A Cognitive Framework to Secure Smart Cities

The advancement in technology has transformed Cyber Physical Systems and their interface with IoT into a more sophisticated and challenging paradigm. As a result, vulnerabilities and potential attacks manifest themselves considerably more than before, forcing researchers to rethink the conventional strategies that are currently in place to secure such physical systems. This manuscript studies the complex interweaving of sensor networks and physical systems and suggests a foundational innovation in the field. In sharp contrast with the existing IDS and IPS solutions, in this paper, a preventive and proactive method is employed to stay ahead of attacks by constantly monitoring network data patterns and identifying threats that are imminent. Here, by capitalizing on the significant progress in processing power (e.g. petascale computing) and storage capacity of computer systems, we propose a deep learning approach to predict and identify various security breaches that are about to occur. The learning process takes place by collecting a large number of files of different types and running tests on them to classify them as benign or malicious. The prediction model obtained as such can then be used to identify attacks. Our project articulates a new framework for interactions between physical systems and sensor networks, where malicious packets are repeatedly learned over time while the system continually operates with respect to imperfect security mechanisms.

Computational sciences in the upstream oil and gas industry

The predominant technical challenge of the upstream oil and gas industry has always been the fundamental uncertainty of the subsurface from which it produces hydrocarbon fluids. The subsurface can be detected remotely by, for example, seismic waves, or it can be penetrated and studied in the extremely limited vicinity of wells. Inevitably, a great deal of uncertainty remains. Computational sciences have been a key avenue to reduce and manage this uncertainty. In this review, we discuss at a relatively non-technical level the current state of three applications of computational sciences in the industry. The first of these is seismic imaging, which is currently being revolutionized by the emergence of full wavefield inversion, enabled by algorithmic advances and petascale computing. The second is reservoir simulation, also being advanced through the use of modern highly parallel computing architectures. Finally, we comment on the role of data analytics in the upstream industry. This article is part of the themed issue ‘Energy and the subsurface’.

A survey of high-performance computing scaling challenges

Commodity clusters revolutionized high-performance computing when they first appeared two decades ago. As scale and complexity have grown, new challenges in reliability and systemic resilience, energy efficiency and optimization and software complexity have emerged that suggest the need for re-evaluation of current approaches. This paper reviews the state of the art and reflects on some of the challenges likely to be faced when building trans-petascale computing systems, using insights and perspectives drawn from operational experience and community debates.