Detection of Illicit Network Activities Based on Multivariate Gaussian Fitting of Multi-Scale Traffic Characteristics

Rocha, Eduardo; Salvador, Paulo; Nogueira, António

doi:10.1109/icc.2011.5962651

Cited by 13 publications

(2 citation statements)

References 14 publications

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“…The statistical methods collect payload-independent variables i.e port numbers, packet length, flow start/stop timestamp and interarrival time of packets in a stream to analyze the network traffic and predict which application or protocol the traffic may belong to [5,101]. In many studies, Machine Learning (ML) algorithms are used [102][103][104] besides statistical methods [105][106][107] to classify network traffic. The studies numbered [103] and [104] use TLS header information and DNS data as well as flow metadata in the analysis of network traffic.…”

Section: Machine Learning Based Techniquementioning

confidence: 99%

Modern ağ trafiği analizi için derin paket incelemesi hakkında kapsamlı bir çalışma: sorunlar ve zorluklar

Çelebi

ÖZBİLEN

Yavanoğlu

2022

NÖHÜ Müh. Bilim. Derg.

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Deep Packet Inspection (DPI) provides full visibility into network traffic by performing detailed analysis on both packet header and packet payload. Accordingly, DPI has critical importance as it can be used in applications i.e network security or government surveillance. In this paper, we provide an extensive survey on DPI. Different from the previous studies, we try to efficiently integrate DPI techniques into network analysis mechanisms by identifying performance-limiting parameters in the analysis of modern network traffic. Analysis of the network traffic model with complex behaviors is carried out with powerful hybrid systems by combining more than one technique. Therefore, DPI methods are studied together with other techniques used in the analysis of network traffic. Security applications of DPI on Internet of Things (IoT) and Software-Defined Networking (SDN) architectures are discussed and Intrusion Detection Systems (IDS) mechanisms, in which the DPI is applied as a component of the hybrid system, are examined. In addition, methods that perform inspection of encrypted network traffic are emphasized and these methods are evaluated from the point of security, performance and functionality. Future research issues are also discussed taking into account the implementation challenges for all DPI processes.

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Section: Machine Learning Based Techniquementioning

confidence: 99%

Modern ağ trafiği analizi için derin paket incelemesi hakkında kapsamlı bir çalışma: sorunlar ve zorluklar

Çelebi

ÖZBİLEN

Yavanoğlu

2022

NÖHÜ Müh. Bilim. Derg.

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“…Another approach is the multivariate Gaussian analysis in which data are flagged as abnormal when they lie a number of standard deviations away from the mean. For instance, in [210], the authors used multivariate Gaussian analysis to detect Internet attacks and intrusions via analyzing the statistical properties of the IP traffic captured. In clustering methods such as k-means clustering, data points can be grouped into clusters based on their distance to the center of the cluster.…”

Section: B Security In Big Data Analyticsmentioning

confidence: 99%

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

et al. 2018

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The world is witnessing an unprecedented growth of cyber-physical systems (CPS), which are foreseen to revolutionize our world via creating new services and applications in a variety of sectors such as environmental monitoring, mobilehealth systems, intelligent transportation systems and so on. The information and communication technology (ICT) sector is experiencing a significant growth in data traffic, driven by the widespread usage of smartphones, tablets and video streaming, along with the significant growth of sensors deployments that are anticipated in the near future. It is expected to outstandingly increase the growth rate of raw sensed data. In this paper, we present the CPS taxonomy via providing a broad overview of data collection, storage, access, processing and analysis. Compared with other survey papers, this is the first panoramic survey on big data for CPS, where our objective is to provide a panoramic summary of different CPS aspects. Furthermore, CPS require cybersecurity to protect them against malicious attacks and unauthorized intrusion, which become a challenge with the enormous amount of data that is continuously being generated in the network. Thus, we also provide an overview of the different security solutions proposed for CPS big data storage, access and analytics. We also discuss big data meeting green challenges in the contexts of CPS.

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