Smart Behavioural Filter for Industrial Internet of Things

Abstract

We are currently experiencing the fourth industrial revolution. This is what the German government initiative, first, has identified with 'Industry 4.0'. The manufacturing future will be marked and will go through the new automation technologies that are being introduced with Industrial Internet of Things (I2oT). Industrial Control Systems (ICSs) are exploiting I2oT for reducing costs and improving efficiency. However, ICSs are already jeopardized by an increasingly large set of threat vectors. Those threats are used by malicious actors to misuse physical Critical Infrastructures that usually are vital services for well-being. I2oT implementation increases the threat surface, generating new possible vulnerabilities.Information Technology (IT) classical approaches to cyber attacks cannot be applied to ICS due to their extreme differences from main priorities to resource constrains. Therefore, innovative approaches and equipment must be developed to suit with ICS world. In this paper, a Smart Behavioural Filter (SBF) for the PLCs (Programmable Logic Controllers) is proposed aiming to secure the PLC itself against logic attacks, that are stealth for other more classical security approaches. An example of the considered logic attacks is many open and close commands towards a valve in a short time. Those logic attacks are usually a sequence of well-formed packets in which the content

Smart Manufacturing Systems and Applied Industrial Technologies for a Sustainable Industry: A Systematic Literature Review

“…The system aims to track the tasks being performed by the machines, enabling automatic detection of the completion time of a job and based on that, dynamic rescheduling. Meanwhile, Corbò et al [41] applied IoT to develop a Smart Behavioral Filter (SBF) for the Programmable Logic Controllers with the aim to secure the PLC itself against logic attacks. Always in the manufacturing context, Um et al [42] proposed an IoT standard to monitor an injection moding machine.…”

Application of the Learning Automaton Model for Ensuring Cyber Resiliency

“…The second group of competing works uses intrusion detection systems (IDS) operating to monitor the system nodes, identify the incidents and respond to security impacts. IDS functionality can be supplemented with additional operation to complete adaptability (e.g., [45][46][47][48][49][50][51]. IDS may be extended with an intelligent a posteriori security management subsystem, e.g., Ref.…”

Evaluation and Selection of a Cybersecurity Platform ─ Case of the Power Sector in India

“…(He et al [39] observed that the most prevalent vulnerabilities are located at each interface between multiple components where information is exchanged. For example, the vulnerability in a SCADA system includes Network Protocols and Communication Networks, Human-Machine Interfaces, Database Servers, Application Servers, Remote Terminals, and Logic Controllers [38,40].…”