanding use of data in transportation management. By employing machine learning (ML), it is possible to construct predictive models that incorporate diverse data from numerous sources. Predicting traffic movement, reducing congestion, and identifying optimal routes that consume the least time or energy all require traffic prediction, which involves forecasting traffic volume and density. Traffic estimation and prediction systems have the potential to reduce travel times and enhance traffic conditions by enabling more efficient utilization of available capacity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.