Early heart disease detection using data mining techniques with hadoop map reduce Early Heart Disease Detection Using Data Mining Techniques with Hadoop Map Reduce

International audienceHeart and other organs are important parts in human body. As per World Health Organisation(WHO)'s statistics, the cause of death in all over world is mostly due to cardiovascular diseases. The reason behind this are sedentary lifestyle which may lead to obesity, increase in cholesterol level, high blood pressure and hypertension. In this paper, by using various data mining techniques, such as Naive Bayes(NB), Decision Tree(DT), Artificial Intelligence (AI), Neural Network (NN) and clustering algorithms such as Association Rules. Support Vector Machine (SVM) and K-NN algorithms are used to extract the Knowledge from the large number of data set. The generated reports help doctors and nurses to identify about disease and their levels with which they can provide a better treatment to the patient. Text Mining is most commonly used mining technique in health care industry. In this paper we compare K-means clustering algorithm with Map Reduce Algorithm's implementation efficiency in parallel and distributed systems

Distributed computational model for shared processing on Cyber-Physical System environments

Cyber-Physical Systems typically consist of a combination of mobile devices, embedded systems and computers to monitor, sense, and actuate with the surrounding real world. These computing elements are usually wireless, interconnected to share data and interact with each other, with the server part and also with cloud computing services. In such a heterogeneous environment, new applications arise to meet ever-increasing needs and these are an important challenge to the processing capabilities of devices. For example, automatic driving systems, manufacturing environments, smart city management, etc. To meet the requirements of said application contexts, the system can create computing processes to distribute the workload over the network and/or a cloud computing server. Multiple options arise in relation to what network nodes should support the execution of the processes. This paper focuses on this problem by introducing a distributed computational model to dynamically share these tasks among the computing nodes and considering the inherent variability of the context in these environments. Our novel approach promotes the integration of the computing resources, with externally supplied cloud services, to fulfill modern application requirements. A prototype implementation for the proposed model has been built and an application example has been designed to validate the proposal in a real working environment

Exploring the motivation behind cybersecurity insider threat and proposed research agenda

Cyber exploitation and malicious activities have become more sophisticated. Insider threat is one of the most significant cyber security threat vector, while posing a great concern to corporations and governments. An overview of the fundamental motivating forces and motivation theory are discussed. Such overview is provided to identify motivations that lead trusted employees to become insider threats in the context of cyber security. A research agenda with two sequential experimental research studies are outlined to address the challenge of insider threat mitigation by a prototype development. The first proposed study will classify data intake feeds, as recognized and weighted by cyber security experts, in an effort to establish predictive analytics of novel correlations of activities that may lead to cyber security incidents. It will also develop approach to identify how user activities can be compared against an established baseline, the user’s network cyber security pulse, with visualization of simulated users’ activities. Additionally, the second study will explain the process of assessing the usability of a developed visualization prototype that intends to present correlated suspicious activities requiring immediate action. Successfully developing the proposed prototype via feeds aggregation and an advanced visualization from the proposed research could assist in the mitigation of malicious insider threat

Development and Validation of a Proof-of-Concept Prototype for Analytics-based Malicious Cybersecurity Insider Threat in a Real-Time Identification System

Insider threat has continued to be one of the most difficult cybersecurity threat vectors detectable by contemporary technologies. Most organizations apply standard technology-based practices to detect unusual network activity. While there have been significant advances in intrusion detection systems (IDS) as well as security incident and event management solutions (SIEM), these technologies fail to take into consideration the human aspects of personality and emotion in computer use and network activity, since insider threats are human-initiated. External influencers impact how an end-user interacts with both colleagues and organizational resources. Taking into consideration external influencers, such as personality, changes in organizational polices and structure, along with unusual technical activity analysis, would be an improvement over contemporary detection tools used for identifying at-risk employees. This would allow upper management or other organizational units to intervene before a malicious cybersecurity insider threat event occurs, or mitigate it quickly, once initiated. The main goal of this research study was to design, develop, and validate a proof-of-concept prototype for a malicious cybersecurity insider threat alerting system that will assist in the rapid detection and prediction of human-centric precursors to malicious cybersecurity insider threat activity. Disgruntled employees or end-users wishing to cause harm to the organization may do so by abusing the trust given to them in their access to available network and organizational resources. Reports on malicious insider threat actions indicated that insider threat attacks make up roughly 23% of all cybercrime incidents, resulting in $2.9 trillion in employee fraud losses globally. The damage and negative impact that insider threats cause was reported to be higher than that of outsider or other types of cybercrime incidents. Consequently, this study utilized weighted indicators to measure and correlate simulated user activity to possible precursors to malicious cybersecurity insider threat attacks. This study consisted of a mixed method approach utilizing an expert panel, developmental research, and quantitative data analysis using the developed tool on simulated data set. To assure validity and reliability of the indicators, a panel of subject matter experts (SMEs) reviewed the indicators and indicator categorizations that were collected from prior literature following the Delphi technique. The SMEs’ responses were incorporated into the development of a proof-of-concept prototype. Once the proof-of-concept prototype was completed and fully tested, an empirical simulation research study was conducted utilizing simulated user activity within a 16-month time frame. The results of the empirical simulation study were analyzed and presented. Recommendations resulting from the study also be provided