Distributed denial of service (DDoS) resilience in cloud: Review and conceptual cloud DDoS mitigation framework

Osanaiye, Opeyemi; Choo, Kim‐Kwang Raymond; Dlodlo, Mqhele E.

doi:10.1016/j.jnca.2016.01.001

Cited by 275 publications

(130 citation statements)

References 83 publications

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“…Reference [37] proposed a conceptual cloud DDOS change-point detection mechanism as a means to detecting and preventing DDOS attacks. The technique consists of a change point detection, a packet inter-arrival time (IAT), and a flow based classifier (FBC).…”

Section: Change-point Detection Framework In the Cloudmentioning

confidence: 99%

“…The technique is still in its conceptual stage and not practically tested but claims that by reading a packet header to determine its source and destination addresses, it will be possible to determine the packet inter-arrival time of packets from the same source and hence easy to detect any anomalies in packet transmission. A probable demerit with the approach is the possibility of high rates of false negatives and false positives [37].…”

Section: Change-point Detection Framework In the Cloudmentioning

confidence: 99%

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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: Change-point Detection Framework In the Cloudmentioning

confidence: 99%

Section: Change-point Detection Framework In the Cloudmentioning

confidence: 99%

A Survey of Cloud Computing Detection Techniques against DDoS Attacks

Alzahrani¹,

Liang²

2018

JIS

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“…However, it searches the space of all possible patterns for rules that meet the user-specified support and confidence thresholds. As an output, it generates new global actions response like rule or signature, which will be used by NIDS and Firewall [19]. A Responses Generation Algorithm may result in a large number of rules, which may turn out to be unusable or inapplicable for analysis.…”

Section: ) Responses Generation Algorithmmentioning

confidence: 99%

Collaborative Trust Framework Based on Hy IDS Firewalls and Mobile Agents to Achieve Effective Security Mechanisms in Cloud Environment

Eddaoui¹,

Marzak²,

Toumi³

et al. 2016

Fourth International Conference on Advances in Information Processing and Communication Technology - IPCT 2016

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Abstract-The cloud has emerged as a successful computing paradigm allowing users and organizations to rely on external providers for storing and processing their data and making them available to others. Undoubtedly, security is one of the significant concerns in cloud computing. However, one of the major security concerns is to protect against network intrusions that affect confidentiality, availability and integrity Cloud resources and offered services. In this paper, we present a new framework, that allows collaboration between Hybrid Intrusion Detection System (Hy-IDS), Firewalls and Mobile Agents. Deploying security mechanisms in cloud computing environment to detect and stop intrusion attempts through our framework. However, the security mechanisms unfolds in four phases: using mobile agents in a virtual environment to collect malicious data in order to detect intrusions. Then, generation of appropriate response actions local and remote from malicious data, which were collected in the first phase. In addition, dynamic deployment of response actions through virtual or physical firewall. Finally, dynamic deployment of updates in cloud computing based on mobile agents. Therefore, the collaborative framework of security management could identify and address new distributed attacks more quickly and effectively

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“…Issues related to cloud security are relevant to the various stakeholders as follows; decision-making on cloud adoption [6], resource management and service quality delivery [7,8], Distributed Denial of Service (DDoS) attacks [9][10][11], fatal attacks [12], machine learning-based cloud security [13], Threat and Risk Management (TRM) schemes [14], an integrated cloud incident handling and forensics model [15], a cross tenant access control model [16], and so on.…”

Section: Introductionmentioning

confidence: 99%

EH-GC: An Efficient and Secure Architecture of Energy Harvesting Green Cloud Infrastructure

et al. 2017

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Nowadays, the high power consumption of data centers is the biggest challenge to making cloud computing greener. Many researchers are still seeking effective solutions to reduce or harvest the energy produced at data centers. To address this challenge, we propose a green cloud infrastructure which provides security and efficiency based on energy harvesting (EH-GC). The EH-GC is basically focused on harvesting the heat energy produced by data centers in the Infrastructure-as-a-Service (IaaS) infrastructure. A pyroelectric material is used to generate the electric current from heat using the Olsen cycle. In order to achieve efficient green cloud computing, the architecture utilizes a genetic algorithm for proper virtual machine allocation, taking into consideration less Service Level Agreement (SLA) violations. The architecture utilizes Multivariate Correlation Analysis (MCA) correlation analysis based on a triangular map area generation to detect Denial of Service (DoS) attacks in the data center layer of the IaaS. Finally, the experimental analysis is explained based on the energy parameter, which proves that our model is efficient and secure, and that it efficiently reuses the energy emitted from the data center.

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Distributed denial of service (DDoS) resilience in cloud: Review and conceptual cloud DDoS mitigation framework

Cited by 275 publications

References 83 publications

A Survey of Cloud Computing Detection Techniques against DDoS Attacks

A Survey of Cloud Computing Detection Techniques against DDoS Attacks

Collaborative Trust Framework Based on Hy IDS Firewalls and Mobile Agents to Achieve Effective Security Mechanisms in Cloud Environment

EH-GC: An Efficient and Secure Architecture of Energy Harvesting Green Cloud Infrastructure

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