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2017-12-04
Rodrigues, P., Sreedharan, S., Basha, S. A., Mahesh, P. S..  2017.  Security threat identification using energy points. 2017 2nd International Conference on Anti-Cyber Crimes (ICACC). :52–54.

This research paper identifies security issues; especially energy based security attacks and enhances security of the system. It is very essential to consider Security of the system to be developed in the initial Phases of the software Cycle of Software Development (SDLC) as many billions of bucks are drained owing to security flaws in software caused due to improper or no security process. Security breaches that occur on software system are in umpteen numbers. Scientific Literature propose many solutions to overcome security issues, all security mechanisms are reactive in nature. In this paper new security solution is proposed that is proactive in nature especially for energy based denial of service attacks which is frequent in the recent past. Proposed solution is based on energy consumption by system known as energy points.

Athinaiou, M..  2017.  Cyber security risk management for health-based critical infrastructures. 2017 11th International Conference on Research Challenges in Information Science (RCIS). :402–407.

This brief paper reports on an early stage ongoing PhD project in the field of cyber-physical security in health care critical infrastructures. The research overall aims to develop a methodology that will increase the ability of secure recovery of health critical infrastructures. This ambitious or reckless attempt, as it is currently at an early stage, in this paper, tries to answer why cyber-physical security for health care infrastructures is important and of scientific interest. An initial PhD project methodology and expected outcomes are also discussed. The report concludes with challenges that emerge and possible future directions.

Mayer, N., Feltus, C..  2017.  Evaluation of the risk and security overlay of archimate to model information system security risks. 2017 IEEE 21st International Enterprise Distributed Object Computing Workshop (EDOCW). :106–116.

We evaluated the support proposed by the RSO to represent graphically our EAM-ISSRM (Enterprise Architecture Management - Information System Security Risk Management) integrated model. The evaluation of the RSO visual notation has been done at two different levels: completeness with regards to the EAM-ISSRM integrated model (Section III) and cognitive effectiveness, relying on the nine principles established by D. Moody ["The 'Physics' of Notations: Toward a Scientific Basis for Constructing Visual Notations in Software Engineering," IEEE Trans. Softw. Eng., vol. 35, no. 6, pp. 756-779, Nov. 2009] (Section IV). Regarding completeness, the coverage of the EAMISSRM integrated model by the RSO is complete apart from 'Event'. As discussed in Section III, this lack is negligible and we can consider the RSO as an appropriate notation to support the EAM-ISSRM integrated model from a completeness point of view. Regarding cognitive effectiveness, many gaps have been identified with regards to the nine principle established by Moody. Although no quantitative analysis has been performed to objectify this conclusion, the RSO can decently not be considered as an appropriate notation from a cognitive effectiveness point of view and there is room to propose a notation better on this aspect. This paper is focused on assessing the RSO without suggesting improvements based on the conclusions drawn. As a consequence, our objective for future work is to propose a more cognitive effective visual notation for the EAM-ISSRM integrated model. The approach currently considered is to operationalize Moody's principles into concrete metrics and requirements, taking into account the needs and profile of the target group of our notation (information security risk managers) through personas development and user experience map. With such an approach, we will be able to make decisions on the necessary trade-offs about our visual syntax, taking care of a specific context. We also aim at valida- ing our proposal(s) with the help of tools and approaches extracted from cognitive psychology research applied to HCI domain (e.g., eye tracking, heuristic evaluation, user experience evaluation…).

Lier, B. van.  2017.  The industrial internet of things and cyber security: An ecological and systemic perspective on security in digital industrial ecosystems. 2017 21st International Conference on System Theory, Control and Computing (ICSTCC). :641–647.

All over the world, objects are increasingly connected in networks such as the Industrial Internet of Things. Interconnections, intercommunications and interactions are driving the development of an entirely new whole in the form of the Industrial Internet of Things. Communication and interaction are the norm both for separate components, such as cyber-physical systems, and for the functioning of the system as a whole. This new whole can be likened to a natural ecosystem where the process of homeostasis ensures the stability and security of the whole. Components of such an industrial ecosystem, or even an industrial ecosystem as a whole, are increasingly targeted by cyber attacks. Such attacks not only threaten the functioning of one or multiple components, they also constitute a threat to the functioning of the new whole. General systems theory can offer a scientific framework for the development of measures to improve the security and stability of both separate components and the new whole.

Hwang, T..  2017.  NSF GENI cloud enabled architecture for distributed scientific computing. 2017 IEEE Aerospace Conference. :1–8.

GENI (Global Environment for Network Innovations) is a National Science Foundation (NSF) funded program which provides a virtual laboratory for networking and distributed systems research and education. It is well suited for exploring networks at a scale, thereby promoting innovations in network science, security, services and applications. GENI allows researchers obtain compute resources from locations around the United States, connect compute resources using 100G Internet2 L2 service, install custom software or even custom operating systems on these compute resources, control how network switches in their experiment handle traffic flows, and run their own L3 and above protocols. GENI architecture incorporates cloud federation. With the federation, cloud resources can be federated and/or community of clouds can be formed. The heart of federation is user identity and an ability to “advertise” cloud resources into community including compute, storage, and networking. GENI administrators can carve out what resources are available to the community and hence a portion of GENI resources are reserved for internal consumption. GENI architecture also provides “stitching” of compute and storage resources researchers request. This provides L2 network domain over Internet2's 100G network. And researchers can run their Software Defined Networking (SDN) controllers on the provisioned L2 network domain for a complete control of networking traffic. This capability is useful for large science data transfer (bypassing security devices for high throughput). Renaissance Computing Institute (RENCI), a research institute in the state of North Carolina, has developed ORCA (Open Resource Control Architecture), a GENI control framework. ORCA is a distributed resource orchestration system to serve science experiments. ORCA provides compute resources as virtual machines and as well as baremetals. ORCA based GENI ra- k was designed to serve both High Throughput Computing (HTC) and High Performance Computing (HPC) type of computes. Although, GENI is primarily used in various universities and research entities today, GENI architecture can be leveraged in the commercial, aerospace and government settings. This paper will go over the architecture of GENI and discuss the GENI architecture for scientific computing experiments.

Johnston, B., Lee, B., Angove, L., Rendell, A..  2017.  Embedded Accelerators for Scientific High-Performance Computing: An Energy Study of OpenCL Gaussian Elimination Workloads. 2017 46th International Conference on Parallel Processing Workshops (ICPPW). :59–68.

Energy efficient High-Performance Computing (HPC) is becoming increasingly important. Recent ventures into this space have introduced an unlikely candidate to achieve exascale scientific computing hardware with a small energy footprint. ARM processors and embedded GPU accelerators originally developed for energy efficiency in mobile devices, where battery life is critical, are being repurposed and deployed in the next generation of supercomputers. Unfortunately, the performance of executing scientific workloads on many of these devices is largely unknown, yet the bulk of computation required in high-performance supercomputers is scientific. We present an analysis of one such scientific code, in the form of Gaussian Elimination, and evaluate both execution time and energy used on a range of embedded accelerator SoCs. These include three ARM CPUs and two mobile GPUs. Understanding how these low power devices perform on scientific workloads will be critical in the selection of appropriate hardware for these supercomputers, for how can we estimate the performance of tens of thousands of these chips if the performance of one is largely unknown?