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Register a new userSecuring Manufacturing Using Blockchain
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Added by
Ali Dorri
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Due to the rise of Industrial Control Systems (ICSs) cyber-attacks in the recent decade, various security frameworks have been designed for anomaly detection. While advanced ICS attacks use sequential phases to launch their final attacks, existing anomaly detection methods can only monitor a single source of data. Therefore, analysis of multiple security data can provide comprehensive and system-wide anomaly detection in industrial networks. In this paper, we propose an anomaly detection framework for ICSs that consists of two stages: i) blockchain-based log management where the logs of ICS devices are collected in a secure and distributed manner, and ii) multi-source anomaly detection where the blockchain logs are analysed using multi-source deep learning which in turn provides a system wide anomaly detection method. We validated our framework using two ICS datasets: a factory automation dataset and a Secure Water Treatment (SWAT) dataset. These datasets contain physical and network level normal and abnormal traffic. The performance of our new framework is compared with single-source machine learning methods. The precision of our framework is 95% which is comparable with single-source anomaly detectors.
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The proliferation of digitization and complexity of connectivity in Cyber-Physical Systems (CPSs) calls for a mechanism that can evaluate the functionality and security of critical infrastructures. In this regard, Digital Twins (DTs) are revolutionizing the CPSs. Driven by asset-centric data, DTs are virtual replicas of physical systems that mirror every facet of a product or process and can provide actionable insights through monitoring, optimization, and prediction. Furthermore, replication and simulation modes in DTs can prevent and detect security flaws in the CPS without obstructing the ongoing operations of the live system. However, such benefits of DTs are based on an assumption about data trust, integrity, and security. Data trustworthiness is considered to be more critical when it comes to the integration and interoperability of multiple components or sub-components among different DTs owned by multiple stakeholders to provide an aggregated view of the complex physical system. Moreover, analyzing the huge volume of data for creating actionable insights in real-time is another critical requirement that demands automation. This article focuses on securing CPSs by integrating Artificial Intelligence (AI) and blockchain for intelligent and trusted DTs. We envision an AI-aided blockchain-based DT framework that can ensure anomaly prevention and detection in addition to responding against novel attack vectors in parallel with the normal ongoing operations of the live systems. We discuss the applicability of the proposed framework for the automotive industry as a CPS use case. Finally, we identify challenges that impede the implementation of intelligence-driven architectures in CPS.
This paper we define a new Puzzle called Proof-of-Interaction and we show how it can replace, in the Bitcoin protocol, the Proof-of-Work algorithm.
Face authentication usually utilizes deep learning models to verify users with high recognition accuracy. However, face authentication systems are vulnerable to various attacks that cheat the models by manipulating the digital counterparts of human faces. So far, lots of liveness detection schemes have been developed to prevent such attacks. Unfortunately, the attacker can still bypass these schemes by constructing wide-ranging sophisticated attacks. We study the security of existing face authentication services (e.g., Microsoft, Amazon, and Face++) and typical liveness detection approaches. Particularly, we develop a new type of attack, i.e., the low-cost 3D projection attack that projects manipulated face videos on a 3D face model, which can easily evade these face authentication services and liveness detection approaches. To this end, we propose FaceLip, a novel liveness detection scheme for face authentication, which utilizes unforgeable lip motion patterns built upon well-designed acoustic signals to enable a strong security guarantee. The unique lip motion patterns for each user are unforgeable because FaceLip verifies the patterns by capturing and analyzing the acoustic signals that are dynamically generated according to random challenges, which ensures that our signals for liveness detection cannot be manipulated. Specially, we develop robust algorithms for FaceLip to eliminate the impact of noisy signals in the environment and thus can accurately infer the lip motions at larger distances. We prototype FaceLip on off-the-shelf smartphones and conduct extensive experiments under different settings. Our evaluation with 44 participants validates the effectiveness and robustness of FaceLip.
Transparent authentication (TA) schemes are those in which a user is authenticated by a verifier without requiring explicit user interaction. By doing so, those schemes promise high usability and security simultaneously. The majority of TA implementations rely on the received signal strength as an indicator for the proximity of a user device (prover). However, such implicit proximity verification is not secure against an adversary who can relay messages over a larger distance. In this paper, we propose a novel approach for thwarting relay attacks in TA schemes: the prover permits access to authentication credentials only if it can confirm that it is near the verifier. We present STASH, a system for relay-resilient transparent authentication in which the prover does proximity verification by comparing its approach trajectory towards the intended verifier with known authorized reference trajectories. Trajectories are measured using low-cost sensors commonly available on personal devices. We demonstrate the security of STASH against a class of adversaries and its ease-of-use by analyzing empirical data, collected using a STASH prototype. STASH is efficient and can be easily integrated to complement existing TA schemes.
OAuth 2.0 is the industry-standard protocol for authorization. It facilitates secure service provisioning, as well as secure interoperability among diverse stakeholders. All OAuth 2.0 protocol flows result in the creation of an access token, which is then used by a user to request access to a protected resource. Nevertheless, the definition of access tokens is transparent to the OAuth 2.0 protocol, which does not specify any particular token format, how tokens are generated, or how they are used. Instead, the OAuth 2.0 specification leaves all these as design choices for integrators. In this paper, we propose a new type of OAuth 2.0 token backed by a distributed ledger. Our construction is secure, and it supports proof-of-possession, auditing, and accountability. Furthermore, we provide added-value token management services, including revocation, delegation, and fair exchange by leveraging smart contracts. We realized a proof-of-concept implementation of our solution using Ethereum smart contracts and the ERC-721 token specification.
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