A reliable and energy-efficient classifier combination scheme for intrusion detection in embedded systems

Viegas, Eduardo K.; Santin, Altair O.; Oliveira, Luiz S.; França, André Luiz Pereira de; Jasinski, Ricardo P.; Pedroni, Volnei A.

doi:10.1016/j.cose.2018.05.014

Cited by 21 publications

(9 citation statements)

References 20 publications

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“…The authors leverage sequence-based supervised learning which is based on Influential Relative Grade (IRG) and Relative Mass Function (RMF) which efficiently detects the outliers. Viegas et al [97] aim at energy-efficient and hardware-friendly implementation of the intrusion detection systems in IoT. The authors leveraged 3 classifiers, Decision Tree (DT), Naive Bayes (NB), and Linear Discriminant Analysis (LDA) during their experimentation for intrusion detection.…”

Section: Anomaly/intrusion Detectionmentioning

confidence: 99%

Machine Learning in IoT Security: Current Solutions and Future Challenges

Hussain

Hassan

et al. 2020

IEEE Commun. Surv. Tutorials

557

270

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The future Internet of Things (IoT) will have a deep economical, commercial and social impact on our lives. The participating nodes in IoT networks are usually resourceconstrained, which makes them luring targets for cyber attacks. In this regard, extensive efforts have been made to address the security and privacy issues in IoT networks primarily through traditional cryptographic approaches. However, the unique characteristics of IoT nodes render the existing solutions insufficient to encompass the entire security spectrum of the IoT networks. This is, at least in part, because of the resource constraints, heterogeneity, massive real-time data generated by the IoT devices, and the extensively dynamic behavior of the networks. Therefore, Machine Learning (ML) and Deep Learning (DL) techniques, which are able to provide embedded intelligence in the IoT devices and networks, are leveraged to cope with different security problems. In this paper, we systematically review the security requirements, attack vectors, and the current security solutions for the IoT networks. We then shed light on the gaps in these security solutions that call for ML and DL approaches. We also discuss in detail the existing ML and DL solutions for addressing different security problems in IoT networks. At last, based on the detailed investigation of the existing solutions in the literature, we discuss the future research directions for ML-and DL-based IoT security.

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Section: Anomaly/intrusion Detectionmentioning

confidence: 99%

Machine Learning in IoT Security: Current Solutions and Future Challenges

Hussain

Hassan

et al. 2020

IEEE Commun. Surv. Tutorials

557

270

View full text Add to dashboard Cite

show abstract

“…IDS consist of four major components namely, Information Source, Feature Selection, Detection Engine and Response. These four components function collaboratively with an objective to identify attacks and report output in a required format [24] . Fig.…”

Section: Intrusion Detection Systemmentioning

confidence: 99%

Comprehensive analysis and recommendation of feature evaluation measures for intrusion detection

Binbusayyis

Vaiyapuri

2020

Heliyon

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The revolutionary advances in network technologies have spearheaded the design of advanced cyberattacks to surpass traditional security defense with dreadful consequences. Recently, Intrusion Detection System (IDS) is considered as a pivotal element in network security infrastructures to achieve solid line of protection against cyberattacks. The prime challenges presented to IDS are curse of high dimensionality and class imbalance that tends to increase the detection time and degrade the efficiency of IDS. As a result, feature selection plays an important role in enabling to identify the most significant features for intrusion detection. Although, several feature evaluation measures are being proposed for feature selection in literature, there is no consensus on which measures are best for intrusion detection. Therein, this work aims at recommending the most appropriate feature evaluation measure for building an efficient IDS. In this direction, four filter-based feature evaluation measures that stem from different theories such as Consistency, Correlation, Information and Distance are investigated for their potential implications in enhancing the detection ability of IDS model for different classes of attacks. Along with this, the influence of the selected features on classification accuracy of an IDS model is analyzed using four different categories of classifiers namely, K-nearest neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM) and Deep Belief Network (DBN). Finally, a two-step statistical significance test is conducted on the experimental results to determine which feature evaluation measure contributes statistically significant difference in IDS performance. All the experimental comparisons are performed on two benchmark intrusion detection datasets, NSL-KDD and UNSW-NB15. In these experiments, consistency measure has best influenced the IDS model in improving the detection ability with regard to detection rate (DR), false alarm rate (FAR), kappa statistics (KS) and identifying the most significant features for intrusion detection. Also, from the analysis results, it is revealed that RF is the ideal classifier to be used in conjunction with any of these four feature evaluation measures to achieve better detection accuracy than others. From the statistical results, we recommend the use of consistency measure for designing an efficient IDS in terms of DR and FAR.

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“…The specificity of their work is to use stateful features and they get up to 30% better performance compared to without these features. Hussain et al [5] list for each problem many surveys that use machine learning techniques ( [9], [10] Butun et al [3] classify the IDS methodology of IDS in 3 categories: 1 -Anomaly Based detection:…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Methods for Anomaly Detection in IoT Networks, with Illustrations

Bonandrini

Bercher

Zangar

2020

Machine Learning for Networking

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IoT devices have been the target of 100 million attacks in the first half of 2019 [1]. According to [2], there will be more than 64 billion Internet of Things (IoT) devices by 2025. It is thus crucial to secure IoT networks and devices, which include significant devices like medical kit or autonomous car. The problem is complicated by the wide range of possible attacks and their evolution, by the limited computing resources and storage resources available on devices. We begin by introducing the context and a survey of Intrusion Detection System (IDS) for IoT networks with a state of the art. So as to test and compare solutions, we consider available public datasets and select the CIDDS-001 Dataset. We implement and test several machine learning algorithms and show that it is relatively easy to obtain reproducible results [20] at the state-of-the-art. Finally, we discuss embedding such algorithms in the IoT context and point-out the possible interest of very simple rules.

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A reliable and energy-efficient classifier combination scheme for intrusion detection in embedded systems

Cited by 21 publications

References 20 publications

Machine Learning in IoT Security: Current Solutions and Future Challenges

Machine Learning in IoT Security: Current Solutions and Future Challenges

Comprehensive analysis and recommendation of feature evaluation measures for intrusion detection

Machine Learning Methods for Anomaly Detection in IoT Networks, with Illustrations

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