USMD: UnSupervised Misbehaviour Detection for Multi-Sensor Data

Alsaedi, Abdullah; Tari, Zahir; Mahmud, Redowan; Moustafa, Nour; Mahmood, Abdun Naser; Anwar, Adnan

doi:10.1109/tdsc.2022.3143493

Cited by 19 publications

(3 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Long Short-Term Memory (LSTM) is another promising ML algorithm for intrusion detection. Alsaedi et al [144] propose a novel framework for detection of malicious behavior in CPS environments through a framework that uses Deep Neural Networks (DNN) to train a ML model to recognize the expected behavior of a complex multi-sensor industrial network. LSTM is used to model temporal sequences from time-series sensor data to capture long-term dependencies and is combined with a novel method of applying different weighting values to focus on the most relevant characteristics in the complex dataset, which improves prediction accuracy by reducing noise in complex datasets.…”

Section: Using Ai/ml For Anomaly Detectionmentioning

confidence: 99%

“…Perez et al [144] explore the ramifications of continued use of legacy protocols that were designed in the era of fully air-gapped CPS environments and how they can be securely operated in the age of ubiquitous connectivity. Many risks can be mitigated through network segmentation, as described by the Purdue model [233], but the continued use of legacy protocols that operate without encryption or replay resistance against false data injection attacks will leave CPS vulnerable to exploitation.…”

Section: Trusted Systems Vs Zero-trust Architecturementioning

confidence: 99%

See 1 more Smart Citation

A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems

Jeffrey,

Tan,

Villar

2023

Electronics

View full text Add to dashboard Cite

Cyber-Physical Systems (CPS) are integrated systems that combine software and physical components. CPS has experienced rapid growth over the past decade in fields as disparate as telemedicine, smart manufacturing, autonomous vehicles, the Internet of Things, industrial control systems, smart power grids, remote laboratory environments, and many more. With the widespread integration of Cyber-Physical Systems (CPS) in various aspects of contemporary society, the frequency of malicious assaults carried out by adversaries has experienced a substantial surge in recent times. Incidents targeting vital civilian infrastructure, such as electrical power grids and oil pipelines, have become alarmingly common due to the expanded connectivity to the public internet, which significantly expands the vulnerability of CPS. This article presents a comprehensive review of existing literature that examines the latest advancements in anomaly detection techniques for identifying security threats in Cyber-Physical Systems. The primary emphasis is placed on addressing life safety concerns within industrial control networks (ICS). A total of 296 papers are reviewed, with common themes and research gaps identified. This paper makes a novel contribution by identifying the key challenges that remain in the field, which include resource constraints, a lack of standardized communication protocols, extreme heterogeneity that hampers industry consensus, and different information security priorities between Operational Technology (OT) and Information Technology (IT) networks. Potential solutions and/or opportunities for further research are identified to address these selected challenges.

show abstract

Section: Using Ai/ml For Anomaly Detectionmentioning

confidence: 99%

Section: Trusted Systems Vs Zero-trust Architecturementioning

confidence: 99%

A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems

Jeffrey,

Tan,

Villar

2023

Electronics

View full text Add to dashboard Cite

show abstract

“…Furthermore, traditional machine learning (TML) algorithms have advanced significantly, and some of their variants have been successfully applied to solve classification tasks related to intrusion detection [12], [13]. Also, the progression of deep learning (DL) and its great success in different fields has guided it as a potential solution for network intrusion detection [12].…”

Section: Introductionmentioning

confidence: 99%

Dependable Intrusion Detection System for IoT: A Deep Transfer Learning-based Approach

Mehedi,

Anwar,

Rahman

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Security concerns for IoT applications have been alarming because of their widespread use in different enterprise systems. The potential threats to these applications are constantly emerging and changing, and therefore, sophisticated and dependable defense solutions are necessary against such threats. With the rapid development of IoT networks and evolving threat types, the traditional machine learning-based IDS must update to cope with the security requirements of the current sustainable IoT environment. In recent years, deep learning, and deep transfer learning have progressed and experienced great success in different fields and have emerged as a potential solution for dependable network intrusion detection. However, new and emerging challenges have arisen related to the accuracy, efficiency, scalability, and dependability of the traditional IDS in a heterogeneous IoT setup. This manuscript proposes a deep transfer learning-based dependable IDS model that outperforms several existing approaches. The unique contributions include effective attribute selection, which is best suited to identify normal and attack scenarios for a small amount of labeled data, designing a dependable deep transfer learning-based ResNet model, and evaluating considering real-world data. To this end, a comprehensive experimental performance evaluation has been conducted. Extensive analysis and performance evaluation show that the proposed model is robust, more efficient, and has demonstrated better performance, ensuring dependability.

show abstract

Amplification methods to promote the attacks against machine learning-based intrusion detection systems

Zhang,

Xu,

Zhang

et al. 2024

Appl Intell

View full text Add to dashboard Cite

USMD: UnSupervised Misbehaviour Detection for Multi-Sensor Data

Cited by 19 publications

References 45 publications

A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems

A Review of Anomaly Detection Strategies to Detect Threats to Cyber-Physical Systems

Dependable Intrusion Detection System for IoT: A Deep Transfer Learning-based Approach

Amplification methods to promote the attacks against machine learning-based intrusion detection systems

Contact Info

Product

Resources

About