IoTSecSim: A framework for modelling and simulation of security in Internet of things

Chee, Kok Onn; Ge, Mengmeng; Bai, Guangdong; Kim, Dan Dongseong

doi:10.1016/j.cose.2023.103534

Cited by 5 publications

(5 citation statements)

References 25 publications

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“…The paper [44] proposed an innovative framework, namely the IoT Security Simulator (IoTSecSim). Graphical security modeling (GSM) was utilized to create IoTSecSim.…”

Section: Other Methodsmentioning

confidence: 99%

“…The exploration of diverse methods in cybersecurity for IoT and IoMT devices, as described in recent studies, unveils several research gaps that require attention for enhanced security solutions. The study by Chee et al [44] introduces IoTSecSim, a security simulator utilizing graphical security modeling (GSM) to simulate cyberattacks and defenses. Despite its capabilities, the simulator lacks real-time simulation for packet flows, which is critical for dynamic network environments.…”

Section: Other Methodsmentioning

confidence: 99%

“…To address the limitations of IoTSecSim noted in Chee et al's study [44], incorporating real-time data processing capabilities could significantly enhance its utility. Integrating more advanced real-time simulation engines and developing capabilities to monitor live network traffic could provide more accurate and timely insights into network security vulnerabilities.…”

Section: Other Methodsmentioning

confidence: 99%

“…This can assist in implementing robust security strategies in the IoT network. The work in [44,45] considers graph-based approaches.…”

Section: Other Methodsmentioning

confidence: 99%

See 3 more Smart Citations

Navigating the Cyber Threat Landscape: An In-Depth Analysis of Attack Detection within IoT Ecosystems

AboulEla,

Ibrahim,

Shehmir

et al. 2024

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The Internet of Things (IoT) is seeing significant growth, as the quantity of interconnected devices in communication networks is on the rise. The increased connectivity of devices has heightened their susceptibility to hackers, underscoring the need to safeguard IoT devices. This research investigates cybersecurity in the context of the Internet of Medical Things (IoMT), which encompasses the cybersecurity mechanisms used for various healthcare devices connected to the system. This study seeks to provide a concise overview of several artificial intelligence (AI)-based methodologies and techniques, as well as examining the associated solution approaches used in cybersecurity for healthcare systems. The analyzed methodologies are further categorized into four groups: machine learning (ML) techniques, deep learning (DL) techniques, a combination of ML and DL techniques, Transformer-based techniques, and other state-of-the-art techniques, including graph-based methods and blockchain methods. In addition, this article presents a detailed description of the benchmark datasets that are recommended for use in intrusion detection systems (IDS) for both IoT and IoMT networks. Moreover, a detailed description of the primary evaluation metrics used in the analysis of the discussed models is provided. Ultimately, this study thoroughly examines and analyzes the features and practicality of several cybersecurity models, while also emphasizing recent research directions.

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“…The paper [44] proposed an innovative framework, namely the IoT Security Simulator (IoTSecSim). Graphical security modeling (GSM) was utilized to create IoTSecSim.…”

Section: Other Methodsmentioning

confidence: 99%

Section: Other Methodsmentioning

confidence: 99%

Section: Other Methodsmentioning

confidence: 99%

“…This can assist in implementing robust security strategies in the IoT network. The work in [44,45] considers graph-based approaches.…”

Section: Other Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Navigating the Cyber Threat Landscape: An In-Depth Analysis of Attack Detection within IoT Ecosystems

AboulEla,

Ibrahim,

Shehmir

et al. 2024

View full text Add to dashboard Cite

show abstract

“…These IoT devices are prevalent in diverse domains, including health care, smart cities, energy, smart homes, and industry. However, the IoT’s rapid development has resulted in potential areas of attack and created attractive targets in the form of weaknesses or vulnerabilities in the system [ 2 , 3 , 4 ]. Breaches in the security of IoT devices and networks can lead to disruption of system infrastructure, privacy violations, and other issues.…”

Section: Introductionmentioning

confidence: 99%

Effective Feature Engineering Framework for Securing MQTT Protocol in IoT Environments

Al Hanif,

Ilyas

2024

Sensors

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The explosive growth of the domain of the Internet of things (IoT) network devices has resulted in unparalleled ease of productivity, convenience, and automation, with Message Queuing Telemetry Transport (MQTT) protocol being widely recognized as an essential communication standard in IoT environments. MQTT enables fast and lightweight communication between IoT devices to facilitate data exchange, but this flexibility also exposes MQTT to significant security vulnerabilities and challenges that demand highly robust security. This paper aims to enhance the detection efficiency of an MQTT traffic intrusion detection system (IDS). Our proposed approach includes the development of a binary balanced MQTT dataset with an effective feature engineering and machine learning framework to enhance the security of MQTT traffic. Our feature selection analysis and comparison demonstrates that selecting a 10-feature model provides the highest effectiveness, as it shows significant advantages in terms of constant accuracy and superior training and testing times across all models. The results of this study show that the framework has the capability to enhance the efficiency of an IDS for MQTT traffic, with more than 96% accuracy, precision, recall, F1-score, and ROC, and it outperformed the most recent study that used the same dataset.

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Combating temporal composition inference by high-order camouflaged network topology obfuscation

Li,

Yang,

Huang

et al. 2024

Computers & Security

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IoTSecSim: A framework for modelling and simulation of security in Internet of things

Cited by 5 publications

References 25 publications

Navigating the Cyber Threat Landscape: An In-Depth Analysis of Attack Detection within IoT Ecosystems

Navigating the Cyber Threat Landscape: An In-Depth Analysis of Attack Detection within IoT Ecosystems

Effective Feature Engineering Framework for Securing MQTT Protocol in IoT Environments

Combating temporal composition inference by high-order camouflaged network topology obfuscation

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