The protection of critical national infrastructures such as drinking water, gas, and electricity is extremely important as nations are dependent on their operation and steadiness. However, despite the value of such utilities their security issues have been poorly addressed which has resulted in a growing number of cyberattacks with increasing impact and huge consequences. There are many machine learning solutions to detect anomalies against this type of infrastructure given the popularity of such an approach in terms of accuracy and success in detecting zero-day attacks. However, machine learning algorithms are prone to adversarial attacks. In this paper, an energy-consumption-based machine learning approach is proposed to detect anomalies in a water treatment system and evaluate its robustness against adversarial attacks using a novel dataset. The evaluations include three popular machine learning algorithms and four categories of adversarial attack set to poison both training and testing data. The captured results show that although some machine learning algorithms are more robust against adversarial confrontations than others, overall, the proposed anomaly detection mechanism which is built on energy consumption metrics and its associated dataset are vulnerable to such attacks. To this end, a blockchain approach to protect the data during the training and testing phases of such machine learning models is proposed. The proposed smart contract is deployed in a public blockchain test network and their costs and mining time are investigated.
Digitalization of the petroleum industry entails a greater interconnection between Information Technology (IT) and Industrial Automation and Control Systems (IACS), and has led to an increased attack surface. To mitigate the consequences of incidents and to ensure a safe operation, the industry uses preparedness exercises. Previously, these exercises have concerned safety-related incidents. Today, digitalization requires the industry to also exercise security incidents, especially incidents that are directed towards IACS. While the need for more detailed guidelines in the area of cyber security and IACS has been explicitly called for by the industry, few guidelines are currently available. We aimed to lessen this shortcoming by investigating descriptions of events to use in exercises, known as scenarios. This project investigated what characterizes a scenario to be realistic and expedient for preparedness exercises on cyber attacks against IACS in the petroleum industry, with a focus on tabletop exercises. Based on data collected through interviews, a list of criteria that characterize such scenarios was created. The list was validated and approved by respondents from two different operator companies. The results highlight the importance of basing the scenario on today's threat landscape, making the scenarios plausible, and design the scenario such that it leads to a challenging tabletop exercise which also gives a sense of empowerment for the participants.
Municipalities are tasked with ensuring the cybersecurity of critical public services and functions in diverse areas such as safe water supply, healthcare, child protective services, and education with vastly different security requirements-all usually served from a common infrastructure with limited technical and organizational cybersecurity capabilities. This literature review identifies recent research on municipal and local government cybersecurity to identify current research areas, state of the art, and research methods used in research so far. We found research in the areas of smart cities, elections, human factors, operational technology, and crisis management. We also give suggestions for further research to develop better models for cybersecurity in cross-disciplinary organizations.
A robust democracy cannot be concn. Therefore, we propose a privacy-preserving, cost-effective and verifiable blockchain-based electronic score voting scheme. Our framework is premised on a watermarked quick response (QR) code-based secure identification mechanism for creating a tamper-resistant biometric voter ID card. Two watermarked image shares, namely, owner and master shares, are created through XOR-based visual cryptography (VC) which are managed by the ID owner and a semi-trusted constituency authority (CA). Election Commission officer (ECO) commences the cryptographic system setup by distributing the secret key shares amongst all the CAs. First, voters show their share for their details verification and biometric authentication. If successful, they use the modified ElGamal homomorphic encryption to enable additive computations on their score votes while ensuring their secrecy. The integrity and validity of the encrypted vote are ensured through a non-interactive partial knowledge range proof (NIPKRP) based on the Pederson commitment protocol. On the counting day, the vote ciphertext en route to CA is subjected to progressive aggregation. This intra-constituency level computation follows the range voting paradigm. Then, CAs decrypt the resultant encrypted ballots and determine the winner for their respective constituencies after comparing the obtained scores. Accordingly, each CA creates and transmits an encrypted binary vector to ECO for pluralistic inter-constituency aggregation. Finally, ECO uses its secret key to decrypt the final encrypted aggregate vector to determine the majority winning party. If at least two maximum scores or seat counts come out to be identical, most significant bit (MSB) is given priority to break the tie. CAs can collaborate to verify the results by using their secret shares. Our proposed scheme facilitates secure, tamperproof and decentralized score aggregation for e-voting with a suitable tie-breaker. The efficacy and usability of the scheme have been reported with experimental results and security proofs.
Recent studies have demonstrated the interest of analyzing GNSS (Global Navigation Satellite System) and AIS (Automatic Id entification System) data to improve the safety of naval infrastructures for a wide spectrum of maritime applications. However, in-depth analyses also underline the sensitivity of these systems to attacks such as jamming and spoofing. In this context, it is essential that researchers, specialized organizations and companies rely on realistic data to improve these types of systems to better detect and cope with potential threats. However, because of the lack of open data sets, or due to financial, technical or operational reasons, the use of simulated data is preferred in most cases over real life data, which can lead to biases. To cope with this challenge, we have developed a prototype called "HAPPINESS" for "Holistic APProach of Integrated Navigation Equipment for Cybersecurity at Sea". The main objective of this dedicated and autonomous embedded system is to collect navigation data in real time without using proprietary or restrictive protocols. The generated open data, then continuously feeds a cyber naval platform able to reproduce the functional and operational systems of a ship. This prototype allows to reproduce the kinematics of a ship in various contexts (like specific maneuvers, long tracks, docking…) in NMEA format in order to design highly realistic scenarios based on real life data and allowing to obtain more complete and richer data (than those freely accessible online) in terms of information, giving additional means to detect anomalies on navigation systems.
