Machine learning-based recommendation trust model for machine-to-machine communication

Eziama, Elvin; Jaimes, Luz Marina Santos; Agajo, James; Nwizege, Kenneth S.; Balador, Ali; Tepe, Kemal

doi:10.1109/isspit.2018.8705147

Cited by 15 publications

(10 citation statements)

References 8 publications

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“…Hardware/Software-based Attacks Several attacks can take place over the hardware components and software sys- Platoon Attack [37] DDoS Attack [38] DDoS Attack [74] DDoS Attack [77] DDoS Attack [39] GreyHole & BlackHole [40] Black hole [41] Sybil Attack [42] Sybil Attack [43] Sybil Attack [44] Jamming Attack [69] Jamming Attack [45] jamming Attack [91] Data Manipulation [70] Crossfire Attack [56] Spoofing Attack [57] Spoofing Attack [58] Spoofing Attack [92] Cyber Physical Attack [93] Cyber Physical Attack [46] MDS [47] MDS [49] MDS [50] MDS [82] MDS [68] MDS [76] MDS [55] MDS [48] FDI [51] IDS [52] IDS [59] IDS [60] IDS [53] IDS [54] IDS [81] IDS [73] IDS [94] IDS [95] Trust Computation [96] Trust Computation [61] Trust Computation [62] Trust Computation [63] Trust Computation [71] Trust Computation [65] Trust Computation [66] Trust Com...…”

Section: Security Attacks and Requirementsmentioning

confidence: 99%

“…Confidentiality [53] Availability [37]- [40], [44], [45], [49]- [55], [59], [60], [69], [70], [74], [77], [81], [84], [94], [98] Integrity [36], [46], [48], [52], [68], [76], [82], [91]- [93] Privacy [42], [51], [54], [64], [72], [75], [78], [80] Authentication [41]- [43], [46], [49], [53], [55]- [60] Trust [61]- [63], [65]- [67], [71], [79], [95], [96] This survey centers around the use of ML in achieving the above security requirements. As shown in Fig.…”

Section: Security Attacks and Requirementsmentioning

confidence: 99%

“…Privacy Protection [64], [72], [75], [78], [80] Trust Computation [61]- [63], [65]- [67], [71], [79], [95], [96] Misbehaviour or Intrusion Detection [46]- [55], [59], [60], [68], [73], [81], [82], [94], [136] Attack Detection Miscellaneous [70], [91]- [93] Spoofing Attack [56]- [58] Jamming Attack [44], [45], [69], [98] Sybil Attack [41]- [43] Black-Hole and Grey-Hole Attack [39], [40] DDoS [37], [38], [74], [77] Platoon Attack [36] Driver Identification/Fingerprinting [125], [137]- [151] Fig. 11.…”

Section: Security Solutionsmentioning

confidence: 99%

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Machine Learning for Security in Vehicular Networks: A Comprehensive Survey

Talpur,

Gurusamy

2021

Preprint

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Machine Learning (ML) has emerged as an attractive and viable technique to provide effective solutions for a wide range of application domains. An important application domain is vehicular networks wherein ML-based approaches are found to be very useful to address various problems. The use of wireless communication between vehicular nodes and/or infrastructure makes it vulnerable to different types of attacks. In this regard, ML and its variants are gaining popularity to detect attacks and deal with different kinds of security issues in vehicular communication. In this paper, we present a comprehensive survey of ML-based techniques for different security issues in vehicular networks. We first briefly introduce the basics of vehicular networks and different types of communications. Apart from the traditional vehicular networks, we also consider modern vehicular network architectures. We propose a taxonomy of security attacks in vehicular networks and discuss various security challenges and requirements. We classify the ML techniques developed in the literature according to their use in vehicular network applications. We explain the solution approaches and working principles of these ML techniques in addressing various security challenges and provide insightful discussion. The limitations and challenges in using ML-based methods in vehicular networks are discussed. Finally, we present observations and lessons learned before we conclude our work.

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Section: Security Attacks and Requirementsmentioning

confidence: 99%

Section: Security Attacks and Requirementsmentioning

confidence: 99%

Section: Security Solutionsmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning for Security in Vehicular Networks: A Comprehensive Survey

Talpur,

Gurusamy

2021

Preprint

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“…Compared to other methods, machine learning approaches provide better results because they can be applied to various types of datasets and can analyze real-time data. In a previous study, a trust model was constructed for machine-to-machine communication using various machine learning approaches, such as logistic regression, NB, DT, KNN and RF [4]. A comparative study has been performed to identify the best approach [5].…”

Section: Literature Surveymentioning

confidence: 99%

“…2, the global cybersecurity market has increased due to increasing threats and attacks, and, by 2023, it is expected that the market will increase exponentially. Despite measures implemented to maintain a secure environment, attacks can occur [4]. Building a network that is immune to all types of attack is not possible.…”

Section: Introductionmentioning

confidence: 99%

Robust Attack Detection Approach for IIoT Using Ensemble Classifier

Priya¹,

Thaseen²,

Gadekallu³

et al. 2021

Computers, Materials &Amp; Continua

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Generally, the risks associated with malicious threats are increasing for the Internet of Things (IoT) and its related applications due to dependency on the Internet and the minimal resource availability of IoT devices. Thus, anomaly-based intrusion detection models for IoT networks are vital. Distinct detection methodologies need to be developed for the Industrial Internet of Things (IIoT) network as threat detection is a significant expectation of stakeholders. Machine learning approaches are considered to be evolving techniques that learn with experience, and such approaches have resulted in superior performance in various applications, such as pattern recognition, outlier analysis, and speech recognition. Traditional techniques and tools are not adequate to secure IIoT networks due to the use of various protocols in industrial systems and restricted possibilities of upgradation. In this paper, the objective is to develop a two-phase anomaly detection model to enhance the reliability of an IIoT network. In the first phase, SVM and Naïve Bayes, are integrated using an ensemble blending technique. K-fold cross-validation is performed while training the data with different training and testing ratios to obtain optimized training and test sets. Ensemble blending uses a random forest technique to predict class labels. An Artificial Neural Network (ANN) classifier that uses the Adam optimizer to achieve better accuracy is also used for prediction. In the second phase, both the ANN and random forest results are fed to the model's classification unit, and the highest accuracy value is considered the final result. The proposed model is tested on standard IoT attack datasets, such as WUSTL_IIOT-2018, N_BaIoT, and Bot_IoT. The highest accuracy obtained is 99%. A comparative analysis of the proposed model using state-of-the-art ensemble techniques is performed to demonstrate the superiority of the results. The results also demonstrate that the proposed model outperforms traditional techniques and thus improves the reliability of an IIoT network.

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Maximizing Sensors Trust Through Support Vector Machine

Habib

Marimuthu

2021

Advances in Intelligent Systems and Computing

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Machine learning-based recommendation trust model for machine-to-machine communication

Cited by 15 publications

References 8 publications

Machine Learning for Security in Vehicular Networks: A Comprehensive Survey

Machine Learning for Security in Vehicular Networks: A Comprehensive Survey

Robust Attack Detection Approach for IIoT Using Ensemble Classifier

Maximizing Sensors Trust Through Support Vector Machine

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