An Evaluation of Machine Learning Methods to Detect Malicious SCADA Communications

Beaver, Justin M.; Hink, Raymond Borges; Buckner, Mark A.

doi:10.1109/icmla.2013.105

Cited by 137 publications

(82 citation statements)

References 17 publications

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“…First, the classification problem is complex as the error rates are high. This has been confirmed previously [36], [37], where the classifiers tend to make mistakes on the rare classes. As for the online learning algorithms evaluated in this experiment, their error rates are approximately 30% in most cases, not differing significantly from those of the batch models reported in [36], [37].…”

Section: Evaluation Of Binary Datasetssupporting

confidence: 85%

Detecting cyberattacks in industrial control systems using online learning algorithms

Shen

Zhao

et al. 2019

Neurocomputing

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Industrial control systems are critical to the operation of industrial facilities, especially for critical infrastructures, such as refineries, power grids, and transportation systems. Similar to other information systems, a significant threat to industrial control systems is the attack from cyberspace-the offensive maneuvers launched by "anonymous" in the digital world that target computer-based assets with the goal of compromising a system's functions or probing for information. Owing to the importance of industrial control systems, and the possibly devastating consequences of being attacked, significant endeavors have been attempted to secure industrial control systems from cyberattacks. Among them are intrusion detection systems that serve as the first line of defense by monitoring and reporting potentially malicious activities. Classical machine-learning-based intrusion detection methods usually generate prediction models by learning modest-sized training samples all at once. Such approach is not always applicable to industrial control systems, as industrial control systems must process continuous control commands with limited computational resources in a nonstop way. To satisfy such requirements, we propose using online learning to learn prediction models from the controlling data stream. We introduce several state-of-the-art online learning algorithms categorically, and illustrate their efficacies on two typically used testbeds-power system and gas pipeline. Further, we explore a new cost-sensitive online learning algorithm to solve the class-imbalance problem that is pervasive in industrial intrusion detection systems. Our experimental results indicate that the proposed algorithm can achieve an overall improvement in the detection rate of cyberattacks in industrial control systems.

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Section: Evaluation Of Binary Datasetssupporting

confidence: 85%

Detecting cyberattacks in industrial control systems using online learning algorithms

Shen

Zhao

et al. 2019

Neurocomputing

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“…The authors have demonstrated the use of classification techniques to predict the faults in advance. Beaver et al [35] compare and evaluate various machine learning algorithms for anomaly detection in SCADA communication channel. Erez and Wool [36] describe a novel domain-aware anomaly detection system that detects irregular changes in Modbus/TCP SCADA control register values.…”

Section: Machine Learning Approaches For Icsmentioning

confidence: 99%

Cybersecurity for industrial control systems: A survey

Bhamare

Zolanvari

Erbad

et al. 2020

Computers & Security

216

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Industrial Control System (ICS) is a general term that includes supervisory control & data acquisition (SCADA) systems, distributed control systems (DCS), and other control system configurations such as programmable logic controllers (PLC). ICSs are often found in the industrial sectors and critical infrastructures, such as nuclear and thermal plants, water treatment facilities, power generation, heavy industries, and distribution systems. Though ICSs were kept isolated from the Internet for so long, significant achievable business benefits are driving a convergence between ICSs and the Internet as well as information technology (IT) environments, such as cloud computing. As a result, ICSs have been exposed to the attack vectors used in the majority of cyber-attacks. However, ICS devices are inherently much less secure against such advanced attack scenarios. A compromise to ICS can lead to enormous physical damage and danger to human lives. In this work, we have a close look at the shift of the ICS from stand-alone systems to cloud-based environments. Then we discuss the major works, from industry and academia towards the development of the secure ICSs, especially applicability of the machine learning techniques for the ICS cyber-security. The work may help to address the challenges of securing industrial processes, particularly while migrating them to the cloud environments.

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“…24,25 However, wireless technologies such as 802.11-based networks, Zigbee, and others are increasingly being deployed in these industrial control systems. This is the case for devices used in industrial control environments.…”

Section: Connectivitymentioning

confidence: 99%

“…This is the case for devices used in industrial control environments. 24,25 However, wireless technologies such as 802.11-based networks, Zigbee, and others are increasingly being deployed in these industrial control systems. Many IoT devices are currently using Zigbee, which has low power consumption (therefore extending battery life).…”

Section: Connectivitymentioning

confidence: 99%

Securing Internet of Things (IoT) with machine learning

Zeadally

Tsikerdekis

2019

Int J Communication

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Advances in hardware, software, communication, embedding computing technologies along with their decreasing costs and increasing performance have led to the emergence of the Internet of Things (IoT) paradigm. Today, several billions of Internet-connected devices are part of the IoT ecosystem.IoT devices have become an integral part of the information and communication technology (ICT) infrastructure that supports many of our daily activities. The security of these IoT devices has been receiving a lot of attention in recent years. Another major recent trend is the amount of data that is being produced every day which has reignited interest in technologies such as machine learning and artificial intelligence. We investigate the potential of machine learning techniques in enhancing the security of IoT devices. We focus on the deployment of supervised, unsupervised learning techniques, and reinforcement learning for both host-based and network-based security solutions in the IoT environment. Finally, we discuss some of the challenges of machine learning techniques that need to be addressed in order to effectively implement and deploy them so that they can better protect IoT devices. KEYWORDSattack, internet of things, machine learning, security

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An Evaluation of Machine Learning Methods to Detect Malicious SCADA Communications

Cited by 137 publications

References 17 publications

Detecting cyberattacks in industrial control systems using online learning algorithms

Detecting cyberattacks in industrial control systems using online learning algorithms

Cybersecurity for industrial control systems: A survey

Securing Internet of Things (IoT) with machine learning

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