Abstract
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.
Highlights
The world is at the brink of a new digital revolution and Cyber Physical Systems (CPS) based on the Internet of Things (IoT) networks mark the frontier
A key challenge is that security solutions for IoT should not hinder the openness of the network, nor should they introduce additional latency or overhead to communications across the network. These requirements are achieved by incorporating security into the design of IoT infrastructures. This project is focused on two main principles: “adaptive security architecture” and “IoT-based CPS or ICPS” both of which are listed on Gartner’s 2016 top 10 strategic technology trends
Smart objects and embedded sensors are currently secured based on the same best practices of traditional networks without considering the limitations imposed by the proliferation of smart nodes in terms of processing power and memory
Summary
The world is at the brink of a new digital revolution and Cyber Physical Systems (CPS) based on the Internet of Things (IoT) networks mark the frontier. Smart objects and embedded sensors are currently secured based on the same best practices of traditional networks without considering the limitations imposed by the proliferation of smart nodes in terms of processing power and memory. This is mainly due to limited research in this field. New generations of devices bring along newer and more sophisticated generation of threat agents and attacks This concern is addressed by integrating security in design and preventing the problem from happening. LTE) in providing IoT connectivity? b) Can Deep Learning (DL) be as successful on IoT security as it has been in computer vision and speech recognition? c) Can security by design guideline and frameworks outperform the existing security patches and protocols? and d) How different are the security gaps for smart city sensors and gateways from those of traditional networks
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