Network Security Strategies in Big Data Context

Alaoui, Imane El; Gahi, Youssef

doi:10.1016/j.procs.2020.07.108

Cited by 17 publications

(7 citation statements)

References 29 publications

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“…According to literature [17], government intervention should be avoided or avoided to the greatest extent possible by relying on the support of a large number of passengers and their monopoly position. According to the literature [18], the OCH platform should be the primary focus of government oversight, with unfair standards reduced and the use of information technology strengthened to ensure the safety of OCH operations. In literature [19], through sorting out and analyzing the OCH management measures formu-lated by various cities, it is considered that it is necessary and reasonable for OCH authorities to adopt safety supervision measures.…”

Section: Related Workmentioning

confidence: 99%

Data Legal Supervision of Online Car-Hailing Platform Based on Big Data Technology and Edge Computing

Zhang¹,

Zhong²

2022

Wireless Communications and Mobile Computing

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There is a lack of fairness in market competition and disorder of order as a result of the lack of supervision. OCH (online car-hailing) driver accidents are also reported frequently, causing a slew of social and traffic safety issues. In this paper, we reconstruct the logical path of OCH platform data legal supervision in China’s big data era. We propose a big data encryption algorithm based on data redundancy technology that combines the characteristics of the ECC (Elliptic Curve Cryptography) and AES (Advanced Encryption Standard) block cipher modes in terms of computing speed, parallelism, and security. The system can process large amounts of network data and detect distributed denial of service (DDOS) attacks in real time. The observed feature change trend charts before and after the attacks show significant differences, demonstrating that the proposed features can better distinguish normal traffic from abnormal traffic.

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Section: Related Workmentioning

confidence: 99%

Data Legal Supervision of Online Car-Hailing Platform Based on Big Data Technology and Edge Computing

Zhang¹,

Zhong²

2022

Wireless Communications and Mobile Computing

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“…All modules work independently [53], communicating asynchronously via a queue system. Consequently, the software is vertically scalable and the system is configured from the administrator panel.…”

Section: System Data Life Cycle and Analysismentioning

confidence: 99%

Efficient Algorithm for Providing Live Vulnerability Assessment in Corporate Network Environment

et al. 2020

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The time gap between public announcement of a vulnerability—its detection and reporting to stakeholders—is an important factor for cybersecurity of corporate networks. A large delay preceding an elimination of a critical vulnerability presents a significant risk to the network security and increases the probability of a sustained damage. Thus, accelerating the process of vulnerability identification and prioritization helps to red the probability of a successful cyberattack. This work introduces a flexible system that collects information about all known vulnerabilities present in the system, gathers data from organizational inventory database, and finally integrates and processes all collected information. Thanks to application of parallel processing and non relational databases, the results of this process are available subject to a negligible delay. The subsequent vulnerability prioritization is performed automatically on the basis of the calculated CVSS 2.0 and 3.1 scores for all scanned assets. The environmental CVSS vector component is evaluated accurately thanks to the fact that the environmental data is imported directly from the organizational inventory database.

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“…Then using machine learning, optimization of traffic analysis becomes an intriguing topic of study. Several researchers use big data and machine learning technology to support traffic analysis, Such as [1] examine the factors influencing network security platforms in the era of big data. [2] Applying duplicate data detection and deletion, detection of missing value, and data quality analysis to the big data methodology.…”

Section: Introductionmentioning

confidence: 99%

Software-Defined Networking (SDN) Traffic Analysis Using Big Data Analytic Approach

Suartana¹,

Putra²

2022

Proceedings of the International Joint Conference on Science and Engineering 2022 (IJCSE 2022)

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Network Traffic Analysis is an essential component of network management, especially for ensuring the proper operation of large-scale networks like the Internet. Monitoring and analyzing network traffic has become increasingly difficult due to the growing complexity of Internet services and the volume of network traffic. The development of the network concept with a virtualization approach such as Software-Defined Networking provides new problems in monitoring and analyzing traffic on the network. The SDN concept that distinguishes data flow and control makes network traffic data different from conventional networks. On the other hand, applications such as traffic classification and policy making in computer networks require a real-time approach and scalability. Anomaly detection and security techniques must rapidly recognize and respond to unanticipated events while processing millions of heterogeneous events extracted from computer network traffic data. Finally, the system must gather, store, and process enormous historical data sets in preparation for analysis. Volume, Velocity, Variety, and Veracity are challenges that must be faced in managing traffic analysis. This study explains how traffic analysis on SDN networks and Big Data analytics are combined to take full advantage of the potential of network data. This study aims to discuss the extent to which traffic analysis can take advantage of the potential of Big Data technology and describe the challenges and opportunities of using Big Data and machine learning technology for traffic analysis. A prototype approach is used to build Big Data Analytics architecture and apply machine learning methods for data analysis. Based on the result big data approach can be used to classify attack traffic on SDN networks. The results of the train scores and test scores on the classification using the decision tree are as follows: the training score is: 0.998265782638848, and the Test score is: 0.9982486670135102.

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Network Security Strategies in Big Data Context

Cited by 17 publications

References 29 publications

Data Legal Supervision of Online Car-Hailing Platform Based on Big Data Technology and Edge Computing

Data Legal Supervision of Online Car-Hailing Platform Based on Big Data Technology and Edge Computing

Efficient Algorithm for Providing Live Vulnerability Assessment in Corporate Network Environment

Software-Defined Networking (SDN) Traffic Analysis Using Big Data Analytic Approach

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