Biblio
The power industrial control system is an important part of the national critical Information infrastructure. Its security is related to the national strategic security and has become an important target of cyber attacks. In order to solve the problem that the vulnerability detection technology of power industrial control system cannot meet the requirement of non-destructive, this paper proposes an industrial control vulnerability analysis technology combined with dynamic and static analysis technology. On this basis, an industrial control non-destructive vulnerability detection system is designed, and a simulation verification platform is built to verify the effectiveness of the industrial control non-destructive vulnerability detection system. These provide technical support for the safety protection research of the power industrial control system.
ISSN: 2693-289X
Digitization has pioneered to drive exceptional changes across all industries in the advancement of analytics, automation, and Artificial Intelligence (AI) and Machine Learning (ML). However, new business requirements associated with the efficiency benefits of digitalization are forcing increased connectivity between IT and OT networks, thereby increasing the attack surface and hence the cyber risk. Cyber threats are on the rise and securing industrial networks are challenging with the shortage of human resource in OT field, with more inclination to IT/OT convergence and the attackers deploy various hi-tech methods to intrude the control systems nowadays. We have developed an innovative real-time ICS cyber test kit to obtain the OT industrial network traffic data with various industrial attack vectors. In this paper, we have introduced the industrial datasets generated from ICS test kit, which incorporate the cyber-physical system of industrial operations. These datasets with a normal baseline along with different industrial hacking scenarios are analyzed for research purposes. Metadata is obtained from Deep packet inspection (DPI) of flow properties of network packets. DPI analysis provides more visibility into the contents of OT traffic based on communication protocols. The advancement in technology has led to the utilization of machine learning/artificial intelligence capability in IDS ICS SCADA. The industrial datasets are pre-processed, profiled and the abnormality is analyzed with DPI. The processed metadata is normalized for the easiness of algorithm analysis and modelled with machine learning-based latest deep learning ensemble LSTM algorithms for anomaly detection. The deep learning approach has been used nowadays for enhanced OT IDS performances.
With the continuous emergence of cyber attacks, the security of industrial control system (ICS) has become a hot issue in academia and industry. Intrusion detection technology plays an irreplaceable role in protecting industrial system from attacks. However, the imbalance between normal samples and attack samples seriously affects the performance of intrusion detection algorithms. This paper proposes SE-IDS, which uses generative adversarial networks (GAN) to expand the minority to make the number of normal samples and attack samples relatively balanced, adopts particle swarm optimization (PSO) to optimize the parameters of LightGBM. Finally, we evaluated the performance of the proposed model on the industrial network dataset.
The ability to advance the state of the art in automated cybersecurity protections for industrial control systems (ICS) has as a prerequisite of understanding the trade-off space. That is, to enable a cyber feedback loop in a control system environment you must first consider both the security mitigation available, the benefits and the impacts to the control system functionality when the mitigation is used. More damaging impacts could be precipitated that the mitigation was intended to rectify. This paper details networked ICS that controls a simulation of the frequency response represented with the swing equation. The microgrid loads and base generation can be balanced through the control of an emulated battery and power inverter. The simulated plant, which is implemented in Raspberry Pi computers, provides an inexpensive platform to realize the physical effects of cyber attacks to show the trade-offs of available mitigating actions. This network design can include a commercial ICS controller and simple plant or emulated plant to introduce real world implementation of feedback controls, and provides a scalable, physical effects measurable microgrid for cyber resilience analysis (SPEMMCRA).