We describe the architecture of an anomaly detection system based on the Hidden Markov Model (HMM) for intrusion detection in Industrial Control Systems (ICS) and especially in SCADA systems interconnected using TCP/IP. The proposed system exploits the unique characteristics of ICS networks and protocols to efficiently detect multiple attack vectors. We evaluate the proposed system in terms of detection accuracy using as reference datasets made available by other researchers. These datasets refer to real industrial networks and contain a variety of identified attack vectors. We benchmark our findings against a large set of machine learning algorithms and demonstrate that our proposal exhibits superior performance characteristics.

Blockchain (BC) represents a disruptive technology that has been extensively used to ensure immutability of digital transactions. Starting as an underlying mechanism in the digital currency sector, it has been applicable in a wide range of sectors and application domains. Agriculture represents a sector of significance for overall sustainability challenges that is benefiting from digitalisation and technological evolution and the enforcement of Industry 4.0 paradigm shift towards precision agriculture. Introduction of Internet of Things, and Cyber-Physical Systems increase overall complexity, with Big Data analysis and Machine Learning technologies providing innovative applications. BC appears to be a promising technology for agriculture providing mechanisms for tracing of products and overall agricultural SC (SC) management from the farm to the fork. Authors investigate the challenges and open issues for the application of BC in agriculture performing a state of the art analysis along the PESTELS framework. A large number of challenges including technological ones, create big research potential for the evolution of the area.

The detection of malicious hardware logic (hardware Trojan) requires test patterns that succeed in exciting the malicious logic part. Testing of all possible input patterns is often prohibitively expensive. As an alternative, we explored previously the applicability of the combinatorial testing principles. In this paper, we turn our focus on the efficiency of this approach for triggering the hidden malicious logic. We present a series of experiments with Trojan designs of various activation patterns and lengths that target a cryptographic module performing AES cryptography. Our findings indicate that the available test suites succeed in triggering the malicious logic in all cases requiring only a very small number of test vectors. Thus, it is an efficient means for detecting malicious hardware logic.