Intrusion Detection Analytics: A Comprehensive Survey

Sameera, Nerella; Shashi, M.

doi:10.36282/ijasrm/4.6.2019.1279

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…IDS is a hardware or software system that observes the behavior of a computer system or a computer network to identify intrusions [2]. IDSs are of 2 types; network-based IDS and host-based IDS.…”

Section: Introductionmentioning

confidence: 99%

Clustering based Intrusion Detection System for effective Detection of known and Zero-day Attacks

Nerella Sameera,

M.Siva Jyothi,

K.Lakshmaji

et al. 2023

J. Adv. Zool.

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Developing effective security measures is the most challenging task now a days and hence calls for the development of intelligent intrusion detection systems. Most of the existing intrusion detection systems perform best at detecting known attacks but fail to detect zero-day attacks due to the lack of labeled examples. Authors in this paper, comes with a clustering-based IDS framework that can effectively detect both known and zero-day attacks by following unsupervised machine learning techniques. This research uses NSL-KDD dataset for the motive of experimentation and the experimental results exhibit best performance with an accuracy of 78%.

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Section: Introductionmentioning

confidence: 99%

Clustering based Intrusion Detection System for effective Detection of known and Zero-day Attacks

Nerella Sameera,

M.Siva Jyothi,

K.Lakshmaji

et al. 2023

J. Adv. Zool.

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show abstract

“…Existing signature-based approaches of Intrusion Detection Systems (IDSs) cannot detect zero-day attacks because of lack of sufficient labeled instances of zero-day attacks and anomaly-based approaches of IDS detect zero-day attacks but results in high False Positive Rates (FPR's) [1]. To detect zero-day attacks with high accuracies and low FPR's classifiers built on available labeled instances in one domain should be used to classify the zero-day attacks of another related domain.…”

Section: Introductionmentioning

confidence: 99%

Inductive and Transductive Transfer Learning for Zero-day Attack Detection

Sameera¹,

Bhanusri²,

Shashi³

2019

IJITEE

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Upon application of supervised machine learning techniques Intrusion Detection Systems (IDSs) are successful in detecting known attacks as they use predefined attack signatures. However, detecting zero-day attacks is challenged because of the scarcity of the labeled instances for zero-day attacks. Advanced research on IDS applies the concept of Transfer Learning (TL) to compensate the scarcity of labeled instances of zero-day attacks by making use of abundant labeled instances present in related domain(s). This paper explores the potential of Inductive and Transductive transfer learning for detecting zero-day attacks experimentally, where inductive TL deals with the presence of minimal labeled instances in the target domain and transductive TL deals with the complete absence of labeled instances in the target domain. The concept of domain adaptation with manifold alignment (DAMA) is applied in inductive TL where the variant of DAMA is proposed to handle transductive TL due to non-availability of labeled instances. NSL_KDD dataset is used for experimentation

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“…Dataset is the intrusion detection benchmark dataset. NSL-KDD consists of 43 attributes (including the class label) and 1,47,907 examples, which are based on the DARPA data set[11,12]…”

mentioning

confidence: 99%

A Review on Intrusion Detection System Based on Various Learning Techniques

Raj¹,

Jain²,

Kamble³

2021

IJAINN

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In this world of the Internet, security plays an important role as Internet users grow rapidly. Security in the network is one of the modern periods' main issues. In the last decade, the exponential growth and massive use of the Internet have enabled system security vulnerabilities a critical aspect. Intrusion detection system to track unauthorized access as well as exceptional attacks through secured networks. Several experiments on the IDS have been carried out in recent years. And to know the current state of machine learning approaches to address the issue of intrusion detection. IDS is commonly used for the detection and recognition of cyberattacks at the network and host stage, in a timely and automatic manner. This research assesses the creation of a deep neural network (DNN), a form of deep learning model as well as ELM to detect unpredictable and unpredictable cyber-attacks

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Intrusion Detection Analytics: A Comprehensive Survey

Cited by 6 publications

References 27 publications

Clustering based Intrusion Detection System for effective Detection of known and Zero-day Attacks

Clustering based Intrusion Detection System for effective Detection of known and Zero-day Attacks

Inductive and Transductive Transfer Learning for Zero-day Attack Detection

A Review on Intrusion Detection System Based on Various Learning Techniques

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