A Systematic Review on Hybrid Intrusion Detection System

Maseno, Elijah M.; Wang, Zenghui; Xing, Hongyan

doi:10.1155/2022/9663052

Cited by 14 publications

(5 citation statements)

References 124 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…10. The creators [16] fostered an interruption location framework by joining firefly and Hopfield brain organization HNN calculations. The analysts utilized Firefly calculation to identify refusal-of-rest assaults through hub grouping and verification.…”

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

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

“…Extensive experiments on four intrusion datasets were conducted to demonstrate the effectiveness and robustness of the RUIDS. Yang et al [5] proposed IDS based on an improved vision transformer, demonstrating superior results on the NSL-KDD public intrusion detection via simulation experiments.…”

Section: B Algorithm-level Mitigation Effortsmentioning

confidence: 99%

“…1 presents a schematic diagram of our proposed model. [5], NSL-KDD [53] and KDD-CUP99 [54] are the most widely used datasets in IDS research (ca. 2012-2022).…”

Section: Proposed Modelmentioning

confidence: 99%

“…Intrusion detection systems (IDSs) are designed to detect a variety of anomalous patterns that serve as the attack signatures of new and known attacks [3], using advanced database systems with machine learning [4]. When an IDS reports potential malicious activities in an information system [5], it kicks off various analytical and alerting processes to confirm the nature of the attack and launch protection measures. IDSs generally operate in three phases: information collection, data analysis, and response.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Hybrid Intrusion Detection System Based on Data Resampling and Deep Learning

Chen,

You,

Shiue

2024

IJACSA

View full text Add to dashboard Cite

The growth of the internet has advanced information-sharing capabilities and vastly increased the importance of global network security. However, because new and inconspicuous abnormal behaviors are nearly impossible to detect in massive network access environments, modern intrusion detection systems have identified a high rate of false-positive (FP) and false-negative (FN) attacks. To overcome this, this paper proposes a hybrid deep learning model that significantly mitigates the disadvantages of consistently imbalanced sample attack data. First, it resolves imbalanced data using random undersampling and synthetic minority oversampling techniques. Then, convolutional neural networks (CNNs) extract local and spatial features, and a transformer encoder extracts global and temporal features. The novelty of this combination increases recognition accuracy at the algorithm level, which is crucial to reducing FPs and FNs. The model was subjected to multiclassification testing on the NSL-KDD and CICIDS2017 benchmark datasets, and the results show that our model has higher classification accuracy and lower FP rates than state-ofthe-art intrusion detection models. Moreover, it significantly improves the detection rate of low-frequency attacks.

show abstract

“…Among these security measures, the Intrusion Detection System is extensively explored in the literature as a technique to enhance security for individual users and information systems. Intrusion, defined as compromising the integrity, availability, and confidentiality of information on a computer network through unauthorized access, has been a subject of significant attention in the field (Maseno et al, 2022). As per Ali et al (2018), an intrusion detection system serves as the primary security technique deployed to protect IT infrastructure against intentional and unintentional attacks.…”

Section: Introductionmentioning

confidence: 99%

CFS-AE: Correlation-based Feature Selection and Autoencoder for Improved Intrusion Detection System Performance

Alhassan,

Abdul-Salaam,

Micheal

et al. 2024

JISIS

View full text Add to dashboard Cite

The major problem computer network users face concerning data – whether in storage, in transit, or being processed - is unauthorized access. This unauthorized access typically leads to the loss of confidentiality, integrity, and availability of data. Consequently, it is essential to implement an accurate Intrusion Detection System (IDS) for every information system. Many researchers have proposed machine learning and deep learning models, such as autoencoders, to enhance existing IDS. However, the accuracy of these models remains a significant research challenge. This paper proposes a Correlation-Based Feature Selection and Autoencoder (CFS-AE) to enhance detection accuracy and reduce the false alarms associated with the current anomaly-based IDS. The first step involves feature selection for the NSL-KDD and CIC-IDS2017 datasets which are used to train and test our model. Subsequently, an autoencoder is employed as a classifier to categorize data traffic into attack and normal categories. The results from our experimental study revealed an accuracy of 94.32% and 97.71% for the NSL-KDD and CIC-IDS2017 datasets, respectively. These results demonstrate improved performance over existing IDS systems.

show abstract

A Systematic Review on Hybrid Intrusion Detection System

Cited by 14 publications

References 124 publications

Updates on Software Usability

Updates on Software Usability

Hybrid Intrusion Detection System Based on Data Resampling and Deep Learning

CFS-AE: Correlation-based Feature Selection and Autoencoder for Improved Intrusion Detection System Performance

Contact Info

Product

Resources

About