Big Data Analytics framework for Peer-to-Peer Botnet detection using Random Forests

Singh, Kamaldeep; Guntuku, Sharath Chandra; Thakur, Abhishek; Hota, Chittaranjan

doi:10.1016/j.ins.2014.03.066

Cited by 247 publications

(114 citation statements)

References 22 publications

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“…The work of [42] presented an important approach to combating botnet attacks in a peer-to-peer network. Their approach included three components; a traffic sniffer that captures and preprocesses packets, a feature extraction mechanism for engendering feature sets, and a machine learning techniques provided by Mahout that offers parallel processing in building a random forest based decision tree model.…”

Section: Botnet Detection Using Big Data Analyticsmentioning

confidence: 99%

“…This implies that Hadoop's MapReduce framework is dependent on <key, value> pair [42]. Both the input and output are <key, value> pairs as presented in the formula below.…”

Section: Botnet Detection Using Big Data Analyticsmentioning

confidence: 99%

“…90% of the dataset was used as training set while 10% formed the testing set. The classifier validity was tested by comparing results of the predicted against those of the experiments using the Pearson product-moment coefficient derived by the formula below [42].…”

Section: Botnet Detection Using Big Data Analyticsmentioning

confidence: 99%

“…The Random Forest is seen to outperform all other machine learning algorithms like Naïve Bayes and SVM. The presented architecture ensures fault tolerance and dynamically adapts to various network situations [42]. The model can be applied in peer-to-peer security modules of threat detection.…”

Section: Botnet Detection Using Big Data Analyticsmentioning

confidence: 99%

See 3 more Smart Citations

A Survey of Cloud Computing Detection Techniques against DDoS Attacks

Alzahrani¹,

Liang²

2018

JIS

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A Distributed Denial of Service Attack (DDoS) is an attack in which multiple systems compromised by a Trojan are maliciously used to target a single system. The attack leads to the denial of a certain service on the target system. In a DDoS attack, both the target system and the systems used to perform the attack are all victims of the attack. The compromised systems are also called Botnets. These attacks occur on networked systems, among them the cloud computing facet. Scholars have tried coming up with separate mechanisms for detecting and preventing such attacks long before they occur. However, as technology progresses in advancement so do the attack mechanisms. In cloud computing, security issues affect various stakeholders who plan on cloud adoption. DDoS attacks are such serious concerns that require mitigation in the cloud. This paper presents a survey of the various mechanisms, both traditional and modern, that are applied in detecting cloud-based DDoS attacks.

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Section: Botnet Detection Using Big Data Analyticsmentioning

confidence: 99%

“…This implies that Hadoop's MapReduce framework is dependent on <key, value> pair [42]. Both the input and output are <key, value> pairs as presented in the formula below.…”

Section: Botnet Detection Using Big Data Analyticsmentioning

confidence: 99%

Section: Botnet Detection Using Big Data Analyticsmentioning

confidence: 99%

Section: Botnet Detection Using Big Data Analyticsmentioning

confidence: 99%

See 2 more Smart Citations

A Survey of Cloud Computing Detection Techniques against DDoS Attacks

Alzahrani¹,

Liang²

2018

JIS

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show abstract

“…For instance, real-time network traffic monitoring and analysis are dynamic and huge. Singh et al [5] combined Hadoop, Hive, and Mahout, open source big data processing tools, to create a scalable quasi-real-time intrusion detection system. They used Hive to create a distributed framework for dynamic network tracking, as well as made use of the parallel processing capabilities of Mahout to create random forests, using the machine learning approach to detect peer to peer botnet attacks.…”

Section: Introductionmentioning

confidence: 99%

A Cloud-based System Framework for Storage and Analysis on Big Data of Massive BIMs

Chen

Chang²

2015

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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In this study, a system which utilizes Cloud technology to establish a data center to store and manage multiple BIMs simultaneously has been developed. This BIM data center can not only handle the big data of massive BIMs using multiple servers in a distributed manner, but can also be accessed by using various online devices anywhere, anytime for information sharing and visualization. Traditional BIM includes only static information, such as the geometric parameters, physical properties, and spatial relations, for modeling of the physical space. In this study, BIM was extended to dynamic BIM which also includes dynamic data, such as historical records from the monitoring of the facility environment and usage, as well as the user experience parameters from continuous observation of the interaction between users and the space. Due to such an extension, dynamic BIM became a parametric model which can be used to simulate user behaviors. Regarding the applications of dynamic BIM big data, this study proposes a Cloud-based system framework to effectively retrieve required information for various applications by big data analysis based on parallel processing of large data sets.

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Modern Big Data Architectures

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Big Data Analytics framework for Peer-to-Peer Botnet detection using Random Forests

Cited by 247 publications

References 22 publications

A Survey of Cloud Computing Detection Techniques against DDoS Attacks

A Survey of Cloud Computing Detection Techniques against DDoS Attacks

A Cloud-based System Framework for Storage and Analysis on Big Data of Massive BIMs

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