A Novel Approach for Cyber Threat Detection Based on Angle-Based Subspace Anomaly Detection

T, Soumya.; Revathy, S.

doi:10.1080/01969722.2022.2148509

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…The probability of a specific class is determined by an output that is closer to 1. Equations 1, 2, and 3 in the mathematical formulation [28,29] provide a description. Linear Model:…”

Section: 2lrmentioning

confidence: 99%

Enhancing intrusion detection with imbalanced data classification and feature selection in machine learning algorithms

2024

IJATEE

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Hackers have evolved at a faster rate in terms of their capabilities because of precise technological developments like networks and communication systems. These crooks are constantly looking for new ways to jeopardize the security of computer networks (CN). Intrusion detection systems (IDS) consequently automatically become important parts of a CN. An IDS is a piece of software or hardware that tracks a company's network for potential threats. Meanwhile, an IDS is capable of reacting to and reporting any fraudulent activity. Node-based or host-based IDS (HIDS) [1, 2] Network based IDS (NIDS) or distributed-based IDS (DIDS), and hybrid-based IDS (HYIDS) are the three basic categories into which IDSs fall based on the unique IDS operation concept, this classification was created. *Author for correspondenceThe primary fundamental design goals of IDS are a decrease in false positive (FP) alerts and an increase in detection accuracy. Subsequently, when designing and implementing any IDS, that perspective must be taken into consideration [3]. In currently decades, machine learning (ML)-based IDS have taken over as the industry standard. According to ML systems may now potentially learn from the past and improve. Broadly speaking, the two ML philosophies of supervised ML and unsupervised ML can be separated. Models are learned using labeled data in supervised ML [4]. Unsupervised ML uses unstructured data to train models.The multiclass and binary classification objectives are carefully taken into consideration when using supervised ML techniques in this study. The classification process occurs whenever a supervised ML technique is asked to identify a specific quality. The training data for the techniques in this configuration frequently has large sizes and high-

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“…The probability of a specific class is determined by an output that is closer to 1. Equations 1, 2, and 3 in the mathematical formulation [28,29] provide a description. Linear Model:…”

Section: 2lrmentioning

confidence: 99%

Enhancing intrusion detection with imbalanced data classification and feature selection in machine learning algorithms

2024

IJATEE

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Outlier Detection in Streaming Data for Telecommunications and Industrial Applications: A Survey

Mfondoum,

Ivanov,

Koleva

et al. 2024

Electronics

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Streaming data are present all around us. From traditional radio systems streaming audio to today’s connected end-user devices constantly sending information or accessing services, data are flowing constantly between nodes across various networks. The demand for appropriate outlier detection (OD) methods in the fields of fault detection, special events detection, and malicious activities detection and prevention is not only persistent over time but increasing, especially with the recent developments in Telecommunication systems such as Fifth Generation (5G) networks facilitating the expansion of the Internet of Things (IoT). The process of selecting a computationally efficient OD method, adapted for a specific field and accounting for the existence of empirical data, or lack thereof, is non-trivial. This paper presents a thorough survey of OD methods, categorized by the applications they are implemented in, the basic assumptions that they use according to the characteristics of the streaming data, and a summary of the emerging challenges, such as the evolving structure and nature of the data and their dimensionality and temporality. A categorization of commonly used datasets in the context of streaming data is produced to aid data source identification for researchers in this field. Based on this, guidelines for OD method selection are defined, which consider flexibility and sample size requirements and facilitate the design of such algorithms in Telecommunications and other industries.

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A Novel Approach for Cyber Threat Detection Based on Angle-Based Subspace Anomaly Detection

Cited by 2 publications

References 11 publications

Enhancing intrusion detection with imbalanced data classification and feature selection in machine learning algorithms

Enhancing intrusion detection with imbalanced data classification and feature selection in machine learning algorithms

Outlier Detection in Streaming Data for Telecommunications and Industrial Applications: A Survey

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