An Analysis of Clustering Algorithms for Big Data

Clustering is an important data mining and tool for reading big records. There are difficulties for making use of clustering strategies to huge data duo to new challenges which might be raised with massive records. As large information is relating to terabytes and peta bytes of information and clustering algorithms are come with excessive computational costs, the question is the way to take care of with this hassle and how to install clustering techniques to big information and get the outcomes in a reasonable time. This study is aimed to review the style and progress of agglomeration algorithms to cope with massive knowledge challenges from first projected algorithms until modern novel solutions. The algorithms and the centered demanding situations for generating stepped forward clustering algorithms are introduced and analyzed, and later on the viable future path for extra superior algorithms are based on computational complexity. In this paper we discuss clustering algorithms and big data applications for real world things

Privacy Preservation Intrusion Detection Technique for SCADA Systems

Supervisory Control and Data Acquisition (SCADA) systems face the absence of a protection technique that can beat different types of intrusions and protect the data from disclosure while handling this data using other applications, specifically Intrusion Detection System (IDS). The SCADA system can manage the critical infrastructure of industrial control environments. Protecting sensitive information is a difficult task to achieve in reality with the connection of physical and digital systems. Hence, privacy preservation techniques have become effective in order to protect sensitive/private information and to detect malicious activities, but they are not accurate in terms of error detection, sensitivity percentage of data disclosure. In this paper, we propose a new Privacy Preservation Intrusion Detection (PPID) technique based on the correlation coefficient and Expectation Maximisation (EM) clustering mechanisms for selecting important portions of data and recognizing intrusive events. This technique is evaluated on the power system datasets for multiclass attacks to measure its reliability for detecting suspicious activities. The experimental results outperform three techniques in the above terms, showing the efficiency and effectiveness of the proposed technique to be utilized for current SCADA systems

Designing unsupervised intrusion detection for SCADA systems

Supervisory Control and Data Acquisition (SCADA) systems have been introduced to control and monitor industrial processes and our daily critical infrastructures such as electric power generation, water distribution and waste water collection systems. In recent years, the incorporation of Commercial-Off-The-Shelf (COTS) products such as standard hardware and software platforms have begun to be used in SCADA systems. This incorporation has allowed various products from different vendors to be integrated with each other to build a SCADA system at low cost. In addition, the integration of standard protocols (e.g. TCP/IP) into COTS products has increased their connectivity, thereby increasing productivity and profitability. However, this shift from proprietary and customized products to standard ones exposes these systems to cyber threats. An awareness of the potential threats to SCADA systems and the need to reduce risk and mitigate vulnerabilities has recently become an interesting research topic in the security area. A number of security measures have been extensively used in traditional IT such as management, filtering, encryption and intrusion detection. However, such measures cannot be applied directly to SCADA systems without considering their different nature and characteristics. Moreover, none of these security measures can completely protect a system from the potential threats. However, the full complement of these measures can create a robust security system. An Intrusion Detection System (IDS) is one of the security measures that has demonstrated promising results in detecting malicious activities in traditional IT systems, and therefore it has been adapted in SCADA systems. This thesis aims to develop an efficient and accurate unsupervised SCADA data-driven IDS. Four research tasks are being addressed in this thesis. The first task is related to the development of a framework for a SCADA security testbed that is intended to be an evaluation and testing environment for SCADA security in general, and for our proposed IDS in particular. While, the last three tasks are focused on developing a set of solutions that can, together, achieve the aim of this study