Machine learning for intrusion detection in industrial control systems: Applications, challenges, and recommendations
Methods from machine learning are used in the design of secure Industrial Control Systems.
Such methods focus on two major areas: detection of intrusions at the network level using …
Such methods focus on two major areas: detection of intrusions at the network level using …
Adversarial machine learning attacks against intrusion detection systems: A survey on strategies and defense
Concerns about cybersecurity and attack methods have risen in the information age. Many
techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs) …
techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs) …
Intriguing properties of adversarial ml attacks in the problem space
Recent research efforts on adversarial ML have investigated problem-space attacks,
focusing on the generation of real evasive objects in domains where, unlike images, there is …
focusing on the generation of real evasive objects in domains where, unlike images, there is …
[HTML][HTML] Adversarial attacks on machine learning cybersecurity defences in industrial control systems
The proliferation and application of machine learning-based Intrusion Detection Systems
(IDS) have allowed for more flexibility and efficiency in the automated detection of cyber …
(IDS) have allowed for more flexibility and efficiency in the automated detection of cyber …
[HTML][HTML] Hardening machine learning denial of service (DoS) defences against adversarial attacks in IoT smart home networks
Abstract Machine learning based Intrusion Detection Systems (IDS) allow flexible and
efficient automated detection of cyberattacks in Internet of Things (IoT) networks. However …
efficient automated detection of cyberattacks in Internet of Things (IoT) networks. However …
[HTML][HTML] Apollon: a robust defense system against adversarial machine learning attacks in intrusion detection systems
Abstract The rise of Adversarial Machine Learning (AML) attacks is presenting a significant
challenge to Intrusion Detection Systems (IDS) and their ability to detect threats. To address …
challenge to Intrusion Detection Systems (IDS) and their ability to detect threats. To address …
Adversarial training for deep learning-based cyberattack detection in IoT-based smart city applications
Abstract Intrusion Detection Systems (IDS) based on deep learning models can identify and
mitigate cyberattacks in IoT applications in a resilient and systematic manner. These models …
mitigate cyberattacks in IoT applications in a resilient and systematic manner. These models …
Machine learning for intrusion detection in industrial control systems: challenges and lessons from experimental evaluation
Gradual increase in the number of successful attacks against Industrial Control Systems
(ICS) has led to an urgent need to create defense mechanisms for accurate and timely …
(ICS) has led to an urgent need to create defense mechanisms for accurate and timely …
Challenges in machine learning based approaches for real-time anomaly detection in industrial control systems
Data-centric approaches are becoming increasingly common in the creation of defense
mechanisms for critical infrastructure such as the electric power grid and water treatment …
mechanisms for critical infrastructure such as the electric power grid and water treatment …
Evasion Attack and Defense On Machine Learning Models in Cyber-Physical Systems: A Survey
Cyber-physical systems (CPS) are increasingly relying on machine learning (ML)
techniques to reduce labor costs and improve efficiency. However, the adoption of ML also …
techniques to reduce labor costs and improve efficiency. However, the adoption of ML also …