A New Method of Fuzzy Support Vector Machine Algorithm for Intrusion Detection

Liu, Wei; Ci, Linlin; Liu, Liping

doi:10.3390/app10031065

Cited by 24 publications

(9 citation statements)

References 56 publications

(65 reference statements)

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“…A voting algorithm is used for decision making to classify the data into attack type or benign type. A new fuzzy SVM was developed 21 for intrusion detection and it proved to be effective in comparison with SVM. This technique is applicable for system call sequence and can predict accurately.…”

Section: Related Workmentioning

confidence: 99%

An intelligent ensemble of long‐short‐term memory with genetic algorithm for network anomaly identification

Thaseen

Chitturi

Al‐Turjman³

et al. 2020

Trans Emerging Tel Tech

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Cyberattacks are increasing rapidly with rapid Internet advancement and, the cybersecurity situation is not optimistic. Anomaly detection is one of the challenging sectors of network security, which shows a significant role in any organization. Many anomaly detection systems identify malicious activities by deploying machine learning and deep learning techniques. The major contribution of this research is to develop an anomaly detection model for networks using a homogenous ensemble of Long-Short-Term-Memory integrated with Genetic Algorithm (GA) utilized for feature extraction. An extensive literature on anomaly detection, which utilizes deep learning algorithms, is studied. NSL-KDD and UNSW-NB datasets are deployed for evaluating the proposed network anomaly model. The experimental analysis shows that the proposed ensemble is superior to other ensembles with a maximum accuracy of 99.9% and a minimum false alarm rate of 1.56% on NSL-KDD dataset and a maximum accuracy of 99.3% is obtained on UNSW-NB15 dataset with false alarm rate of 1.7%.Hence, the proposed model performs fair on both the datasets.

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Section: Related Workmentioning

confidence: 99%

An intelligent ensemble of long‐short‐term memory with genetic algorithm for network anomaly identification

Thaseen

Chitturi

Al‐Turjman³

et al. 2020

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…For features with lower variance, they are normalized by division of their max value (second equation). Specific to the sensitivity to noise innate in support vector machines (SVMs), Liu et al [100] worked towards mitigating the sensitivity that SVMs have for noise samples by applying a fuzzy membership to measure the distance between a sample and the hyperplane, as in SVM. The larger the distance, the smaller the weight coefficient for the sample.…”

Section: Handling Noisy Featuresmentioning

confidence: 99%

Data-Driven Network Intrusion Detection: A Taxonomy of Challenges and Methods

Chou,

Jiang

2020

Preprint

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Data-driven methods have been widely used in network intrusion detection (NID) systems. However, there are currently a number of challenges derived from how the datasets are being collected. Most attack classes in network intrusion datasets are considered the minority compared to normal traffic and many datasets are collected through virtual machines or other simulated environments rather than real-world networks. These challenges undermine the performance of intrusion detection machine learning models by fitting models such as random forests or support vector machines to unrepresentative "sandbox" datasets. This survey presents a carefully designed taxonomy highlighting eight main challenges and solutions and explores common datasets from 1999 to 2020. Trends are analyzed on the distribution of challenges addressed for the past decade and future directions are proposed on expanding NID into cloud-based environments, devising scalable models for larger amount of network intrusion data, and creating labeled datasets collected in real-world networks. CCS Concepts: • General and reference → Surveys and overviews; • Networks → Network security; • Security and privacy → Intrusion/anomaly detection and malware mitigation; • Computing methodologies → Machine learning.

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“…However, as a supervised learning algorithm, sufficient labeled data are required to achieve good classification performance. In this study, the extracted features were linear and nonlinear to detect the fault and classified using the quadratic support vector machine (QSVM) [5,6,7].…”

Section: Introductionmentioning

confidence: 99%

Quadratic Support Vector Machine and K-Nearest Neighbor Based Robust Sensor Fault Detection and Isolation

Abed¹,

Gitaffa²,

Issa³

2021

ETJ

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Fault detection plays a serious role in high-cost and safety-critical processes. There are two main drivers for continuous improvement in the area of early detection of process faults safety and reliability of technical plants. Detect fault in Geophone string sensors (SG-10) are very important in oil exploration to avoid loss economy. Methods are developed to enable earlier detection of process faults than the traditional limit and trend checking based on a single process variable and the development of these methods is a key matter. Classification methods will be used for pattern recognition and as such is appropriate for fault detection. In supervised training input-output pairs, both for normal and fault conditions, are presented to the network. The models were trained on the free fault and fault sensors. Then the Quadratic Support Vector Machine (QSVM) and k-Nearest Neighbor (KNN) as the classifiers are used. The test results for measuring the performance of 1232 sample classifiers from data show that the accuracy of fault-free sensor recognition is 97.4 % and 100% consecutively for these classifiers.

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A New Method of Fuzzy Support Vector Machine Algorithm for Intrusion Detection

Cited by 24 publications

References 56 publications

An intelligent ensemble of long‐short‐term memory with genetic algorithm for network anomaly identification

An intelligent ensemble of long‐short‐term memory with genetic algorithm for network anomaly identification

Data-Driven Network Intrusion Detection: A Taxonomy of Challenges and Methods

Quadratic Support Vector Machine and K-Nearest Neighbor Based Robust Sensor Fault Detection and Isolation

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