A Hybrid Intrusion Detection System for Smart Home Security Based on Machine Learning and User Behavior

Alghayadh, Faisal; Debnath, Debatosh

doi:10.4236/ait.2021.111002

Cited by 21 publications

(5 citation statements)

References 29 publications

(27 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…8. For smarter home security, the experts [14] suggested a half-and-half model interruption identification model. The model was divided into two pieces.…”

Section: Hybrid-based Methodologymentioning

confidence: 99%

Updates on Software Usability

Buttar¹,

Majid²

2023

Quality Control - An Anthology of Cases

View full text Add to dashboard Cite

Network security ensures that essential and accessible network assets are protected from viruses, key loggers, hackers, and unauthorized gain. Interruption detection system (IDS) is one of the most widespread significant network tools for network security management. However, it has been shown that the current IDS is challenging for network professionals to use. The interface, which assists users in evaluating the software usability, is a crucial aspect that influences the effectiveness of IDS, while security software such as IDS is effective. Usability testing is essential for supporting users in successful interaction and IDS utilization because the user finds it difficult to assess and use the output quality. Usability engineers are responsible for the majority of usability evaluations. Small and large business software engineers must master multiple usability paradigms. This is more difficult than teaching usability engineers how to write software. The Cognitive Analysis of Software Interface (CASI) technology was created as a solution for software engineers. Furthermore, this system aids software engineers in evaluating IDS based on user perception and evaluation perspectives. This study also discusses a large body of research on software interfaces and assessment procedures to evaluate novel heuristics for IDS. Finally, additional interface challenges and new ways for evaluating programmed usability are discussed. Topic Subject Areas: Intrusion Detection System (IDS) Usability.

show abstract

“…8. For smarter home security, the experts [14] suggested a half-and-half model interruption identification model. The model was divided into two pieces.…”

Section: Hybrid-based Methodologymentioning

confidence: 99%

Updates on Software Usability

Buttar¹,

Majid²

2023

Quality Control - An Anthology of Cases

View full text Add to dashboard Cite

show abstract

“…F1-Score F1-score is the harmonic mean of the precision and recall values, essentially a combined measure of the two performance metrics. F1-score quantifies how discriminative the model is [26] and acts as a good indicator of performance since a decrease in either precision or recall results in a significant decrease in the F1-score. In addition, for multiclass classification, we present both the unweighted and weighted F1-scores.…”

Section: Recallmentioning

confidence: 99%

DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection

Strickland,

Zakar,

Saha

et al. 2024

Sensors

View full text Add to dashboard Cite

Our increasingly connected world continues to face an ever-growing number of network-based attacks. An Intrusion Detection System (IDS) is an essential security technology used for detecting these attacks. Although numerous Machine Learning-based IDSs have been proposed for the detection of malicious network traffic, the majority have difficulty properly detecting and classifying the more uncommon attack types. In this paper, we implement a novel hybrid technique using synthetic data produced by a Generative Adversarial Network (GAN) to use as input for training a Deep Reinforcement Learning (DRL) model. Our GAN model is trained on the NSL-KDD dataset, a publicly available collection of labeled network traffic data specifically designed to support the evaluation and benchmarking of IDSs. Ultimately, our findings demonstrate that training the DRL model on synthetic datasets generated by specific GAN models can result in better performance in correctly classifying minority classes over training on the true imbalanced dataset.

show abstract

“…In [23], the researchers proposed a hybrid model intrusion detection model for smart home security. e model consisted of two components.…”

Section: Hybrid Intrusion Detection Systemmentioning

confidence: 99%

A Systematic Review on Hybrid Intrusion Detection System

Maseno

Wang

Xing

2022

Security and Communication Networks

View full text Add to dashboard Cite

As computer networks keep growing at a high rate, achieving confidentiality, integrity, and availability of the information system is essential. Intrusion detection systems (IDSs) have been widely used to monitor and secure networks. The two major limitations facing existing intrusion detection systems are high rates of false-positive alerts and low detection rates on zero-day attacks. To overcome these problems, we need intrusion detection techniques that can learn and effectively detect intrusions. Hybrid methods based on machine learning techniques have been proposed by different researchers. These methods take advantage of the single detection methods and leverage their weakness. Therefore, this paper reviews 111 related studies in the period between 2012 and 2022 focusing on hybrid detection systems. The review points out the existing gaps in the development of hybrid intrusion detection systems and the need for further research in this area.

show abstract

A Hybrid Intrusion Detection System for Smart Home Security Based on Machine Learning and User Behavior

Cited by 21 publications

References 29 publications

Updates on Software Usability

Updates on Software Usability

DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection

A Systematic Review on Hybrid Intrusion Detection System

Contact Info

Product

Resources

About