A Software Deep Packet Inspection System for Network Traffic Analysis and Anomaly Detection

Song, Wenguang; Beshley, Mykola; Przystupa, Krzysztof; Beshley, Halyna; Кочан, Орест; Pryslupskyi, Andrii; Pieniak, Daniel; Su, Jun

doi:10.3390/s20061637

Cited by 51 publications

(25 citation statements)

References 70 publications

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“…Several studies have attempted to overcome the aforementioned limitations by employing ML techniques to detect attacks. Studies like [27], [28] rely on deep packet inspection (DPI) to analyze the packet contents. Such techniques are computationally expensive since it searches through the payload for known application signatures.…”

Section: Background and Related Work A Botnet Detectionmentioning

confidence: 99%

Botnet Detection Approach Using Graph-Based Machine Learning

Alharbi

Alsubhi

2021

IEEE Access

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Detecting botnet threats has been an ongoing research endeavor. Machine Learning (ML) techniques have been widely used for botnet detection with flow-based features. The prime challenges with flow-based features are that they have high computational overhead and do not fully capture network communication patterns. Recently, graph-based ML has witnessed a dramatic increase in attention. In communication networks, graph data offers insights information about the communication patterns. In this paper, we propose a graph-based ML model for botnet detection that first considers the significance of graph features before developing a generalized model for detecting botnets based on the selected important features. We explore different feature sets selected using five filter-based feature evaluation measures derived from various theories such as consistency, correlation, and information. Two heterogeneous botnet datasets, CTU-13 and IoT-23, were used to evaluate the effectiveness of the proposed graph-based botnet detection with several supervised ML algorithms. Experiment results show that using features reduces training time and model complexity and provides high bot detection rate. Our proposed detection model detects different types of botnets families and exhibits robustness to zero-day attacks. Compared to state-of-the-art techniques flow-, and graph-based, our approach achieves higher precision and shows competitive accuracy.

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Section: Background and Related Work A Botnet Detectionmentioning

confidence: 99%

Botnet Detection Approach Using Graph-Based Machine Learning

Alharbi

Alsubhi

2021

IEEE Access

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“…As [15] referred to, case studies are helpful to introduce new theory based on empirical data. The case study method is used in many scientific works concerning the transport sector, for example [11][12][13]16,17].…”

Section: Introductionmentioning

confidence: 99%

The Impact of COVID-19 on Highway Traffic and Management: The Case Study of an Operator Perspective

Cruz

Sarmento

2021

Sustainability

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The COVID-19 pandemic created an enormous disruption to the everyday life of the modern society. Among the various urban systems, transportation services were among those that suffered the most significant impacts, particularly severe in the case of highways. This paper addresses the challenges and responses to the pandemic from a private highway operator’s perspective and from a multidisciplinary perspective. Highway operators faced two main challenges: on one hand, the need to cope with the potential disruption caused by the pandemic and a national lockdown for almost three months, the provision of road services, and the requirement to ensure the proper operation and maintenance, and on the other hand, the strong negative impact of the pandemic on levels of traffic. Our case study shows that the operator’s management response in question is essentially characterised by being a first response to short term impacts while balancing for workers health and safety, engineering and management, internal business management, and overall economic impact. Highway operators were hardly prepared for such an event and became more focused on prioritising their employees and clients’ safety to avoid service disruption. Regarding levels of traffic, the pandemic has had severe effects, although to a varying degree, depending on the different types of vehicles (heavy, light, passenger, freight, among other types of vehicles) and the location of highways (coastal vs. interior). The lessons learnt can be valuable in future disruptive events and for other highway concession operators.

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“…In some cases, some methods to subside the impact of the degradation processes in sensors were developed, but this is the exception rather than the rule [ 18 , 19 ]. Therefore, the problem of developing new sensors and new sensor materials is very topical [ 20 , 21 ], especially with respect to the concept of the Internet of Things [ 22 , 23 ]. There is a growing need to develop better and more accurate sensors because measurement accuracy also has economic [ 24 , 25 , 26 ], environmental [ 27 ] and medical effects [ 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

Energy Structure and Luminescence of CeF3 Crystals

et al. 2021

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The results of the calculation of the energy band structure and luminescent research of CeF3 crystals are presented. The existence of two 5d1 and 5d2 subbands of the conduction band genetically derived from 5d states of Ce3+ ions with different effective electron masses of 4.9 me and 0.9 me, respectively, is revealed. The large electron effective mass in the 5d1 subband facilitates the localization of electronic excitations forming the 4f-5d cerium Frenkel self-trapped excitons responsible for the CeF3 luminescence. The structure of the excitation spectra of the exciton luminescence peaked at 290 nm, and the defect luminescence at 340 nm confirms the aforementioned calculated features of the conduction band of CeF3 crystals. The peculiarities of the excitation spectra of the luminescence of CaF2:Ce crystals dependent on the cerium concentration are considered with respect to the phase formation possibility of CeF3.

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A Software Deep Packet Inspection System for Network Traffic Analysis and Anomaly Detection

Cited by 51 publications

References 70 publications

Botnet Detection Approach Using Graph-Based Machine Learning

Botnet Detection Approach Using Graph-Based Machine Learning

The Impact of COVID-19 on Highway Traffic and Management: The Case Study of an Operator Perspective

Energy Structure and Luminescence of CeF3 Crystals

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