CSI Transactions on ICT
Springer Science+Business Media
About: CSI Transactions on ICT is an academic journal. The journal publishes majorly in the area(s): Computer science & Cloud computing. It has an ISSN identifier of 2277-9078. Over the lifetime, 297 publications have been published receiving 1262 citations.
TL;DR: This paper reviews the research literature since 2000 and categorizes developments in the field into four major categories and highlights the observations made by previous researchers and summarizes the research directions for the future.
Abstract: Digital forensics is the process of employing scientific principles and processes to analyze electronically stored information and determine the sequence of events which led to a particular incident. In this digital age, it is important for researchers to become aware of the recent developments in this dynamic field and understand scope for the future. The past decade has witnessed significant technological advancements to aid during a digital investigation. Many methodologies, tools and techniques have found their way into the field designed on forensic principles. Digital forensics has also witnessed many innovative approaches that have been explored to acquire and analyze digital evidence from diverse sources. In this paper, we review the research literature since 2000 and categorize developments in the field into four major categories. In recent years the exponential growth of technological has also brought with it some serious challenges for digital forensic research which is elucidated. Within each category, research is sub-classified into conceptual and practical advancements. We highlight the observations made by previous researchers and summarize the research directions for the future.
TL;DR: The aim of this paper is to employ and analyze different data mining techniques for the prediction of heart disease in a patient through extraction of interesting patterns from the dataset using vital parameters and reveal that Artificial Neural Networks outperformed Naive Bayes and Decision Tree.
Abstract: The healthcare industry is a vast field with a plethora of data about patients,added to the huge medical records every passing day. In terms of science, this industry is ’information rich’ yet ’knowledge poor’. However, data mining with its various analytical tools and techniques plays a major role in reducing the use of cumbersome tests used on patients to detect a disease. The aim of this paper is to employ and analyze different data mining techniques for the prediction of heart disease in a patient through extraction of interesting patterns from the dataset using vital parameters. This paper strives to bring out the methodology and implementation of these techniques-Artificial Neural Networks, Decision Tree and Naive Bayes and stress upon the results and conclusion induced on the basis of accuracy and time complexity. By far, the observations reveal that Artificial Neural Networks outperformed Naive Bayes and Decision Tree.
TL;DR: Some of the critical issues along with state of the art solutions towards them like heterogeneity and interoperability, scalability, QoS, and security are presented.
Abstract: Faster development of sensor and network technologies is facilitating immense deployment of Internet of Things (IoT) towards creating a smart world. In IoT, a massive number of heterogeneous resource–constraint devices communicate with each other without any human intervention and generate a huge amount of data. Unique research challenges posed by IoT are fascinating the research community. This paper presents some of the critical issues along with state of the art solutions towards them. In-depth discussion is provided on various key issues like heterogeneity and interoperability, scalability, QoS, and security. Directions for further researches in those areas are also pointed out.
TL;DR: Proposed experimental research model can be used to mitigate the attacks at application layer and data link layer by adopting the IEC 62351 standards.
Abstract: Current hierarchical SCADA systems uses communication protocols which aren’t having the inbuilt security mechanism. This lack of security mechanism will help attackers to sabotage the SCADA system. However, to cripple down the SCADA systems completely coordinated communication channel attacks can be performed. IEC 60870-5-101 and IEC 60870-5-104 protocols are widely used in current SCADA systems in power utilities sector. These protocols are lacking in the application layer and the data link layer security. Application layer security is necessary to protect the SCADA systems from Spoofing and Non-Repudiation attacks. Data link layer security is necessary to protect the systems from the Sniffing, Data modification and Replay attacks. IEC 60870-5-101 & 104 communication protocol vulnerabilities and their exploitation by coordinated attacks are explained in this paper. Proposed experimental research model can be used to mitigate the attacks at application layer and data link layer by adopting the IEC 62351 standards.
TL;DR: The results shows that the accuracy of K-nearest neighbour is better than Naive Bayes to detect thyroid disease.
Abstract: Data mining is an important research activity in the field of medical sciences since there is a requirement of efficient methodologies for analyzing and detecting diseases. Data mining applications are used for the management of healthcare, health information, patient care system, etc. It also plays a major role in analyzing survivability of a disease. Classification and clustering are the popular data mining techniques used to understand the various parameters of the health data set. In this research work, various classification models are used to classify thyroid disease based on the parameters like TSH, T4U and goiter. Several classification techniques like K-nearest neighbour, support vector machine and Naive Bayes are used. The experimental study has been conducted using Rapid miner tool and the results shows that the accuracy of K-nearest neighbour is better than Naive Bayes to detect thyroid disease.
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