Features selection and prediction for IoT attacks

Su, Jingyi; Shan, He; Wu, Yan

doi:10.1016/j.hcc.2021.100047

Cited by 22 publications

(12 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The simulation experiments are made to run for 30 times, and the averages of the results are used for plotting the graph to quantify the performance of the proposed BFPSMTM and the benchmarked schemes. The dataset utilized for evaluating the performance of the proposed BFPSMTM and the benchmarked schemes is the open‐source dataset termed IoTID20 derived from IEEE DataPort 21 . This dataset consists of maximized number of flow and network‐based features that are indispensable for evaluating and investigating the flow‐based malicious IoT nodes detection system.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

Bipolar Fuzzy PROMETHEE‐based Decision‐Making Trust Model for guaranteeing data dissemination reliability in Internet of Things

Duraisamy

Amalraj²,

Ramaiah

2022

Int J Communication

View full text Add to dashboard Cite

Internet of Things (IoT) is the rapidly growing domain that facilitates seamless connectivity to physical objects that makes them suitable and ideal entity of smart environment. The enforcement of trust between the physical objects is essential for completely utilizing the significant potentiality of these connected IoT objects. The traditional security strategies are not potent enough in provisioning comprehensive protection to the smart world. In this paper, Bipolar Fuzzy PROMETHEE-based Decision-Making Trust Model (BFPSMTM) is

show abstract

Section: Simulation Results and Discussionmentioning

confidence: 99%

Bipolar Fuzzy PROMETHEE‐based Decision‐Making Trust Model for guaranteeing data dissemination reliability in Internet of Things

Duraisamy

Amalraj²,

Ramaiah

2022

Int J Communication

View full text Add to dashboard Cite

show abstract

“…Most importantly, the proposed model is lightweight and can be deployed on IoT nodes with limited power and storage capabilities. Jingyi et al [77] used DT, RF, and GBM ML algorithms with a dataset generated from the IoTID20 dataset known as IoT2020 dataset. According to the results, the DT algorithm performed more accurately than the other algorithms, but RF had better AUC score.…”

Section: Analysis and Comparison Of Supervised ML Algorithms For Iot ...mentioning

confidence: 99%

A Review on IoT Intrusion Detection Systems Using Supervised Machine Learning: Techniques, Datasets, and Algorithms

Abdulla

Jameel

2023

UHD J SCI TECH

View full text Add to dashboard Cite

Physical objects that may communicate with one another are referred to “things” throughout the Internet of Things (IoT) concept. It introduces a variety of services and activities that are both available, trustworthy and essential for human life. The IoT necessitates multifaceted security measures that prioritize communication protected by confidentiality, integrity and authentication services; data inside sensor nodes are encrypted and the network is secured against interruptions and attacks. As a result, the issue of communication security in an IoT network needs to be solved. Even though the IoT network is protected by encryption and authentication, cyber-attacks are still possible. Consequently, it’s crucial to have an intrusion detection system (IDS) technology. In this paper, common and potential security threats to the IoT environment are explored. Then, based on evaluating and contrasting recent studies in the field of IoT intrusion detection, a review regarding the IoT IDSs is offered with regard to the methodologies, datasets and machine learning (ML) algorithms. In This study, the strengths and limitations of recent IoT intrusion detection techniques are determined, recent datasets collected from real or simulated IoT environment are explored, high-performing ML methods are discovered, and the gap in recent studies is identified.

show abstract

“…The drawback of the Gradient boosted models is delay in processing the input patterns with respect to the trained patterns. For all kinds of datasets, gradient boosted algorithms may not get adopted [19]. IoT network enabled mobile edge tracking system is developed here.…”

Section: Literature Surveymentioning

confidence: 99%

Detection of Phishing in Internet-of-Things Using Hybrid Deep Belief Network

Ashwini¹,

Kumar²

2023

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

Increase in the use of internet of things owned devices is one of the reasons for increased network traffic. While connecting the smart devices with publicly available network many kinds of phishing attacks are able to enter into the mobile devices and corrupt the existing system. The Phishing is the slow and resilient attack stacking techniques probe the users. The proposed model is focused on detecting phishing attacks in internet of things enabled devices through a robust algorithm called Novel Watch and Trap Algorithm (NWAT). Though Predictive mapping, Predictive Validation and Predictive analysis mechanism is developed. For the test purpose Canadian Institute of cyber security (CIC) dataset is used for creating a robust prediction model. This attack generates a resilience corruption works that slowly gathers the credential information from the mobiles. The proposed Predictive analysis model (PAM) enabled NWAT algorithm is used to predict the phishing probes in the form of suspicious process happening in the IoT networks. The prediction system considers the peer-to-peer communication window open for the established communication, the suspicious process and its pattern is identified by the new approach. The proposed model is validated by finding the prediction accuracy, Precision, recalls F1score, error rate, Mathew's Correlation Coefficient (MCC) and Balanced Detection Rate (BDR). The presented approach is comparatively analyzed with the state-of-the-art approach of existing system related to various types of Phishing probes.

show abstract

Features selection and prediction for IoT attacks

Cited by 22 publications

References 4 publications

Bipolar Fuzzy PROMETHEE‐based Decision‐Making Trust Model for guaranteeing data dissemination reliability in Internet of Things

Bipolar Fuzzy PROMETHEE‐based Decision‐Making Trust Model for guaranteeing data dissemination reliability in Internet of Things

A Review on IoT Intrusion Detection Systems Using Supervised Machine Learning: Techniques, Datasets, and Algorithms

Detection of Phishing in Internet-of-Things Using Hybrid Deep Belief Network

Contact Info

Product

Resources

About