A Synchronized Shared Key Generation Method for Maintaining End-to-End Security of Big Data Streams

A large number of mission critical applications ranging from disaster management to smart city are built on the Internet of Things (IoT) platform by deploying a number of smart sensors in a heterogeneous environment. The key requirements of such applications are the need of near real-time stream data processing in large scale sensing networks. This trend gives birth of an area called big data stream. One of the key problems in big data stream is to ensure the end-to-end security. To address this challenge, we proposed Dynamic Prime Number Based Security Verification (DPBSV) and Dynamic Key Length Based Security Framework (DLSeF) methods for big data streams based on the shared key derived from synchronized prime numbers in our earlier works. One of the major shortcomings of these methods is that they assume synchronization of the shared key. However, the assumption does not hold when the communication between Data Stream Manager (DSM) and sensing devices is broken. To address this problem, this paper proposes an adaptive technique to synchronize the shared key without communication between sensing devices and DSM, where sensing devices obtain the shared key re-initialization properties from its neighbours. Theoretical analyses and experimental results show that the proposed technique can be integrated with our DPBSV and DLSeF methods without degrading the performance and efficiency. We observed that the proposed synchronization method also strengthens the security of the models

Towards efficient and lightweight security architecture for big sensing data streams

University of Technology Sydney. Faculty of Engineering and Information Technology.A large number of mission critical applications from disaster management to health monitoring are contributing to the Internet of Things (IoT) by deploying a number of smart sensing devices in a heterogeneous environment. Resource constrained sensing devices are being used widely to build and deploy self-organising wireless sensor networks for a variety of critical applications. Many such devices sense the deployed environment and generate a variety of data and send them to the server for analysis as data streams. The key requirement of such applications is the need for near real-time stream data processing in large scale sensing networks. This trend gives birth to an area called big sensing data streams. One of the key problems in big data is to ensure end-to-end security where a Data Stream Manager (DSM) must always verify the security of the data before executing a query to ensure data security (i.e., confidentiality, integrity, authenticity, availability and freshness) as the medium of communication is untrusted. A malicious adversary may access or tamper with the data in transit. One of the challenging tasks in such applications is to ensure the trustworthiness of collected data so that any decisions are made on the correct data, followed by protecting the data streams from information leakage and unauthorised access. This thesis considers end-to-end means from source sensors to cloud data centre. Although some security issues are not new, the situation is aggravated due to the features of the five Vs of big sensing data streams: Volume, Velocity, Variety, Veracity and Value. Therefore, it is still a significant challenge to achieve data security in big sensing data streams. Providing data security for big sensing data streams in the context of near real time analytics is a challenging problem. This thesis mainly investigates the problems and security issues of big sensing data streams from the perspectives of efficient and lightweight processing. The big data streams computing advantages including real-time processing in efficient and lightweight fashion are exploited to address the problem, aiming at gaining high scalability and effectiveness. Specifically, the thesis examines three major properties in the lifecycle of security in big data streams environments. The three properties include authenticity, integrity and confidentiality also known as the AIC triad, which is different to CIA triad used in general data security. Accordingly, a lightweight security framework is proposed to maintain data integrity and a selective encryption technique to maintain data confidentiality over big sensing data streams. These solutions provide data security from source sensing devices to the processing layer of cloud data centre. The thesis also explore a further proposal on a lattice based information flow control model to protect data against information leakage and unauthorised access after performing the security verification at DSM. By integrating the access control model, this thesis provides an end-to-end security of big sensing data streams i.e. source sensing device to the cloud data centre processing layer. This thesis demonstrates that our solutions not only strengthen the data security but also significantly improve the performance and efficiency of big sensing data streams compared with existing approaches

Novel holistic architecture for analytical operation on sensory data relayed as cloud services

With increasing adoption of the sensor-based application, there is an exponential rise of the sensory data that eventually take the shape of the big data. However, the practicality of executing high end analytical operation over the resource-constrained big data has never being studied closely. After reviewing existing approaches, it is explored that there is no cost effective schemes of big data analytics over large scale sensory data processiing that can be directly used as a service. Therefore, the propsoed system introduces a holistic architecture where streamed data after performing extraction of knowedge can be offered in the form of services. Implemented in MATLAB, the proposed study uses a very simplistic approach considering energy constrained of the sensor nodes to find that proposed system offers better accuracy, reduced mining duration (i.e. faster response time), and reduced memory dependencies to prove that it offers cost effective analytical solution in contrast to existing system

Framework for efficient transformation for complex medical data for improving analytical capability

The adoption of various technological advancement has been already adopted in the area of healthcare sector. This adoption facilitates involuntary generation of medical data that can be autonomously programmed to be forwarded to a destined hub in the form of cloud storage units. However, owing to such technologies there is massive formation of complex medical data that significantly acts as an overhead towards performing analytical operation as well as unwanted storage utilization. Therefore, the proposed system implements a novel transformation technique that is capable of using a template based stucture over cloud for generating structured data from highly unstructured data in a non-conventional manner. The contribution of the propsoed methodology is that it offers faster processing and storage optimization. The study outcome also proves this fact to show propsoed scheme excels better in performance in contrast to existing data transformation scheme

Adjoining Internet of Things with Data Mining : A Survey

The Interactive Data Corporative (IDC) conjectures that by 2025 the worldwide data circle will develop to 163ZB (that is a trillion gigabytes) which is ten times the 16.1ZB of information produced in 2016. The Internet of Things is one of the hot topics of this living century and researchers are heading for mass adoption 2019 driven by better than-expected business results. This information will open one of a kind of user experience and another universe of business opening. The huge information produced by the Internet of Things (IoT) are considered of high business esteem, and information mining calculations can be connected to IoT to extract hidden data from information. This paper concisely discusses the work done in sequential manner of time in different fields of IOT along with its outcome and research gap. This paper also discusses the various aspects of data mining functionalities with IOT. The recommendation for the Challenges in IOT that can be adopted for betterment is given. Finally, this paper presents the vision for how IOT will have impact on changing the distant futur

Framework for cost-effective analytical modelling for sensory data over cloud environment

In order to offer sensory data as a service over the cloud, it is necessary to execute a cost-effective and yet precise data analytical logic within the sensing units. However, it is quite questionable as such forms of analytical operation are quite resource dependent which cannot be offered by the resource constraint sensory units. Therefore, the proposed paper introduces a novel approach of performing cost-effective data analytical method in order to extract knowledge from big data over the cloud. The proposed study uses a novel concept of the frequent pattern along with a tree-based approach in order to develop an analytical model for carrying out the mining operation in the large-scale sensor deployment over the cloud environment. Using a simulation-based approach over the mathematical model, the proposed model exhibit reduced mining duration, controlled energy dissipation, and highly optimized memory demands for all the resource constraint nodes