Recently, OT (operational technology) networks of industrial control systems have been combined with IT networks. Therefore, OT networks have inherited the vulnerabilities and attack paths existing in IT networks. Consequently, attacks on industrial control systems are increasing, and research on technologies combined with artificial intelligence for detecting attacks is active. Current research focuses on detecting attacks and improving the detection accuracy. Few studies exist on metrics that interpret anomaly detection results. Different analysis metrics are required depending on the characteristics of the industrial control system data used for anomaly detection and the type of attack they contain. We focused on the fact that industrial control system data are time series data. The accuracy and F1-score are used as metrics for interpreting anomaly detection results. However, these metrics are not suitable for evaluating anomaly detection in time series data. Because it is not possible to accurately determine the start and end of an attack, range-based performance metrics must be used. Therefore, in this study, when evaluating anomaly detection performed on time series data, we propose a range-based performance metric with an improved algorithm. The previously studied range-based performance metric time-series aware precision and recall (TaPR) evaluated all attacks equally. In this study, improved performance metrics were studied by deriving ambiguous instances according to the characteristics of each attack and redefining the algorithm of the TaPR metric. This study provides accurate assessments when performing anomaly detection on time series data and allows predictions to be evaluated based on the characteristics of the attack.
Recently, as new threats from attackers are discovered, the damage and scale of these threats are increasing. Vulnerabilities should be identified early, and countermeasures should be implemented to solve this problem. However, there are limitations to applying the vulnerability discovery framework used in practice. Existing frameworks have limitations in terms of the analysis target. If the analysis target is abstract, it cannot be easily applied to the framework. Therefore, this study proposes a framework for vulnerability discovery and countermeasures that can be applied to any analysis target. The proposed framework includes a structural analysis to discover vulnerabilities from a scenario composition, including analysis targets. In addition, a proof of concept is conducted to derive and verify threats that can actually occur through threat modeling. In this study, the open platform communication integrated architecture used in the industrial control system and industrial Internet of Things environment was selected as an analysis target. We find 30 major threats and four vulnerabilities based on the proposed framework. As a result, the validity of malicious client attacks using certificates and DoS attack scenarios using flooding were validated, and we create countermeasures for these vulnerabilities.
With the growing awareness regarding the importance of personal data protection, many countries have established laws and regulations to ensure data privacy and are supervising managements to comply with them. Although various studies have suggested compliance methods of the general data protection regulation (GDPR) for personal data, no method exists that can ensure the reliability and integrity of the personal data processing request records of a data subject to enable its utilization as a GDPR compliance audit proof for an auditor. In this paper, we propose a delegation-based personal data processing request notarization framework for GDPR using a private blockchain. The proposed notarization framework allows the data subject to delegate requests to process of personal data; the framework makes the requests to the data controller, which performs the processing. The generated data processing request and processing result data are stored in the blockchain ledger and notarized via a trusted institution of the blockchain network. The Hypderledger Fabric implementation of the framework demonstrates the fulfillment of system requirements and feasibility of implementing a GDPR compliance audit for the processing of personal data. The analysis results with comparisons among the related works indicate that the proposed framework provides better reliability and feasibility for the GDPR audit of personal data processing request than extant methods.
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