Creation and Deployment of Data Mining-Based Intrusion Detection Systems

Campos, Marcos M.; Milenova, Boriana L.

doi:10.1109/icmla.2005.17

Cited by 16 publications

(16 citation statements)

References 17 publications

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“…In both techniques, Data Mining (DM) is used to reduce human effort and increase detection accuracy [22]. In recent years, DMbased IDS for databases have been developed [5,6,10,13,16,19,21,26,33,34,44]. Supervising user queries is also a component of IDS.…”

Section: B Reactive Data Security Solutionsmentioning

confidence: 99%

A survey on data security in data warehousing: Issues, challenges and opportunities

Santos

Bernardino

Vieira

2011

2011 IEEE EUROCON - International Conference on Computer as a Tool

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Data Warehouses (DWs) are the enterprise's most valuable assets in what concerns critical business information, making them an appealing target for malicious inside and outside attackers. Given the volume of data and the nature of DW queries, most of the existing data security solutions for databases are inefficient, consuming too many resources and introducing too much overhead in query response time, or resulting in too many false positive alarms (i.e., incorrect detection of attacks) to be checked. In this paper, we present a survey on currently available data security techniques, focusing on specific issues and requirements concerning their use in data warehousing environments. We also point out challenges and opportunities for future research work in this field.

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Section: B Reactive Data Security Solutionsmentioning

confidence: 99%

A survey on data security in data warehousing: Issues, challenges and opportunities

Santos

Bernardino

Vieira

2011

2011 IEEE EUROCON - International Conference on Computer as a Tool

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“…Abuse identification utilizes pattern of familiar assault or weak areas of the system to detect intrusion [9]. Anomaly detection determines the deviation that is established from normally used patterns and flagged as intrusion [10]. The major challenge in intrusion detection is the identification of the hidden intrusion from a large quantity of typical communication practices [11].…”

Section: Related Workmentioning

confidence: 99%

Data Mining Based Intrusion Detection in Wireless Sensor Network

Asha¹,

Venkatesan²

2019

IJRTE

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Abstract: Intrusion detection is the one of the challenging task in wireless sensor network and prevents the system and network resources from being intrude or compromised. One of the ongoing strategies for recognizing any anomalous activities presented in a network is done by intrusion detection systems (IDS) and it becomes an essential part of defense system against attacker problems. The primary goal of our work is to study and analyze intrusion detection technique meant for improving the performance of Intrusion Detection using hybrid ANN based Clustering technique. To estimate the effectiveness of the proposed strategy, KDD CUP 99 dataset is utilized for testing and assessment. Based on the analysis, it is noticed that the proposed ANN clustering performs much better than other methods with respect to accuracy which attains an average high accuracy of 93.91%when compared with other methods.

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“…This dataset consists of selected records from the original dataset to overcome those shortcomings. In [10], this paper describes Database-centric Architecture for Intrusion Detection (DAID); a system that leverages data mining within the Oracle RDBMS to address the challenges arise when designing and implementing data mining-based intrusion detection systems. DAID offers numerous advantages in terms of scheduling capabilities, alert infrastructure, data analysis tools, security, scalability, and reliability.…”

Section: Related Workmentioning

confidence: 99%