Intrusion detection using clustering of network traffic flows

Bailey, Matthew; Collins, Connor; Sinda, Matthew; Hu, Gongzhu

doi:10.1109/snpd.2017.8022786

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…In the era of digitization of data, security of the Network is the main objective because network services are enormously increasing day by day and they are easily targeted by attackers. These types of attackers are harm network as well as host machine [1]. From past decade intrusion attacks are increased, so recover from this problem there are different approaches like firewall security, encryption, control access, VPN(virtual private network) [2,3], and IDS/IPS are used.…”

Section: Introductionmentioning

confidence: 99%

Anomaly-Based Intrusion Detection System using Supervised Learning Algorithm Artificial Neural Network and Ant Colony Optimization with Feature Selection

Raj¹,

Poriye²

2020

IJEAT

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In the advent of the cyber world, all know that cyber security is randomly used research area for researchers to secure host, network, and data because of increasingly complex attacks. In the advent of anomaly-based intrusion detection system, various techniques are applied to detect intrusion on system or network. This approach attains an extreme detection rate and accuracy but there may be overhead acquired to build and training them. The objective of this paper is to detect the intrusion of a system by proposing a Data mining technique which is based on supervised learning algorithm for training dataset. Artificial neural network (ANN) and Ant Colony Optimization (ACO) with feature selection are the basics of the proposed scheme. ACO work on a population-based algorithm and is motivated by the pheromone trail laying behavior of real ants, in which NSL-KDD Cup99 Dataset is used. Empirical Results clearly explain that the proposed system can attain an overall detection rate of 88% and time complexity of 0.343 sec, which is satisfactory when compared to other anomaly-based schemes.

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Section: Introductionmentioning

confidence: 99%

Anomaly-Based Intrusion Detection System using Supervised Learning Algorithm Artificial Neural Network and Ant Colony Optimization with Feature Selection

Raj¹,

Poriye²

2020

IJEAT

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Artificial Intelligence for Security of e‐Health Systems

CHALOUF,

MEJRI,

HAMDI

2024

E‐Health Security Management

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2D2N: A Dynamic Degenerative Neural Network for Classification of Images of Live Network Data

Flanagan

Fallon

Jacob

et al. 2019

2019 16th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

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The detection of new, novel attacks on organizational networks is a problem of ever-increasing relevance in today's society. Research in the area is focused on the detection of "Zero-Day" and "Black Swan" events through the use of machine learning technologies. Where previous technologies needed a known example of malicious behavior to detect a similar event, recent advances in anomaly detection on network activity has shown promise of detecting novel attacks. In a real word environment however, novel behavior occurs relatively frequently as users utilize new software applications and new standards in networking. Changes such as these, while of notable importance to network security technicians, may not present themselves as an imminent threat to a network. This paper proposes a novel method for the detection and classification of changes in networking behavior. Through the use of a Dynamic Degenerative Neural Network (2D2N), changes in recognizable user activity are dynamically classified and stored for future reference. Through the use of a time-based entropy function, infrequent activity can be analyzed and given precedence over frequent activity. This aids in the classification of abnormal activity for fast, efficient assessment by the relevant persons in an organization. The proposed method enables the detection, classification and scoring of any and all user activity on a network. Evaluation of the proposed method is based upon live data gathered from a large, multinational organization.

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Intrusion detection using clustering of network traffic flows

Cited by 4 publications

References 13 publications

Anomaly-Based Intrusion Detection System using Supervised Learning Algorithm Artificial Neural Network and Ant Colony Optimization with Feature Selection

Anomaly-Based Intrusion Detection System using Supervised Learning Algorithm Artificial Neural Network and Ant Colony Optimization with Feature Selection

Artificial Intelligence for Security of e‐Health Systems

2D2N: A Dynamic Degenerative Neural Network for Classification of Images of Live Network Data

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