A Cloud Computing Based Network Monitoring and Threat Detection System for Critical Infrastructures

Chen, Zhijiang; Xu, Guobin; Mahalingam, Vivek; Ge, Linqiang; Nguyen, James; Yu, Wei; Lü, Chao

doi:10.1016/j.bdr.2015.11.002

Cited by 84 publications

(34 citation statements)

References 7 publications

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“…In the event that system component such as the memory are compromised, there is need to develop detective mechanisms using reactive defense strategies. This is possible if the system incorporates neural networks and machine learning techniques [36].…”

Section: Big Data Testbed For Detecting Network Attacksmentioning

confidence: 99%

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: Big Data Testbed For Detecting Network Attacksmentioning

confidence: 99%

A Survey of Cloud Computing Detection Techniques against DDoS Attacks

Alzahrani¹,

Liang²

2018

JIS

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“…Besides, most of the existing NSSA schemes use relational databases to store awareness data. Chen et al [6] proposed a cloud computing-based network monitoring and threat detection system, which utilized Hadoop MapReduce and Spark to process the data, and the results will be restored in the MySQL database server. The detector detected the malicious behavior of the data in the MySQL database and returned the detection results to the MySQL database.…”

Section: Introductionmentioning

confidence: 99%

Graph database-based network security situation awareness data storage method

Tao

Yang

Zhao

et al. 2018

J Wireless Com Network

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With the rapid development of the Internet, network security situation awareness has attracted tremendous attention. In large-scale complex networks, network security situation awareness data presents the characteristics of large-scale, multi-source, and heterogeneous. Recently, much research work have been done on network security situation awareness. However, most of the existing methods store different types of data in different ways, which makes data query and analysis inefficient. To solve this problem, we propose a graph database-based hierarchical multi-domain network security situation awareness data storage method. In our scheme, we build a hierarchical multi-domain network security situation awareness model to divide the network into different domains, which can collect and dispose the awareness data more efficiently. Meanwhile, to unify our storage mode, we also define network security situation awareness data storage rules and methods based on graph database. Finally, extensive experiments on real datasets show that our proposed method is efficient compared to state-of-the-art storage models.

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“…C h e n et al [2] provided the highly secured critical infrastructure system and threat monitoring system to perform functions for health and security. X u et al [3], A h m a d et al [6], G r z o n k a et al [17] [47], L i et al [40], A l k h a n a k et al [25], W a n g et al [38], B i t t e n c o u r t and M a d e i r a [31], W u et al [10] and L i et al [40] formulated and solved the task scheduling problem on cloud computing system in modern distributed environments.…”

Section: Review Of Cloud Environmentmentioning

confidence: 99%

“…Z i n e d d i n e [50] worked on solving the vulnerabilities coverage and cost optimization. Cloud providers running task C h e n et al [2] Critical infrastructure & threat monitoring system X u et al [3]  -A h m a d et al [6]  -G r z o n k a et al [17]  -P a c i n i, M a t e o s and G a r i n o [20]  -…”

Section: O G H a D D A M R M O G H A D D A M And C H E R I E T [22]mentioning

confidence: 99%

“…In 2015, C h e n et al [2] had worked on cloud security and proposed a cloud computing-based network monitoring and threat detection system using Hadoop, MapReduce, and Spark. A. K o n a k, S. K o n a k and S n y d e r [5] developed the Game-Theoretic Genetic Algorithm and compared its performance with the nested genetic algorithms for maximizing the network reliability and simultaneously reducing the reliable server assignment problem.…”

Section: Introductionmentioning

confidence: 99%

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