Network based intrusion detection: A review of technologies

Richards, Kevin

doi:10.1016/s0167-4048(99)80131-x

Cited by 21 publications

(7 citation statements)

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“…The phenomenon of extracting required and needed data (knowledge), like patterns, association, changes, and significant structures a data base which is in the large form 2.2 Areas and fields  Emerging data mining applications in bioinformatics, engineering, and medicine  Image analysis [22].  Noise detection and cleansing in large, distributed data environments  Ontology-based information extraction and knowledge discovery  Pattern discovery in data streams  Pattern matching and mining…”

Section: Definition Of Data Miningmentioning

confidence: 99%

Data Mining based Soft Computing Skills towards Prevention of Cyber Crimes on the Web

Sara¹

2015

IJRITCC

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Section: Definition Of Data Miningmentioning

confidence: 99%

Data Mining based Soft Computing Skills towards Prevention of Cyber Crimes on the Web

Sara¹

2015

IJRITCC

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“…Intrusion Detection System (IDS) is software with functions of detecting, identifying, and responding to unauthorized or abnormal activities on a target system [3,4]. The goal of the IDS is to provide a mechanism for the detection of security violations either in real-time or batch-mode [5,6].…”

Section: Detection Systemsmentioning

confidence: 99%

A Neural Network Model for Detection Systems Based on Data Mining and False Errors

Lee

Kim

et al. 2006

Emerging Directions in Embedded and Ubiquitous Computing

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Abstract. Nowadays, computer network systems play an increasingly important role in our society. They have become the target of a wide array of malicious attacks that can turn into actual intrusions. This is the reason why computer security has become an essential concern for network administrators. Intrusions can wreak havoc on LANs. And the time and cost to repair the damage can grow to extreme proportions. Instead of using passive measures to fix and patch security holes, it is more effective to adopt proactive measures against intrusions. Recently, several IDS have been proposed and they are based on various technologies. However, these techniques, which have been used in many systems, are useful only for detecting the existing patterns of intrusion. It can not detect new patterns of intrusion. Therefore, it is necessary to develop a new technology of IDS that can find new patterns of intrusion. This paper investigates the asymmetric costs of false errors to enhance the detection systems performance. The proposed method utilizes a network model considering the cost ratio of false errors. Compared with false positive, this scheme accomplishes both security and system performance objectives. The results of our empirical experiment show that the network model provides high accuracy in detection. In addition, the simulation results show that effectiveness of probe detection can be enhanced by considering the costs of false errors.

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“…Therefore, an IDS has a role similar to that of a firewall, but it also detects internal hacking and malicious codes that the firewall cannot detect and defend against. In addition, the IDS detects and responds to unauthorized activities against target systems that are not certified [4]. Thus, an IDS is an important tool for detecting security violations in real time.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Anomaly Detection Accuracy of Host-based Intrusion Detection Systems based on Different Machine Learning Algorithms

Shin¹,

Kim²

2020

IJACSA

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Among the different host-based intrusion detection systems, an anomaly-based intrusion detection system detects attacks based on deviations from normal behavior; however, such a system has a low detection rate. Therefore, several studies have been conducted to increase the accurate detection rate of anomaly-based intrusion detection systems; recently, some of these studies involved the development of intrusion detection models using machine learning algorithms to overcome the limitations of existing anomaly-based intrusion detection methodologies as well as signature-based intrusion detection methodologies. In a similar vein, in this study, we propose a method for improving the intrusion detection accuracy of anomaly-based intrusion detection systems by applying various machine learning algorithms for classification of normal and attack data. To verify the effectiveness of the proposed intrusion detection models, we use the ADFA Linux Dataset which consists of system call traces for attacks on the latest operating systems. Further, for verification, we develop models and perform simulations for host-based intrusion detection systems based on machine learning algorithms to detect and classify anomalies using the Arena simulation tool.

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Network based intrusion detection: A review of technologies

Cited by 21 publications

References 0 publications

Data Mining based Soft Computing Skills towards Prevention of Cyber Crimes on the Web

Data Mining based Soft Computing Skills towards Prevention of Cyber Crimes on the Web

A Neural Network Model for Detection Systems Based on Data Mining and False Errors

Comparison of Anomaly Detection Accuracy of Host-based Intrusion Detection Systems based on Different Machine Learning Algorithms

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