A PCA-based method for IoT network traffic anomaly detection

Hoang, Dang Hai; Nguyen, Ha Duong

doi:10.23919/icact.2018.8323765

Cited by 25 publications

(7 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The PCA technique transforms a large set of variables into a reduced set of features without losing much of the information. Various research works [159][160][161][162] used a combination of PCA with various classifiers to detect anomalies in IoT networks. -It produces a more robust and accurate output which is resistant to overfitting.…”

Section: Principle Component Analysis (Pca)mentioning

confidence: 99%

A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

et al. 2020

View full text Add to dashboard Cite

The Internet of Things (IoT) is poised to impact several aspects of our lives with its fast proliferation in many areas such as wearable devices, smart sensors and home appliances. IoT devices are characterized by their connectivity, pervasiveness and limited processing capability. The number of IoT devices in the world is increasing rapidly and it is expected that there will be 50 billion devices connected to the Internet by the end of the year 2020. This explosion of IoT devices, which can be easily increased compared to desktop computers, has led to a spike in IoT-based cyber-attack incidents. To alleviate this challenge, there is a requirement to develop new techniques for detecting attacks initiated from compromised IoT devices. Machine and deep learning techniques are in this context the most appropriate detective control approach against attacks generated from IoT devices. This study aims to present a comprehensive review of IoT systems-related technologies, protocols, architecture and threats emerging from compromised IoT devices along with providing an overview of intrusion detection models. This work also covers the analysis of various machine learning and deep learning-based techniques suitable to detect IoT systems related to cyber-attacks.

show abstract

Section: Principle Component Analysis (Pca)mentioning

confidence: 99%

A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

et al. 2020

View full text Add to dashboard Cite

show abstract

“…However, the scheme fails to provide security and privacy during information exchange between sensor nodes, fog, and cloud layers. A Principal Component Analysis (PCA) approach for network anomaly detection in IoT is proposed [45]. The schemes employ the Minkowski formula to compute the distance between the principal components (PC) and Empirical Cumulative Distribution Function (ECDF) for defining the threshold.…”

Section: ) Statistical Based Schemesmentioning

confidence: 99%

Security Analysis of Network Anomalies Mitigation Schemes in IoT Networks

2020

View full text Add to dashboard Cite

The Internet of Things (IoT) is on the rise and it is giving a new shape to several fields such as smart cities, smart homes, smart health, etc. as it facilitates the connection of physical objects to the internet. However, this advancement comes along with new challenges in terms of security of the devices in the IoT networks. Some of these challenges come as network anomalies. Hence, this has prompted the use of network anomaly mitigation schemes as an integral part of the defense mechanisms of IoT networks in order to protect the devices from malicious users. Thus, several schemes have been proposed to mitigate network anomalies. This paper covers a review of different network anomaly mitigation schemes in IoT networks. The schemes' objectives, operational procedures, and strengths are discussed. A comparison table of the reviewed schemes, as well as a taxonomy based on the detection methodology, is provided. In contrast to other surveys that presented qualitative evaluations, our survey provides both qualitative and quantitative evaluations. The UNSW-NB15 dataset was used to conduct a performance evaluation of some classification algorithms used for network anomaly mitigation schemes in IoT. Finally, challenges and open issues in the development of network anomaly mitigation schemes in IoT are discussed.

show abstract

“…Such capability in data reduction, while retaining most of the variation presents in the original data, has made PCA useful. Hong in (Hoang and Nguyen, 2018) applied PCA using substantial data sample for IoT anomaly detection.…”

Section: Resource Reductionmentioning

confidence: 99%

Reducing Computational Cost in IoT Cyber Security: Case Study of Artificial Immune System Algorithm

Zakariyya

Al-Kadri

Kalutarage

et al. 2019

Proceedings of the 16th International Joint Conference on E-Business and Telecommunications

View full text Add to dashboard Cite

Using Machine Learning (ML) for Internet of Things (IoT) security monitoring is a challenge. This is due to their resource constraint nature that limits the deployment of resource-hungry monitoring algorithms. Therefore, the aim of this paper is to investigate resource consumption reduction of ML algorithms in IoT security monitoring. This paper starts with an empirical analysis of resource consumption of Artificial Immune System (AIS) algorithm, and then employs carefully selected feature reduction techniques to reduce the computational cost of running the algorithm. The proposed approach significantly reduces computational cost as illustrated in the paper. We validate our results using two benchmarks and one purposefully simulated data set. 2 RELATED WORK There are various works in the field of IoT from the perspectives of security, architecture, deployment op

show abstract

A PCA-based method for IoT network traffic anomaly detection

Cited by 25 publications

References 19 publications

A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

A Review of Intrusion Detection Systems Using Machine and Deep Learning in Internet of Things: Challenges, Solutions and Future Directions

Security Analysis of Network Anomalies Mitigation Schemes in IoT Networks

Reducing Computational Cost in IoT Cyber Security: Case Study of Artificial Immune System Algorithm

Contact Info

Product

Resources

About