Bio: E. Sathiyamoorthy is an academic researcher from VIT University. The author has contributed to research in topic(s): Cloud computing & Intrusion detection system. The author has an hindex of 6, co-authored 21 publication(s) receiving 83 citation(s).
TL;DR: The experimental results demonstrate that FTHDPS is much efficient to provide a reliable and accurate recommendation to the heart patients, by using Fourier transformation and machine learning technique to predict the chronic heart diseases effectively.
Abstract: Recently, using of the intelligent technologies in the field of clinical decision making is increased rapidly to improve the lifestyles of patients and to help for reducing the workload and cost concerned in their healthcare. Heart diseases are one of the primary causes of death. However, if the diseases are identified at the early stage, the rate of death can be decreased. Thus, the disease identification process has become a matter of concern. An efficient medical recommendation system has been proposed in this paper, namely Fourier transformation-based heart disease prediction system (FTHDPS) by using Fourier transformation and machine learning technique to predict the chronic heart diseases effectively. Here, the input sequences rely on the patient’s time series details or data, which are crumbled by Fourier transformation for extracting the frequency information. In FTHDPS, a bagging model is utilized for predicting the conditions of the patients in advance to produce the absolute recommendation. In FTHDPS, three classifiers are used, namely artificial neural network, Naive Bayes and support vector machine, and real-life time series chronic heart disease data are used to evaluate the proposed model. The experimental results demonstrate that FTHDPS is much efficient to provide a reliable and accurate recommendation to the heart patients.
TL;DR: This paper proposes an agent based model that would secure the users data over the cloud and implemented various algorithms to provide a secured system.
Abstract: Cloud computing is a class of the next generation highly scalable and distributed computing platform in which computing resources are offered ‘as a service’ leveraging virtualization and Internet technologies. Cloud computing does not clearly define boundaries to protect the user data. The data, communications, services and other important resource are controlled by the cloud service provider. The alarming situation is the probable leakage of sensitive data by service provider. To protect the data from service provider, In this paper we propose an agent based model that would secure the users data over the cloud and implemented various algorithms to provide a secured system.
TL;DR: This paper has proposed the Strong and Encrypted Session ID to prevent the session hijack attacks in web applications and tested the integrity of the session ID of length 32, 92 and 212 characters in a web application.
Abstract: Most of the web applications are establishing the web session with the client. It is very important to protect the wireless networks against session hijacking attack. Session Hijack attack is easy to execute and difficult to detect. Wireless networks do not have specific boundary regions for the packets to be transferred. As the data packets are transferred in air, the chances of sniffing the network packets by the hackers or attackers are high by using the network sniffing tools. In this paper, we have proposed the Strong and Encrypted Session ID to prevent the session hijack attacks in web applications. Session ID is generated and the generated Session ID is encrypted, using a Secret Key Sharing algorithm and decrypted at the client side. We have tested the integrity of the session ID of length 32, 92 and 212 characters in a web application. Attacks are executed to capture the session ID of a web application. Our experimental results proved that 212 characters encrypted session ID completely prevents the session hijack attacks in web applications of wireless networks.
TL;DR: This paper identifies the best feature selection algorithm to select the important and useful features from the network dataset by identifying the optimal feature selection methods for intrusion detection.
Abstract: The Intrusion Detection System (IDS) deals with the huge amount of network data that includes redundant and irrelevant features causing slow training and testing procedure, higher resource usage and poor detection ratio. Feature selection is a vital preprocessing step in intrusion detection. Hence, feature selec-tion is an essential issue in intrusion detection and need to be addressed by selec-ting the appropriate feature selection algorithm. A major challenge to select the optimal feature selection methods can precisely calculate the relevance of fea-tures to the detection process and the redundancy among features. In this paper, we study the concepts and algorithms used for feature selection algorithms in the IDS. We conclude this paper by identifying the best feature selection algorithm to select the important and useful features from the network dataset.
TL;DR: An Ontology-based Multi-Agent Model Intrusion Detection System (OMAMIDS) for detecting web service attacks achieves high detection rate and accuracy and lower false positive rate than the existing techniques.
Abstract: Web service plays a significant role in the Internet applications. According to the current researchers, the web services are highly prone to the cyber-attacks. The Intrusion Detection System (IDS)...
TL;DR: A detailed investigation and analysis of various machine learning techniques have been carried out for finding the cause of problems associated with variousMachine learning techniques in detecting intrusive activities and future directions are provided for attack detection using machinelearning techniques.
Abstract: Intrusion detection is one of the important security problems in todays cyber world. A significant number of techniques have been developed which are based on machine learning approaches. However, they are not very successful in identifying all types of intrusions. In this paper, a detailed investigation and analysis of various machine learning techniques have been carried out for finding the cause of problems associated with various machine learning techniques in detecting intrusive activities. Attack classification and mapping of the attack features is provided corresponding to each attack. Issues which are related to detecting low-frequency attacks using network attack dataset are also discussed and viable methods are suggested for improvement. Machine learning techniques have been analyzed and compared in terms of their detection capability for detecting the various category of attacks. Limitations associated with each category of them are also discussed. Various data mining tools for machine learning have also been included in the paper. At the end, future directions are provided for attack detection using machine learning techniques.
