In order to provide an accurate and timely response to different types of the attacks, intrusion and anomaly detection systems collect and analyze a lot of data that may include personal and other sensitive data. These systems could be considered a source of privacy-aware risks. Application of the federated learning paradigm for training attack and anomaly detection models may significantly decrease such risks as the data generated locally are not transferred to any party, and training is performed mainly locally on data sources. Another benefit of the usage of federated learning for intrusion detection is its ability to support collaboration between entities that could not share their dataset for confidential or other reasons. While this approach is able to overcome the aforementioned challenges it is rather new and not well-researched. The challenges and research questions appear while using it to implement analytical systems. In this paper, the authors review existing solutions for intrusion and anomaly detection based on the federated learning, and study their advantages as well as open challenges still facing them. The paper analyzes the architecture of the proposed intrusion detection systems and the approaches used to model data partition across the clients. The paper ends with discussion and formulation of the open challenges.

Cyberattacks on cyber-physical systems (CPS) can lead to severe consequences, and therefore it is extremely important to detect them at early stages. However, there are several challenges to be solved in this area; they include an ability of the security system to detect previously unknown attacks. This problem could be solved with the system behaviour analysis methods and unsupervised or semi-supervised machine learning techniques. The efficiency of the attack detection system strongly depends on the datasets used to train the machine learning models. As real-world data from CPS systems are mostly not available due to the security requirements of cyber-physical objects, there are several attempts to create such datasets; however, their completeness and validity are questionable. This paper reviews existing approaches to attack and anomaly detection in CPS, with a particular focus on datasets and evaluation metrics used to assess the efficiency of the proposed solutions. The analysis revealed that only two of the three selected datasets are suitable for solving intrusion detection tasks as soon as they are generated using real test beds; in addition, only one of the selected datasets contains both network and sensor data, making it preferable for intrusion detection. Moreover, there are different approaches to evaluate the efficiency of the machine learning techniques, that require more analysis and research. Thus, in future research, the authors aim to develop an approach to anomaly detection for CPS using the selected datasets and to conduct experiments to select the performance metrics.

One of the challenges in the Internet of Things systems is the security of the critical data, for example, data used for intrusion detection. The paper research construction of an intrusion detection system that ensures the confidentiality of critical data at a given level of intrusion detection accuracy. For this goal, federated learning is used to train an intrusion detection model. Federated learning is a computational model for distributed machine learning that allows different collaborating entities to train one global model without sharing data. This paper considers the case when entities have data that are different in attributes. Authors believe that it is a common situation for the critical systems constructed using Internet of Things (IoT) technology, when industrial objects are monitored by different sets of sensors. To evaluate the applicability of the federated learning for this case, the authors developed an approach and an architecture of the intrusion detection system for vertically partitioned data that consider the principles of federated learning and conducted the series of experiments. To model vertically partitioned data, the authors used the Secure Water Treatment (SWaT) data set that describes the functioning of the water treatment facility. The conducted experiments demonstrate that the accuracy of the intrusion detection model trained using federated learning is compared with the accuracy of the intrusion detection model trained using the centralized machine learning model. However, the computational efficiency of the learning and inference process is currently extremely low. It is explained by the application of homomorphic encryption for input data protection from different data owners or data sources. This defines the necessity to elaborate techniques for generating attributes that could model horizontally partitioned data even for the cases when the collaborating entities share datasets that differ in their attributes.

The paper suggests several techniques for computer network risk assessment based on Common Vulnerability Scoring System (CVSS) and attack modeling. Techniques use a set of integrated security metrics and consider input data from security information and event management (SIEM) systems. Risk assessment techniques differ according to the used input data. They allow to get risk assessment considering requirements to the accuracy and efficiency. Input data includes network characteristics, attacks, attacker characteristics, security events and countermeasures. The tool that implements these techniques is presented. Experiments demonstrate operation of the techniques for different security situations.

Security assessment is an important task for operation of modern computer networks. The paper suggests the security assessment technique based on attack graphs which can be implemented in contemporary SIEM systems. It is based on the security metrics taxonomy and different techniques for calculation of security metrics according to the data about current events. Proposed metrics form the basis for security awareness and reflect current security situation, including development of attacks, attacks sources and targets, attackers’ characteristics. The technique suggested is demonstrated on a case study.

The paper considers the issue of countermeasures selection for ongoing computer network attacks. The suggested technique is based on the countermeasure model that was defined on the base of the open standards, the family of interrelated security metrics and the security analysis technique based on attack graphs and service dependencies. The technique was implemented in a security assessment and countermeasure selection system. This technique was validated on case studies. It is applicable for security information and event management systems.