Security Data Collection and Data Analytics in the Internet: A Survey

Jing, Xuyang; Yan, Zheng; Pedrycz, Witold

doi:10.1109/comst.2018.2863942

Cited by 100 publications

(51 citation statements)

References 141 publications

(198 reference statements)

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“…Jing et al claimed that DDoS attacks mainly include direct DDoS attacks, reflective amplification DDoS attacks and link flooding attacks [30]. The data packets of direct DDoS attacks and reflective amplification DDoS attacks are similar, and many data packets come from the same flow.…”

Section: Traffic Casesmentioning

confidence: 99%

An Adaptive Network Data Collection System in SDN

Zhou

Yan

Liu

et al. 2020

IEEE Trans. Cogn. Commun. Netw.

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Network data collection is a vital part in the process of network monitoring, traffic billing, network management and intrusion detection. As a new kind of network architecture, Software Defined Network (SDN) provides a possibility of intelligent and adaptive network data collection with centralized control and programming. However, existing literatures lack a concrete solution to economically collect network data, while satisfying the quality of data processing and analytics. Current data collection methods are not sufficiently adaptive and intelligent in terms of network context awareness. In this paper, we propose an adaptive network data collection system in SDN by automatically selecting proper data collection nodes based on network status in a dynamic way. During data collection, network traffic is sampled by considering flow characteristics in order to effectively reduce the amount of collected data while ensuring the accuracy of later data analysis, e.g., malicious traffic detection. A series of experiments are conducted to test and verify the data collection system and show its advantages through comparison with existing works in terms of CPU/memory consumption, storage usage, flow size recovery, and threat perception.

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Section: Traffic Casesmentioning

confidence: 99%

An Adaptive Network Data Collection System in SDN

Zhou

Yan

Liu

et al. 2020

IEEE Trans. Cogn. Commun. Netw.

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“…In 2019, X. Jing et al [1] Network spasms are the main dangers for safe keeping over the web have involved special care. The honesty and interconnection of the network and the security exposures of protocols and software main to several and multilevel network spasms.…”

Section: Literature Surveymentioning

confidence: 99%

“…Now a days the internet plays a very major role in our daily life. In the present society the information is being received and misused by the people in that situation a lot of security threats arises [1]. 2 types of encryption in use, they are symmetric type and asymmetric type [4] i) Symmetric key: encryption using secret keys.…”

Section: Introductionmentioning

confidence: 99%

Internet Data Security System using AES256 Encoding Technique

Kanakaraja*,

Sahithi,

Ramyavani

et al. 2019

IJRTE

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Now a days 60% of communication are done only through internet based in this circumstance data security is very important while we are communicating through any internet based networks like cloud, servers etc. With the help of some few sensors like temperature, alcohol or gas sensor data encrypted. In AES256 standard, the sensors convert the plain text into cipher text (which is not understandable) and it is uploaded to the cloud on the transmitter side. On the receiver side we can decode the data only with the help of AES256 key and hash code which are used on the transmitter side. In between this process any person is trying to hack the data he cannot get the original data because the data is in the form of cipher text format.

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“…Although the methods to detect various attacks have been intensively researched and reported in the literature, none of them provide a thorough review on security data collection and data analytics for detecting mainstream attacks in WSNs. Some recent surveys focus on security data collection [93], [94] and data analytics in the Internet [92], Ad Hoc network [96], LTE/LTE-A network [97] and mobile phones [95], rather than WSN. Some surveys [6][7][8], [12] mainly focus on a limited number of typical attacks and lack detailed performance analysis under uniform criteria in WSN.…”

Section: Introductionmentioning

confidence: 99%

Data Collection for Security Measurement in Wireless Sensor Networks: A Survey

Xie

Yan

Yao

et al. 2019

IEEE Internet Things J.

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152

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Wireless Sensor Network (WSN) is an indispensible part of IoT that has been applied in many fields to monitor environments and collect data from surroundings. However, WSNs are highly susceptible to attacks due to its unique characteristics: large-scale, self-organization, dynamic topology and constrained resources. A number of systems have been proposed to effectively detect varieties of attacks in WSNs. However, most previous surveys on WSN attacks focus on detection methods for only one or two types of attacks and lack detailed performance analysis. Additionally, the literature lacks comprehensive studies on security-related data (in short security data) collection in WSNs. In this paper, we first provide an overview of WSNs and classify the attacks in WSNs based on protocol stack layers. For the purpose of WSN security measurement, we then research attack detection methods of eleven mainstream attacks. We extract security data that play an important role for detecting security anomaly towards security measurement. We further elaborate the advantages and disadvantages of the existing detection methods based on a number of evaluation criteria. Finally, we highlight a number of open problems in this research field based on our thorough survey and conclude this paper with possible future research directions.

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Security Data Collection and Data Analytics in the Internet: A Survey

Cited by 100 publications

References 141 publications

An Adaptive Network Data Collection System in SDN

An Adaptive Network Data Collection System in SDN

Internet Data Security System using AES256 Encoding Technique

Data Collection for Security Measurement in Wireless Sensor Networks: A Survey

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