TL;DR: This survey provides a comprehensive discussion of all aspects of MAS, starting from definitions, features, applications, challenges, and communications to evaluation, and a classification on MAS applications and challenges is provided.
Abstract: Multi-agent systems (MASs) have received tremendous attention from scholars in different disciplines, including computer science and civil engineering, as a means to solve complex problems by subdividing them into smaller tasks. The individual tasks are allocated to autonomous entities, known as agents. Each agent decides on a proper action to solve the task using multiple inputs, e.g., history of actions, interactions with its neighboring agents, and its goal. The MAS has found multiple applications, including modeling complex systems, smart grids, and computer networks. Despite their wide applicability, there are still a number of challenges faced by MAS, including coordination between agents, security, and task allocation. This survey provides a comprehensive discussion of all aspects of MAS, starting from definitions, features, applications, challenges, and communications to evaluation. A classification on MAS applications and challenges is provided along with references for further studies. We expect this paper to serve as an insightful and comprehensive resource on the MAS for researchers and practitioners in the area.
TL;DR: A smart healthcare system is proposed for heart disease prediction using ensemble deep learning and feature fusion approaches and obtains accuracy of 98.5%, which is higher than existing systems.
Abstract: The accurate prediction of heart disease is essential to efficiently treating cardiac patients before a heart attack occurs. This goal can be achieved using an optimal machine learning model with rich healthcare data on heart diseases. Various systems based on machine learning have been presented recently to predict and diagnose heart disease. However, these systems cannot handle high-dimensional datasets due to the lack of a smart framework that can use different sources of data for heart disease prediction. In addition, the existing systems utilize conventional techniques to select features from a dataset and compute a general weight for them based on their significance. These methods have also failed to enhance the performance of heart disease diagnosis. In this paper, a smart healthcare system is proposed for heart disease prediction using ensemble deep learning and feature fusion approaches. First, the feature fusion method combines the extracted features from both sensor data and electronic medical records to generate valuable healthcare data. Second, the information gain technique eliminates irrelevant and redundant features, and selects the important ones, which decreases the computational burden and enhances the system performance. In addition, the conditional probability approach computes a specific feature weight for each class, which further improves system performance. Finally, the ensemble deep learning model is trained for heart disease prediction. The proposed system is evaluated with heart disease data and compared with traditional classifiers based on feature fusion, feature selection, and weighting techniques. The proposed system obtains accuracy of 98.5%, which is higher than existing systems. This result shows that our system is more effective for the prediction of heart disease, in comparison to other state-of-the-art methods.
Abstract: Biometric research is directed increasingly toward Wearable Biometric Systems (WBS) for user authentication and identification. However, prior to engaging in WBS research, how their operational dynamics and design considerations differ from those of Traditional Biometric Systems (TBS) must be understood. While the current literature is cognizant of those differences, there is no effective work that summarizes the factors where TBS and WBS differ, namely, their modality characteristics, performance, security, and privacy. To bridge the gap, this article accordingly reviews and compares the key characteristics of modalities, contrasts the metrics used to evaluate system performance, and highlights the divergence in critical vulnerabilities, attacks, and defenses for TBS and WBS. It further discusses how these factors affect the design considerations for WBS, the open challenges, and future directions of research in these areas. In doing so, the article provides a big-picture overview of the important avenues of challenges and potential solutions that researchers entering the field should be aware of. Hence, this survey aims to be a starting point for researchers in comprehending the fundamental differences between TBS and WBS before understanding the core challenges associated with WBS and its design.
TL;DR: This study proposes a new classification from the point of view of Cloud Computing, based on the reference architecture proposed by the National Institute of Standards and Technology and the different responsibilities of each of the roles that participate in the Cloud Computing paradigm as identified in the architecture: Provider, Consumer, Broker, Carrier and Auditor.
Abstract: In the state of the art, there are very few studies on agent-based Cloud Computing. Nevertheless, this is an emerging trend and the number of studies and applications in this field is beginning to increase. Cloud Computing and Agents are complementary technologies. The features of Cloud Computing can provide advanced computational characteristics to multi-agent systems. In turn, the inclusion of agent systems in the core of the Cloud platform makes it possible to incorporate different functionalities, such as reasoning and learning capabilities. This study analyzes the emerging relationship between both distributed systems. Specifically, this study proposes a new classification from the point of view of Cloud Computing, based on the reference architecture proposed by the National Institute of Standards and Technology and the different responsibilities of each of the roles that participate in the Cloud Computing paradigm as identified in the architecture: Provider, Consumer, Broker, Carrier and Auditor.