Intrusion detection system using an optimized framework based on datamining techniques

Ariafar, Elham; Kiani, Rasoul

doi:10.1109/kbei.2017.8324903

Cited by 11 publications

(4 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This method is based on the classification strategies for the K-means and decision tree (DT) clustering algorithm. 24 Selvakumar B and Muneeswaran K in 2019 proposed that the reduction of dimensions shows a dynamic part in IDS, as timeconsuming process is to detect anomalies from network traffic in high-dimensionality function. Feature selection affects analytical speed and proposed research and deploys firefly algorithm-dependent filter and wrapper system in the wrapper to pick the apps.…”

Section: Related Workmentioning

confidence: 99%

Retracted: An efficient stacking model with SRPF classifier technique for intrusion detection system

Karthikeyan

Mohanraj²,

Suresh³

et al. 2021

Int J Communication

View full text Add to dashboard Cite

Machine learning process is an important part in intrusion detection system (IDS) and plays an essential role in security information. Analysts operate IDS for the study of server logs or packets of data to identify network traffic malware. IDS automates this process with machine learning-based techniques that allow packet detection on a continuous basis without much human effort or intervention through network analyzing data. It has the following limitations on low rate of detection, false alarm rate is high, and so on, to which the conventional IDS has indicated. We proposed a stacking model that is more accurate to detect the attacks than other techniques of Random Forest, Naïve Bayes, and Decision Tree C4.5. Different researchers are exploring various techniques to classify the rules for associations. But the measure of confidence itself, as investigated in various literature, produces many times inaccurate results. So based on a recent value test called the significant rule power factor (SRPF), this paper discusses classifier. The experiments are carried out by implementation in Weka's method. Results indicate that the proposed classifier is a newly created, light weight classifier with better accuracy performance. Detection of different attacks uses NSL-KDD dataset to train and test the data in this technique. This proposed work is measured using different performance metrics to other intrusion detection models. Compared to existing methods, our proposed model offers prominent increase in detection accuracy.

show abstract

Section: Related Workmentioning

confidence: 99%

Retracted: An efficient stacking model with SRPF classifier technique for intrusion detection system

Karthikeyan

Mohanraj²,

Suresh³

et al. 2021

Int J Communication

View full text Add to dashboard Cite

show abstract

“…With hybrid IDS , first, known attacks are detected and separated by matching with the attack signatures and then among the remaining stream the unknown attacks are detected by observing deviations of such packet features from those of the normal packets. [9], DT [25], Naïve Bayes [12], SVM [21], RF [30], NN [26], etc. are mostly used by many researchers for the construction of IDS.…”

Section: Intrusion Detection Systemsmentioning

confidence: 99%

“…Ariafar et. al in [25] proposed an optimized framework for network attack detection using Kmeans and DT methods. GA is used for optimizing the parameters (value of K and number of runs) of K-means and confidence parameter of DT.…”

Section: Existing Approaches To Idsmentioning

confidence: 99%

Intrusion Detection Analytics: A Comprehensive Survey

Sameera¹,

Shashi²

2019

International Journal of Advanced Scientific Research and Management

View full text Add to dashboard Cite

Cyber threat is growing on par with the advancements in the field of co mputer technology and information age which makes Intrusion detection Systems (IDSs) to get a lot of attention now a days. IDS is an evolv ing research area in the field of cyber security, which is aimed to detect cyber-intrusions. The authors have surveyed many research papers on IDS in the resent past and the essence of their survey is presented in this paper by keeping in thought of helping research scholars in the area of IDS. This paper aims at p resenting brief description of IDS and mach ine learning approach for its imp lementation. Though lot of literature survey on IDS exist, in this paper authors attempt to present a clear picture of IDS in all aspects through their extensive survey.

show abstract

“…One such study was conducted by Ramaki et al, [14] where the authors reviewed hundreds of recently proposed solutions. Statistical methods are being used to detect events and discover previously unknown patterns using data mining approaches [15,16]. Many issues of the centralized approach like scalability and single point of failure can be solved by hierarchical structures.…”

Section: Introductionmentioning

confidence: 99%

Prioritization and Alert Fusion in Distributed IoT Sensors Using Kademlia Based Distributed Hash Tables

et al. 2020

View full text Add to dashboard Cite

Distributed intrusion detection systems (IDS) are primarily deployed across the network to monitor, detect, and report anomalies, as well as to respond in real-time. Predominantly, an IDS is equipped with a set of rules that it needs to infer to be able to perform efficient detection. However, generating false alarms is a major challenge in any IDS implementation. Additionally, the sheer number of IoT devices that generate alarms in a moderately large sensor network may be overwhelming. In order to reduce alarms, this paper contributes to the field by proposing an original framework that limits the number of generated messages without compromising detection accuracy. The primary idea is to exploit mid-level nodes called collectors where similar alerts are collected and analyzed independently. Priority is assigned to each alert and similar alert are fused to respective collectors for more informed decision making. In addition, Kademlia based Distributed Hash Table (DHT) is used for efficient alert transportation and distributed fusion of similar alerts. In order to minimize false alarm rate, event correlation is used to find similarity between events fused by different detection sensors. The framework is implemented in a fog-based environment to assess and evaluate the efficiency of the proposed system in edge network. The architecture is evaluated with the recognized DARPA 1999 dataset; the reported results show that the proposed technique reduces message generation by 62% while achieving false positive accuracy over 80%.

show abstract

Intrusion detection system using an optimized framework based on datamining techniques

Cited by 11 publications

References 15 publications

Retracted: An efficient stacking model with SRPF classifier technique for intrusion detection system

Retracted: An efficient stacking model with SRPF classifier technique for intrusion detection system

Intrusion Detection Analytics: A Comprehensive Survey

Prioritization and Alert Fusion in Distributed IoT Sensors Using Kademlia Based Distributed Hash Tables

Contact Info

Product

Resources

About