Data preprocessing on input

Madrakhimov, Shavkat; Makharov, K T; Lolaev, Musulmon

doi:10.1063/5.0058132

Cited by 3 publications

(4 citation statements)

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“…One of the real ways to reduce the cost of resources for detecting attacks is the selection of informative feature sets [10]. The main goals pursued in feature selection are to achieve high accuracy and prevent retraining of algorithms [15]. When identifying selection methods, they are divided into 3 main types [16]:…”

Section: Releated Workmentioning

confidence: 99%

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Estimates of the Complexity of Detecting Types of DDOS Attacks

Ignatev¹,

Navruzov²

2022

IJC

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The problem of substantiating decisions made in the field of information security through estimates of the complexity of detecting types of DDOS attacks is considered. Estimates are a quantitative measure of a particular type of attack relative to normal network operation traffic data in its own feature space. Own space is represented by a set of informative features. To assess the complexity of detecting types of DDOS attacks, a measure of compactness by latent features on the numerical axis was used. The values of this measure were calculated as the product of intraclass similarity and interclass difference. It is shown that compactness in terms of latent features in its own space is higher than in the entire space. The values of latent features were calculated using the method of generalized estimates. According to this method, objects of normal traffic and a specific type of attack are considered as opposition to each other. An informative feature set is the result of an algorithm that uses the rules of hierarchical agglomerative grouping. At the first step, the feature with the maximum weight value is included in the set. The grouping rules apply the feature invariance property to the scales of their measurements. An analysis of the complexity of detection for 12 types of DDOS attacks is given. The power of sets of informative features ranged from 3 to 16.

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Section: Releated Workmentioning

confidence: 99%

“…When developing a model based on machine learning algorithms, it is of particular importance to decide on the composition of the feature set and objects used as input data [15], [26]. The goals pursued in the selection of informative features:…”

Section: Releated Workmentioning

confidence: 99%

Estimates of the Complexity of Detecting Types of DDOS Attacks

Ignatev¹,

Navruzov²

2022

IJC

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“…The main drawbacks of most existing approaches to anomaly detection are summarized [19], [20]. These approaches are not optimized for detecting anomalies, a consequence, these approaches are often not efficient enough, which leads to too many false alarms (when normal instances are identified as anomalies) or too many anomalies; many existing methods are limited to low-dimensional data and small data size due to their legacy algorithms.…”

mentioning

confidence: 99%

“…These approaches are not optimized for detecting anomalies, a consequence, these approaches are often not efficient enough, which leads to too many false alarms (when normal instances are identified as anomalies) or too many anomalies; many existing methods are limited to low-dimensional data and small data size due to their legacy algorithms. Based on this overview in the previous paragraphs, we compare our method with these methods regarding our statement of the task, and the main contributions of the paper in continuity [20] are: − We construct a latent feature by a simple-linear combination of two features in order to conduct logical analysis based on two features; − We propose an expert-based model which leverages the latent feature to solve the problem of logical inconsistencies; − We suggest how to utilize this model providing a general example, and we also compare the results with results of existing methods, even though many of them are suitable for another task. For example, the DBSCAN is a clustering algorithm, however, it can separate the noisy clusters too [21].…”

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confidence: 99%

Choosing allowability boundaries for describing objects in subject areas

Lolaev,

Madrakhimov,

Makharov

et al. 2024

IJ-AI

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Anomaly detection is one of the most promising problems for study and can be used as independent units and preprocessing tools before solving any fundamental data mining problems. This article proposes a method for detecting specific errors with the involvement of experts from subject areas to fill knowledge. The proposed method about outliers hypothesizes that they locate closer to logical boundaries of intervals derived from pair features, and the interval ranges vary in different domains. We construct intervals leveraging pair feature values. While forming knowledge in a specific field, a domain specialist checks the logical allowability of objects based on the range of the intervals. If the objects are logical outliers, the specialist ignores or corrects them. We offer the general algorithm for the formation of the database based on the proposed method in the form of a pseudo-code, and we provide comparison results with existing methods.

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