A network attack discovery algorithm based on unbalanced sampling vehicle evolution strategy for intrusion detection

Zhang, Yongxiong; Wang, Liangming; Yi, Luxia

doi:10.1080/1206212x.2017.1397387

Cited by 3 publications

(2 citation statements)

References 8 publications

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“…Moreover, a Person Correlation Coefficient was employed to determine the optimum SOM grid size. The results showed that the system was able to outperform other algorithms in detecting accuracy and computing time . Midzic et al also proposed a hybrid intrusion detection system by employing fuzzy logic to classify data points that failed to be categorized by the SOM.…”

Section: Related Workmentioning

confidence: 99%

Unsupervised learning with hierarchical feature selection for DDoS mitigation within the ISP domain

Chambers

Barrett

2019

ETRI Journal

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A new Mirai variant found recently was equipped with a dynamic update ability, which increases the level of difficulty for DDoS mitigation. Continuous development of 5G technology and an increasing number of Internet of Things (IoT) devices connected to the network pose serious threats to cyber security. Therefore, researchers have tried to develop better DDoS mitigation systems. However, the majority of the existing models provide centralized solutions either by deploying the system with additional servers at the host site, on the cloud, or at third party locations, which may cause latency. Since Internet service providers (ISP) are links between the internet and users, deploying the defense system within the ISP domain is the panacea for delivering an efficient solution. To cope with the dynamic nature of the new DDoS attacks, we utilized an unsupervised artificial neural network to develop a hierarchical two‐layered self‐organizing map equipped with a twofold feature selection for DDoS mitigation within the ISP domain.

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Section: Related Workmentioning

confidence: 99%

Unsupervised learning with hierarchical feature selection for DDoS mitigation within the ISP domain

Chambers

Barrett

2019

ETRI Journal

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“…The coverage of a REGIONAL trade agreement is not necessarily correlated with its absolute size, while the size of trading partners determines the coverage of a regional trade agreement to a greater extent [10]. Value competition mechanism put forward new demands for the global trade and investment rules [11], it requires that the traditional border measures and market admittance problem as the core of trade policy to border measures and regulations in the integration as the core of the next generation of trade policy to shift [12,13], narrow diameter based on the traditional mode of production and trade of the trade policy and trade rules no longer apply, need a new definition of new international trade rules based on the value of competition mechanism [14], the so-called "next generation" trade policies.…”

Section: Introductionmentioning

confidence: 99%

International Trade Strategy Evolution and Pattern Discrimination under the Value Competition Mechanism

Dong¹,

Hao²

2021

BCPBM

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International trade theory generally assumes that trade is carried out in the ideal state of perfect competition market and constant return on scale, but the reality is not so. Every economy will formulate some policies to restrict or encourage trade for the sake of maximizing its own trade interests. With the acceleration of globalization, the industrial division of labor, trade benefits and competitiveness of an economy in the global economy are considered from the perspective of value competition mechanism, and the change and future trend of international trade policy under the value competition mechanism are analyzed starting from the introduction of the concept of value competition mechanism. Starting from the integration of the value competition mechanism, focusing on the increase of the added value of the whole industry, combining the technological progress, industry integration and the transformation of factor input. Value through the extension of competition mechanism and integration, the mainstream trade theory deals with the main factors and system variables into a unified multi-factor panel data model framework for empirical analysis, found on the evolution of international trade strategy and pattern identify differences, including system quality and the cost difference split effectively the technological progress, economic benefit and sustainable development and full employment, to maximize the added value of the whole value competition mechanism, and finally realize the improvement of industry international competitiveness.

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Implementation of Intrusion Detection Model for Detecting Cyberattacks Using Support Vector Machine

Ashwini

Sinha

Sabarinathan

2023

IoT, Cloud and Data Science

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A Cyber-attack is a deliberate intent to take illegal access to one’s computer and data. The ascent of the web has turned into the groundwork of the vast majority's day-to-day schedules, and online administration has raised security worries. The rising measure of information, dividing among the cloud and the clients, additionally makes an attack surface. The attack surface has likewise extended with the ascent of organizations and the rising number of individuals utilizing them. The capacity of existing discovery plans to approve the goal and the earlier acknowledgment of assaults is falling apart. In the event that no effective assurance mechanism is carried out, the web will turn out to be substantially more helpless, expanding the gamble of information spillage or hacking. The focus here is to put forward a model (IDS) that detects network intrusions or anomaly detection by classifying all the network traffic packets as non-attack (harmless) or attack (vindictive) classes and also classifying the type of malicious classes using Support Vector Machine algorithm. The machine learning algorithm Support Vector Machine works for classification as well as regression problems. Decision boundaries are usually used in Support Vector Classification (SVC). We have used two different datasets of cybersecurity, namely KDDCUP 1999 and UNSW_NB15. The proposed model has been evaluated using performance metrics, namely accuracy, precision, recall (Detection rate), and F-measure. The test results exhibit that our framework has better identification execution for various cyberattacks. This model achieves an accuracy of 99.8 percent with the KDDCUP 1999 dataset and 98.2 percent with the UNSW_NB15 dataset, and remarkable detection rates of attacks.

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A network attack discovery algorithm based on unbalanced sampling vehicle evolution strategy for intrusion detection

Cited by 3 publications

References 8 publications

Unsupervised learning with hierarchical feature selection for DDoS mitigation within the ISP domain

Unsupervised learning with hierarchical feature selection for DDoS mitigation within the ISP domain

International Trade Strategy Evolution and Pattern Discrimination under the Value Competition Mechanism

Implementation of Intrusion Detection Model for Detecting Cyberattacks Using Support Vector Machine

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