Countermeasure of Attack Vectors using Signature-Based IDS in IoT Environments

Sheikh, Taj Uddin; Rahman, Hasina; Alqahtani, Hamed; Hazra, Tapan Kumar; Sheikh, Nazim Uddin

doi:10.1109/iemcon.2019.8936231

Cited by 9 publications

(5 citation statements)

References 7 publications

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“…In another categorization performed depending on the type of intrusions, the IDSs are categorised into four, namely, IDS based on signature, IDS based on anomaly [18], IDS based on specifcation [19,20], and hybrid approachbased IDS [21]. In IDS based on signatures [22], for each attack to be detected, it describes the attack pattern. In this scheme, a trigger message is raised when the attack matches the described pattern.…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey on Machine Learning‐Based Intrusion Detection Systems for Secure Communication in Internet of Things

Kumar

Selvi

Kannan

2023

Computational Intelligence and Neuroscience

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The Internet of Things (IoT) is a distributed system which is made up of the connections of smart objects (things) that can continuously sense the events in their sensing domain and transmit the data via the Internet. IoT is considered as the next revolution of the Internet since it has provided vast improvements in day-to-day activities of humans including the provision of efficient healthcare services and development of smart cities and intelligent transport systems. The IoT environment, by the application of suitable security mechanisms through efficient security management techniques, intrusion detection systems provide a wall of defence against the attacks on the Internet and on the devices connected with Internet by effective monitoring of the Internet traffic. Therefore, the intrusion detection system (IDS) is a resolution proposed by the researchers to monitor and secure the IoT communication. In this work, a meticulous analysis of the security of IoT networks based on quality-of-service metrics is performed for deploying intrusion detection systems by carrying out experiments on secured communication and measuring the network’s performance based on comparing them with the existing security metrics. Finally, we propose a new and effective IDS using a deep learning-based classification approach, namely, fuzzy CNN, for improving the security of communication. The major and foremost advantages of this system include an upsurge in detection accuracy, the accurate detection of denial of service (DoS) attacks more efficiently, and the reduction of false positive rates.

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Section: Introductionmentioning

confidence: 99%

A Comprehensive Survey on Machine Learning‐Based Intrusion Detection Systems for Secure Communication in Internet of Things

Kumar

Selvi

Kannan

2023

Computational Intelligence and Neuroscience

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“…It is well known for its flexibility to customize feature. However, it has some limitations such as the difficulty of deployment [35].…”

Section: Zeek (Formally "Bro")mentioning

confidence: 99%

Importance of Machine Learning Techniques to Improve the Open Source Intrusion Detection Systems

Vadhil

Nanne

Salihi

2021

IJEEI

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Nowadays, it became difficult to ensure data security because of the rapid development of information technology according to the Vs of Big Data. To secure a network against malicious activities and to ensure data protection, an intrusion detection system played a very important role. The main objective was to obtain a high-performance solution capable of detecting different types of attacks around the system. The main aim of this paper is to study the lacks of traditional and open source Intrusion Detection Systems and the Machine Learning techniques commonly used to overcome these lacks. A comparison of some existing works by Intrusion Detection System type, detection method, algorithm and accuracy was provided.

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“…It can be either host-based (HIDS) or network-based (NIDS) and is integral for IoT systems as it can detect variations in sensor readings, network abnormality, and so on [ 24 , 25 ]. Intrusion detection systems are categorized into three types: signature-based, anomaly-based, and stateful protocol [ 26 , 27 ]. Additionally, IDS methods can be implemented in three ways: supervised, unsupervised, or semi-supervised, which can be implemented through AI, statistical modeling, and so on [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and Deep Learning Techniques for Internet of Things Network Anomaly Detection—Current Research Trends

Rafique,

Abdallah,

Musa

et al. 2024

Sensors

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With its exponential growth, the Internet of Things (IoT) has produced unprecedented levels of connectivity and data. Anomaly detection is a security feature that identifies instances in which system behavior deviates from the expected norm, facilitating the prompt identification and resolution of anomalies. When AI and the IoT are combined, anomaly detection becomes more effective, enhancing the reliability, efficacy, and integrity of IoT systems. AI-based anomaly detection systems are capable of identifying a wide range of threats in IoT environments, including brute force, buffer overflow, injection, replay attacks, DDoS assault, SQL injection, and back-door exploits. Intelligent Intrusion Detection Systems (IDSs) are imperative in IoT devices, which help detect anomalies or intrusions in a network, as the IoT is increasingly employed in several industries but possesses a large attack surface which presents more entry points for attackers. This study reviews the literature on anomaly detection in IoT infrastructure using machine learning and deep learning. This paper discusses the challenges in detecting intrusions and anomalies in IoT systems, highlighting the increasing number of attacks. It reviews recent work on machine learning and deep-learning anomaly detection schemes for IoT networks, summarizing the available literature. From this survey, it is concluded that further development of current systems is needed by using varied datasets, real-time testing, and making the systems scalable.

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Countermeasure of Attack Vectors using Signature-Based IDS in IoT Environments

Cited by 9 publications

References 7 publications

A Comprehensive Survey on Machine Learning‐Based Intrusion Detection Systems for Secure Communication in Internet of Things

A Comprehensive Survey on Machine Learning‐Based Intrusion Detection Systems for Secure Communication in Internet of Things

Importance of Machine Learning Techniques to Improve the Open Source Intrusion Detection Systems

Machine Learning and Deep Learning Techniques for Internet of Things Network Anomaly Detection—Current Research Trends

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