Graph-based IoT microservice security

Pahl, Marc-Oliver; Aubet, François-Xavier; Liebald, Stefan

doi:10.1109/noms.2018.8406118

Cited by 48 publications

(52 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed work has two phases: (a) obtaining the balanced corpus of IoT profiles from original imbalanced data 9 by using SMOTE and (b) designing multiclass adaptive boosting based model for prediction of anomalies in IoT network. In this work, we have used IoT security dataset from kaggle 53 for the model evaluation.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…While implementing machine learning algorithm, the structure of the data, particularly the balance between the numbers of observations for each potential target, has significant influence of the performance of the model prediction's performance 54 . Due to imbalance nature of the IoT anomalies data, 9 we have used SMOTE 55 in order to achieve a balance between the underrepresented classes and majority class. 1 and 2 have been used to transform the original imbalanced IoT dataset 9 to balanced corpus of IoT profiles.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…Due to imbalance nature of the IoT anomalies data, 9 we have used SMOTE 55 in order to achieve a balance between the underrepresented classes and majority class. 1 and 2 have been used to transform the original imbalanced IoT dataset 9 to balanced corpus of IoT profiles. Figure 1A,B represents the distribution of samples in original data and transformed corpus of IoT profiles respectively.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…Pahl and Aubet 9 have developed a novel strategy for the detection of anomalies in IoT microservices. K‐means, as well as Birch clustering algorithm, have been considered for the study.…”

Section: Literature Studymentioning

confidence: 99%

See 3 more Smart Citations

Model based IoT security framework using multiclass adaptive boosting with SMOTE

Dash

Nayak

Naik

et al. 2020

Security and Privacy

View full text Add to dashboard Cite

Security and threats are growing immensely due to the higher usage of internet of things applications in all aspects. Due to imbalanced nature of IoT security data, the designing of model‐based anomaly detection in IoT network poses a challenge for machine learning model as most of the machine learning model assumes the equal number of samples for each class. Approximately, 2.79% of IoT network profiles are of anomaly types which impose severe imbalance where there are three samples in the anomaly types for hundreds of samples in the majority normal class. This results in poor predictive performance for identification of anomaly type, which is essentially a problem because the anomaly type is more sensitive than the normal activity type. This work proposes a multiclass adaptive boosting ensemble learning‐based model with the synthetic minority oversampling technique for prediction of an anomaly in IoT network. The proposed approaches are simulated with DS2OS data and the performance is compared with other machine learning approaches. The evaluation metrics such as sensitivity, F1‐score, and receiver operating characteristic‐AUC imply the efficiency of the proposed approach in handling the imbalanced nature of the data and found efficient to identify both anomaly types and normal activity.

show abstract

Section: Proposed Methodologymentioning

confidence: 99%

Section: Proposed Methodologymentioning

confidence: 99%

Section: Proposed Methodologymentioning

confidence: 99%

“…Pahl and Aubet 9 have developed a novel strategy for the detection of anomalies in IoT microservices. K‐means, as well as Birch clustering algorithm, have been considered for the study.…”

Section: Literature Studymentioning

confidence: 99%

See 2 more Smart Citations

Model based IoT security framework using multiclass adaptive boosting with SMOTE

Dash

Nayak

Naik

et al. 2020

Security and Privacy

View full text Add to dashboard Cite

show abstract

“…IoT devices use different types of services, technologies and protocols. As a result, huge complexity will arise to maintain the future IoT infrastructures, which consequently leads to undesirable vulnerability to the system [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

Cyberattacks Detection in IoT-Based Smart City Applications Using Machine Learning Techniques

Rashid

Kamruzzaman

Hassan

et al. 2020

IJERPH

101

View full text Add to dashboard Cite

In recent years, the widespread deployment of the Internet of Things (IoT) applications has contributed to the development of smart cities. A smart city utilizes IoT-enabled technologies, communications and applications to maximize operational efficiency and enhance both the service providers’ quality of services and people’s wellbeing and quality of life. With the growth of smart city networks, however, comes the increased risk of cybersecurity threats and attacks. IoT devices within a smart city network are connected to sensors linked to large cloud servers and are exposed to malicious attacks and threats. Thus, it is important to devise approaches to prevent such attacks and protect IoT devices from failure. In this paper, we explore an attack and anomaly detection technique based on machine learning algorithms (LR, SVM, DT, RF, ANN and KNN) to defend against and mitigate IoT cybersecurity threats in a smart city. Contrary to existing works that have focused on single classifiers, we also explore ensemble methods such as bagging, boosting and stacking to enhance the performance of the detection system. Additionally, we consider an integration of feature selection, cross-validation and multi-class classification for the discussed domain, which has not been well considered in the existing literature. Experimental results with the recent attack dataset demonstrate that the proposed technique can effectively identify cyberattacks and the stacking ensemble model outperforms comparable models in terms of accuracy, precision, recall and F1-Score, implying the promise of stacking in this domain.

show abstract

Raspberry Pi-Based Intelligent Cyber Defense Systems for SMEs: An Exploratory Study

Tirumala

Nepal

Ray

2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

View full text Add to dashboard Cite

Graph-based IoT microservice security

Cited by 48 publications

References 11 publications

Model based IoT security framework using multiclass adaptive boosting with SMOTE

Model based IoT security framework using multiclass adaptive boosting with SMOTE

Cyberattacks Detection in IoT-Based Smart City Applications Using Machine Learning Techniques

Raspberry Pi-Based Intelligent Cyber Defense Systems for SMEs: An Exploratory Study

Contact Info

Product

Resources

About