Cyber Threat Intelligence for IoT Using Machine Learning

Mishra, Shailendra; J.Albarakati, Aiman; Sharma, Sunil Kumar

doi:10.3390/pr10122673

Cited by 14 publications

(11 citation statements)

References 33 publications

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“…In a study by Mishra et al [ 31 ], anomalies in IoT networks were detected using message queuing telemetry transport (MQTT) and machine-learning algorithms, with a dataset of 4998 records and 34 features. Among the various classifiers employed, the random forest classifier demonstrated the highest level of accuracy at 99.94% [ 31 ].…”

Section: Resultsmentioning

confidence: 99%

“…Human identification of cyber threats is limited due to the cognitive limitations of humans., As highlighted in the literature [ 12 , 25 , 27 , 31 , 45 ], machine-learning and artificial intelligence tools can help to identify malicious network traffic. Therefore, the proposed detection models’ second layer includes applications that include these technologies for signature-based, anomaly-based, and behavior-based detection.…”

Section: Discussionmentioning

confidence: 99%

“…Community-sourced threat intelligence and open-source intelligence [31] This paper focuses on potential network attack identification, and countermeasures are recommended utilizing simulated data. Anomalies in IoT networks are detected using message queuing telemetry transport (MQTT) and machine-learning algorithms.…”

Section: Cti Systems Modelingmentioning

confidence: 99%

See 2 more Smart Citations

A Systematic Literature Review on Cyber Threat Intelligence for Organizational Cybersecurity Resilience

Saeed,

Suayyid,

Al-Ghamdi

et al. 2023

Sensors

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Cybersecurity is a significant concern for businesses worldwide, as cybercriminals target business data and system resources. Cyber threat intelligence (CTI) enhances organizational cybersecurity resilience by obtaining, processing, evaluating, and disseminating information about potential risks and opportunities inside the cyber domain. This research investigates how companies can employ CTI to improve their precautionary measures against security breaches. The study follows a systematic review methodology, including selecting primary studies based on specific criteria and quality valuation of the selected papers. As a result, a comprehensive framework is proposed for implementing CTI in organizations. The proposed framework is comprised of a knowledge base, detection models, and visualization dashboards. The detection model layer consists of behavior-based, signature-based, and anomaly-based detection. In contrast, the knowledge base layer contains information resources on possible threats, vulnerabilities, and dangers to key assets. The visualization dashboard layer provides an overview of key metrics related to cyber threats, such as an organizational risk meter, the number of attacks detected, types of attacks, and their severity level. This relevant systematic study also provides insight for future studies, such as how organizations can tailor their approach to their needs and resources to facilitate more effective collaboration between stakeholders while navigating legal/regulatory constraints related to information sharing.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Literature Review on Cyber Threat Intelligence for Organizational Cybersecurity Resilience

Saeed,

Suayyid,

Al-Ghamdi

et al. 2023

Sensors

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show abstract

“…Detecting dangerous programs and protecting confidential information is always an urgent problem and a key asset for cybersecurity experts. The number of cybercriminals and types of threats [2] has grown significantly recently. Moreover, cyberattacks are becoming more challenging and complex than ever.…”

Section: Introductionmentioning

confidence: 99%

The development of a model for the threat detection system with the use of machine learning and neural network methods

Ussatova,

Zhumabekova,

Karyukin

et al. 2024

ijirss

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This study examines the development of a model for the threat detection system with the use of machine learning and neural network methods. The fast development of Internet technologies has led to the appearance of many digital systems and platforms. However, despite the impressive technological progress, another side also emerged in the spread of a massive number of different cyber threats. Although various ways have been created to detect and prevent them, the threats are also developing and becoming more complex each year. Therefore, new system defense and data protection methods using machine and deep learning approaches have been proposed recently. The methods based on these approaches have proved to be especially effective in the wave of new Artificial Intelligence applications. In this paper, a threat detection system has been designed to disclose different kinds of threats while maintaining the security, confidentiality, and availability of the computer system. The development of machine learning models for detecting DDoS and man-in-the-middle attacks, Structured Query Language (SQL) injections, phishing, and malware was examined. The data scaling, feature selection, feature extraction, and classification steps were also thoroughly described. Naïve Bayes, Logistic Regression, Decision Tree, Random Forest, XGBoost, CatBoost, and Deep Neural Network algorithms were utilized for training the cyber threat detection models. The experimental results evaluated all the models using accuracy, precision, recall, and F1-score metrics. The best models achieved scores in the range of 0.90 to 1.00.

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“…The parameters' uncertainty is modelled using probability distributions, and decisions are made based on maximizing the expected reward. [17]. Cyberattack predictions may be made in advance with the help of a virtual agent built using reinforcement learning.…”

Section: Introductionmentioning

confidence: 99%

Application Study on the Reinforcement Learning Strategies in the Network Awareness Risk Perception and Prevention

Xie

2024

Int J Comput Intell Syst

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The intricacy of wireless network ecosystems and Internet of Things (IoT) connected devices have increased rapidly as technology advances and cyber threats increase. The existing methods cannot make sequential decisions in complex network environments, particularly in scenarios with partial observability and non-stationarity. Network awareness monitors and comprehends the network's assets, vulnerabilities, and ongoing activities in real-time. Advanced analytics, machine learning algorithms, and artificial intelligence are used to improve risk perception by analyzing massive amounts of information, identifying trends, and anticipating future security breaches. Hence, this study suggests the Deep Reinforcement Learning-assisted Network Awareness Risk Perception and Prevention Model (DRL-NARPP) for detecting malicious activity in cybersecurity. The proposed system begins with the concept of network awareness, which uses DRL algorithms to constantly monitor and evaluate the condition of the network in terms of factors like asset configurations, traffic patterns, and vulnerabilities. DRL provides autonomous learning and adaptation to changing network settings, revealing the ever-changing nature of network awareness risks in real time. Incorporating DRL into risk perception increases the system's capacity to recognize advanced attack methods while simultaneously decreasing the number of false positives and enhancing the reliability of risk assessments. DRL algorithms drive dynamic and context-aware response mechanisms, making up the adaptive network prevention component of the development. Predicting new threats and proactively deploying preventive measures, such as changing firewall rules, isolating compromised devices, or dynamically reallocating resources to reduce developing risks, is made possible by the system's ability to learn from historical data and prevailing network activity. The suggested DRL-NARPP model increases the anomaly detection rate by 98.3%, the attack prediction accuracy rate by 97.4%, and the network risk assessment ratio by 96.4%, reducing the false positive ratio by 11.2% compared to other popular methodologies.

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Cyber Threat Intelligence for IoT Using Machine Learning

Cited by 14 publications

References 33 publications

A Systematic Literature Review on Cyber Threat Intelligence for Organizational Cybersecurity Resilience

A Systematic Literature Review on Cyber Threat Intelligence for Organizational Cybersecurity Resilience

The development of a model for the threat detection system with the use of machine learning and neural network methods

Application Study on the Reinforcement Learning Strategies in the Network Awareness Risk Perception and Prevention

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