A Specification-Based Intrusion Prevention System for Malicious Payloads

Werth, Aaron; Morris, Thomas H.

doi:10.1007/978-3-030-31239-8_13

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…Nikhil et al [86] propose an integrated technique for prediction and prevention in agriculture sector with smart connected devices. The experiment conducted on the real- [88]. Serial connectivity of the network is the character of a prevention system; this may lead to po-tential and communication issues.…”

Section: A Ml-based Prevention Models For Iotmentioning

confidence: 99%

Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey

et al. 2022

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The increasing number of connected devices in the era of Internet of Thing (IoT) has also increased the number intrusions. Intrusion Detection System (IDS) is a secondary intelligent system to monitor, detect, and alert about malicious activities; an Intrusion Prevention System (IPS) is an extension of a detection system that triggers relevant action when an attack is suspected in a futuristic aspect. Both IDS and IPS systems are significant and useful for developing a security model. Several studies exist to review the detection and prevention models; however, the coherence in the opportunistic or advancements in the models is missing. Besides, the existing models also have some limitations, which need to be surveyed to develop new security models. Our survey is the first one to present a study of risk factor analysis using mapping technique, and provide a proposal for hybrid framework for an efficient security model for intrusion detection and/or prevention. We explore the importance of various Artificial Intelligence (AI)-based techniques, tools, and methods used for the detection and/or prevention systems in IoTs. More specifically, we emphasize on Machine Learning (ML) and Deep Learning (DL) techniques for intrusion detection-prevention systems and provide a comparative analysis focusing on the feasibility, compatibility, challenges, and real-time issues. This present survey is beneficial for industry and academia to categorize the challenges and issues in the current security models and generate the new dimensions of developments of security frameworks with efficient ML or DL methods.

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Section: A Ml-based Prevention Models For Iotmentioning

confidence: 99%

Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey

et al. 2022

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“…The related disadvantages are the computational load that needs for the protocol state tracing and analysis and their inability to inspect attacks based on the same behavior of the normal network activities. An example of this category of IDSs can be found in [60], where the protocol packets are examined to detect malicious payloads.…”

Section: Intrusion Detection Systemsmentioning

confidence: 99%

A Local Feature Engineering Strategy to Improve Network Anomaly Detection

et al. 2020

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The dramatic increase in devices and services that has characterized modern societies in recent decades, boosted by the exponential growth of ever faster network connections and the predominant use of wireless connection technologies, has materialized a very crucial challenge in terms of security. The anomaly-based intrusion detection systems, which for a long time have represented some of the most efficient solutions to detect intrusion attempts on a network, have to face this new and more complicated scenario. Well-known problems, such as the difficulty of distinguishing legitimate activities from illegitimate ones due to their similar characteristics and their high degree of heterogeneity, today have become even more complex, considering the increase in the network activity. After providing an extensive overview of the scenario under consideration, this work proposes a Local Feature Engineering (LFE) strategy aimed to face such problems through the adoption of a data preprocessing strategy that reduces the number of possible network event patterns, increasing at the same time their characterization. Unlike the canonical feature engineering approaches, which take into account the entire dataset, it operates locally in the feature space of each single event. The experiments conducted on real-world data showed that this strategy, which is based on the introduction of new features and the discretization of their values, improves the performance of the canonical state-of-the-art solutions.

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Prototyping PLCs and IoT Devices in an HVAC Virtual Testbed to Study Impacts of Cyberattacks

Werth

Morris

2020

Proceedings of Fifth International Congress on Information and Communication Technology

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A Specification-Based Intrusion Prevention System for Malicious Payloads

Cited by 4 publications

References 18 publications

Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey

Machine and Deep Learning Solutions for Intrusion Detection and Prevention in IoTs: A Survey

A Local Feature Engineering Strategy to Improve Network Anomaly Detection

Prototyping PLCs and IoT Devices in an HVAC Virtual Testbed to Study Impacts of Cyberattacks

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