Evaluasi Algoritma LSTM dan Algoritma Validasi Sekuensi ID Untuk Mendeteksi Serangan Pada Protokol Komunikasi Modbus TCP/IP Dalam SCADA

Pesatnya perkembangan IoT terutama dengan penerapan teknologi 5G, SCADA menjadi protokol yang semakin banyak diminta yang dulunya hanya dikembangkan di lingkungan yang hampir tidak memerlukan dan menerapkan keamanan, menjadi target utama serangan cyber. Oleh karena itu, implementasi Intrusion Detection System (IDS) yang tepat menjadi penting. Diperlukan metode yang dapat mendeteksi penyusup dalam sistem. Metode neural network menjadi metode yang cukup terkenal dalam pendeteksi penyusup dan memiliki hasil yang baik tetapi, sifat neural network yang rumit dan memakan banyak waktu untuk melatih. Metode lainnya adalah metode validasi sekuensi ID yang sebelumnya diusulkan untuk CanBus. Kedua metode ini dievaluasi dalam penelitian ini dan ditemukan bahwa LSTM lebih unggul dengan akurasi 99,7%, presisi 99.74%, recal 99.7%, dan F1-Score 99,69

A review of research works on supervised learning algorithms for SCADA intrusion detection and classification

Abstract: Supervisory Control and Data Acquisition (SCADA) systems play a significant role in providing remote access, monitoring and control of critical infrastructures (CIs) which includes electrical power systems, water distribution systems, nuclear power plants, etc. The growing interconnectivity, standardization of communication protocols and remote accessibility of modern SCADA systems have contributed massively to the exposure of SCADA systems and CIs to various forms of security challenges. Any form of intrusive action on the SCADA modules and communication networks can create devastating consequences on nations due to their strategic importance to CIs’ operations. Therefore, the prompt and efficient detection and classification of SCADA systems intrusions hold great importance for national CIs operational stability. Due to their well-recognized and documented efficiencies, several literature works have proposed numerous supervised learning techniques for SCADA intrusion detection and classification (IDC). This paper presents a critical review of recent studies whereby supervised learning techniques were modelled for SCADA intrusion solutions. The paper aims to contribute to the state-of-the-art, recognize critical open issues and offer ideas for future studies. The intention is to provide a research-based resource for researchers working on industrial control systems security. The analysis and comparison of different supervised learning techniques for SCADA IDC systems were critically reviewed, in terms of the methodologies, datasets and testbeds used, feature engineering and optimization mechanisms and classification procedures. Finally, we briefly summarized some suggestions and recommendations for future research works

Anomalous behaviour detection for cyber defence in modern industrial control systems

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.The fusion of pervasive internet connectivity and emerging technologies in smart cities creates fragile cyber-physical-natural ecosystems. Industrial Control Systems (ICS) are intrinsic parts of smart cities and critical to modern societies. Not designed for interconnectivity or security, disruptor technologies enable ubiquitous computing in modern ICS. Aided by artificial intelligence and the industrial internet of things they transform the ICS environment towards better automation, process control and monitoring. However, investigations reveal that leveraging disruptive technologies in ICS creates security challenges exposing critical infrastructure to sophisticated threat actors including increasingly hostile, well-organised cybercrimes and Advanced Persistent Threats. Besides external factors, the prevalence of insider threats includes malicious intent, accidental hazards and professional errors. The sensing capabilities create opportunities to capture various data types. Apart from operational use, this data combined with artificial intelligence can be innovatively utilised to model anomalous behaviour as part of defence-in-depth strategies. As such, this research aims to investigate and develop a security mechanism to improve cyber defence in ICS. Firstly, this thesis contributes a Systematic Literature Review (SLR), which helps analyse frameworks and systems that address CPS’ cyber resilience and digital forensic incident response in smart cities. The SLR uncovers emerging themes and concludes several key findings. For example, the chronological analysis reveals key influencing factors, whereas the data source analysis points to a lack of real CPS datasets with prevalent utilisation of software and infrastructure-based simulations. Further in-depth analysis shows that cross-sector proposals or applications to improve digital forensics focusing on cyber resilience are addressed by a small number of research studies in some smart sectors. Next, this research introduces a novel super learner ensemble anomaly detection and cyber risk quantification framework to profile anomalous behaviour in ICS and derive a cyber risk score. The proposed framework and associated learning models are experimentally validated. The produced results are promising and achieve an overall F1-score of 99.13%, and an anomalous recall score of 99% detecting anomalies lasting only 17 seconds ranging from 0.5% to 89% of the dataset. Further, a one-class classification model is developed, leveraging stream rebalancing followed by adaptive machine learning algorithms and drift detection methods. The model is experimentally validated producing promising results including an overall Matthews Correlation Coefficient (MCC) score of 0.999 and the Cohen’s Kappa (K) score of 0.9986 on limited variable single-type anomalous behaviour per data stream. Wide data streams achieve an MCC score of 0.981 and a K score of 0.9808 in the prevalence of multiple types of anomalous instances. Additionally, the thesis scrutinises the applicability of the learning models to support digital forensic readiness. The research study presents the concept of digital witness and digital chain of custody in ICS. Following that, a use case integrating blockchain technologies into the design of ICS to support digital forensic readiness is discussed. In conclusion, the contributions of this research thesis help towards developing the next generation of state-of-the-art methods for anomalous behaviour detection in ICS defence-in-depth