Enhanced intrusion detection system via agent clustering and classification based on outlier detection

Sandosh, S.; Govindasamy, V.; Akila, G.

doi:10.1007/s12083-019-00822-3

Cited by 34 publications

(13 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sandosh et al 33 have proposed an enhanced intrusion detection system via agent clustering and classification based on outlier detection (EIDS‐ACC‐OD). The preprocessing is done before removing the unwanted spots using external detection.…”

Section: Problem Methodology and System Architecturementioning

confidence: 99%

“…The results of EIDS-ACC-OD technique showed the accuracy, precision, recall, specificity, F1-score, false positive ratio (FRT), and false negative ratio (FNR). From the review, [21][22][23][24][25][26][27][28][29][30][31][32][33] with the increased use of the Internet and the services it provides, there has been an increase in cyber attacks for the use of information. Cloud computing is a technology used to store and maintain user information due to its simplicity and low budget services.…”

Section: Research Gapmentioning

confidence: 99%

See 1 more Smart Citation

A hybrid soft computing technique for intrusion detection in web and cloud environment

Maheswari¹,

Siva²,

Nalinipriya

2022

Concurrency and Computation

View full text Add to dashboard Cite

Cloud computing environment contains important, essential, or confidential information; therefore, a security solution is needed to prevent this environment from potential attacks. In short, cloud computing has become one of the most sought after technologies in the field of information technology, and among the most dangerous threats. In this article, we propose a hybrid soft computing technique for intrusion detection in web and cloud environment (ST‐IDS). In ST‐IDS, we illustrate whale integrated slap swarm optimization algorithm for pre‐processing which remove the unwanted/repeated data's in dataset. We introduce new clustering technique based on modified tug‐of‐war optimization algorithm which groups the data in different segments. Then, we develop hybrid machine learning technique that is, capsule learning based neural network which categorize the attack in cloud environment. Finally, the proposed ST‐IDS technique can evaluate through standard open source datasets are KDD cup'99 and NSL‐KDD. The performance comparison of the proposed ST‐IDS technique using existing innovative technologies in terms of accuracy, precession, recall, specificity, F measure, false positive rate, and false negative rate.

show abstract

Section: Problem Methodology and System Architecturementioning

confidence: 99%

Section: Research Gapmentioning

confidence: 99%

A hybrid soft computing technique for intrusion detection in web and cloud environment

Maheswari¹,

Siva²,

Nalinipriya

2022

Concurrency and Computation

View full text Add to dashboard Cite

show abstract

“…Once calculated, these two distances are summed, and the obtained one‐dimensional distance is used to represent each data instance for intrusion detection by the KNN classifier. Sandoshet al 102 proposed the EIDS‐ACC‐OD, a new IDS via an agent clustering and classification based on Outlier Detection. The KDD CUP 99 was used to test the proposed model.…”

Section: Anomaly‐based Intrusion Detection Techniquesmentioning

confidence: 99%

Anomaly‐based intrusion detection systems: The requirements, methods, measurements, and datasets

Hajj

Sibai

Abdo

et al. 2021

Trans Emerging Tel Tech

View full text Add to dashboard Cite

With the Internet's unprecedented growth and nations' reliance on computer networks, new cyber‐attacks are created every day as means for achieving financial gain, imposing political agendas, and developing cyberwarfare arsenals. Network security is thus acquiring increasing attention among researchers, practitioners, network architects, policy makers, and others. To defend organizations' networks from existing, foreseen, and future threats, intrusion detection systems (IDSs) are becoming a must. Existing surveys on anomaly‐based IDS (AIDS) focus on specific components such as detection mechanisms and lack many others. In contrast to existing surveys, this article covers the full scope needed by researchers and practitioners alike when studying AIDS. The scope ranges from the intrusion detection techniques to attacks forms and passing through the relevant attack features, most‐used datasets, challenges, and potential solutions. This article provides an exhaustive review of IDSs and discusses their requirements and performance metrics in deep. It presents a taxonomy of IDSs based on four criteria: information source, detection strategy, detection mode, and architecture. Then, in‐depth analysis and a comparison of network intrusion detection approaches based on anomaly detection techniques are given. The article also introduces a classification of computer network attacks, along with their different forms and the relevant network traffic features to detect them, as well as a summary of the popular datasets used by the researchers to evaluate the IDSs. Finally, the article highlights several research challenges and the possible solutions to deal with them.

show abstract

“…Therefore, it is necessary to detect both outliers for building an intrusion detection system for practical domains. However, most previous studies have focused on detecting point outliers [6,20].…”

Section: Introductionmentioning

confidence: 99%

Towards Building Intrusion Detection Systems for Multivariate Time-Series Data

Seong

Song

Hyun

et al. 2022

Communications in Computer and Information Science

View full text Add to dashboard Cite

Recent network intrusion detection systems have employed machine learning and deep learning algorithms to defend against dynamically evolving network attacks. While most previous studies have focused on detecting attacks which can be determined based on a single time instant, few studies have paid attention to subsequence outliers, which require inspecting consecutive points in time for detection. To address this issue, this paper applies a time-series anomaly detection method in an unsupervised learning manner. To this end, we converted the UNSW-NB15 dataset into the time-series data. We carried out a preliminary evaluation to test the performance of the anomaly detection on the created time-series network dataset as well as on a time-series dataset obtained from sensors. We analyze and discuss the results.

show abstract

Enhanced intrusion detection system via agent clustering and classification based on outlier detection

Cited by 34 publications

References 17 publications

A hybrid soft computing technique for intrusion detection in web and cloud environment

A hybrid soft computing technique for intrusion detection in web and cloud environment

Anomaly‐based intrusion detection systems: The requirements, methods, measurements, and datasets

Towards Building Intrusion Detection Systems for Multivariate Time-Series Data

Contact Info

Product

Resources

About