Anomaly-Based Intrusion Detection System for Cyber-Physical System Security

Colelli, Riccardo; Magri, Filippo; Panzieri, Stefano; Pascucci, Federica

doi:10.1109/med51440.2021.9480182

Cited by 13 publications

(9 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are various application areas where efforts are being made to address security and privacy issues in CPSs. In the field of Industrial Control Systems, studies have focused on addressing data theft and intrusion attacks [12,13]. Solutions such as secure data containers and Intrusion Detection Systems (IDS) have been proposed to safeguard data integrity and monitor system behavior.…”

Section: Security and Privacymentioning

confidence: 99%

“…They found that SVM+ outperformed the other algorithms in terms of F1-score, although the dataset used was imbalanced in nature. Colelli et al [13] developed a machine learning tool that detects cyberattacks in cyber-physical systems to improve their security. They evaluated the performance of three models in classifying normal and anomalous behavior in a water tank system to identify attacks and prevent hazardous conditions.…”

Section: Network-based Threatsmentioning

confidence: 99%

“…Deep Learning [15,27,42] Supervised Learning K-Means Clustering [41] Gaussian Mixture [42] Support Vector Machine [12,15,18,21,40] Classification Naive Bayes [21,24] Decision Tree [18,21] Random Forest [12,13,21] K-Nearest Neighbors [12,15,18,21] AI is a computer science discipline that enables machines to think and behave like humans using methods such as machine learning, deep learning, game theory, optimization theory, and evolutionary algorithms. Bayesian filtering [24] and robotic automation [39] are being used to protect sensitive data CPSs.…”

Section: Artificial Intelligencementioning

confidence: 99%

“…addressing security and privacy issues in CPS, several AI techniques are employed, with supervised machine learning algorithms being the most prevalent, particularly classification algorithm [12,13,15,18,21,24,40]. The classification ML algorithms can be used to train models that can classify data as normal or anomalous.…”

Section: Artificial Intelligencementioning

confidence: 99%

See 3 more Smart Citations

A Taxonomy of AI Techniques for Security and Privacy in Cyber-Physical Systems

Bandi¹

2023

Preprint

View full text Add to dashboard Cite

Background:As cyber-physical systems (CPS) continue to grow, it is progressively crucial to address the challenges of data protection using artificial intelligence (AI). Objective:The goal of this research is to provide an up-to-date overview of the security and privacy issues of CPS that incorporate AI techniques. Method:To achieve this, the author conducted a systematic literature review, focusing on 35 relevant articles. Results:The data collected from these studies was then categorized into three main areas: 1) different security and privacy issues, 2) application areas and vulnerabilities, and 3) the AI techniques used to address security and privacy concerns. The literature review highlights that intrusion detection and cyberattacks are the most commonly studied areas in CPS, while Machine Learnning (ML)-based attacks and vessel trajectory are less explored. The review identifies various CPS applications such as water treatment, energy, healthcare, and transportation that address security and privacy concerns. However, a relatively small proportion of studies focused on the manufacturing domain. The review also notes that while supervised machine learning algorithms under the classification category are commonly used to address data protection issues, there are comparatively fewer studies that have implemented automation processes using robots and deep learning. Limitations:The articles related to blockchain-based research were not included in this review to focus solely on AI techniques. Conclusion:The results of this analysis indicate that there is a significant need for innovative AI/ML techniques to protect intelligent systems and networks from ML-based security threats.

show abstract

Section: Security and Privacymentioning

confidence: 99%

Section: Network-based Threatsmentioning

confidence: 99%

Section: Artificial Intelligencementioning

confidence: 99%

Section: Artificial Intelligencementioning

confidence: 99%

See 2 more Smart Citations

A Taxonomy of AI Techniques for Security and Privacy in Cyber-Physical Systems

Bandi¹

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Within computer science, Machine learning (ML) examines algorithms and systems capable of data synthesis (Colelli et al, 2021). It is employed in cyber-physical security to alleviate concerns regarding safety and reliability, and it is closely related to arti cial intelligence and data mining (Shin et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Advancements in Anomaly Detection: A Review of Machine Learning Applications in Cyber-Physical System Networks

Ahmed

2024

Preprint

View full text Add to dashboard Cite

In the practical world, Cyber-Physical Systems have integrated physical systems and software management in the cyber-world, with networks responsible for information interchange. CPSs are key technologies for various industrial domains, including intelligent medical systems, transport systems, and smart grids. The advancements in cybersecurity have surpassed the rapid growth of CPS, with new security challenges and threat models that lack an integrated and cohesive framework. The review methodology includes the search strategy along with the inclusion and exclusion criteria of fifteen studies conducted in the past ten years. The studies specific to the relevant topic have been added, while the others have been excluded. According to the results, Machine Learning (ML) algorithms and systems can synthesize data. It is employed in cyber-physical security to alleviate concerns regarding the safety and reliability of the findings. ML offers a solution to complex problems, enhancing computer-human interaction and enabling problem-solving in areas where custom-built algorithms are impractical. A comprehensive overview of the application of ML across various domains, such as smart grids, smart vehicles, healthcare systems, and environmental monitoring, has been included. However, a few challenges are associated with implementing ML techniques in CPS networks, including feature selection complexity, model performance, deployment challenges, algorithm biases, model mismatches, and the need to foster a robust safety culture. Overall, integrating ML techniques with CPS networks holds promise for enhancing system safety, reliability, and security but requires ongoing refinement and adaptation to address existing limitations and emerging threats.

show abstract

Intrusion Detection for Cyber Physical Systems Using Light Gradient Boost Model

Latha,

Bommi

2024

Communications in Computer and Information Science

View full text Add to dashboard Cite

Anomaly-Based Intrusion Detection System for Cyber-Physical System Security

Cited by 13 publications

References 13 publications

A Taxonomy of AI Techniques for Security and Privacy in Cyber-Physical Systems

A Taxonomy of AI Techniques for Security and Privacy in Cyber-Physical Systems

Advancements in Anomaly Detection: A Review of Machine Learning Applications in Cyber-Physical System Networks

Intrusion Detection for Cyber Physical Systems Using Light Gradient Boost Model

Contact Info

Product

Resources

About