Distributed Denial of Service (DDoS) Attack in Cloud- Assisted Wireless Body Area Networks: A Systematic Literature Review

Latif, Rabia; Abbas, Haider; Assar, Saïd

doi:10.1007/s10916-014-0128-8

Cited by 44 publications

(19 citation statements)

References 19 publications

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“…In [8], the potential risk of conducting Denial of Service (DoS) attacks in the Wireless Body Area Networks (WBAN's) is recognized. Given the limited processing and the storage resources, the authors have noticed the existence of threats targeting the availability of data in such networks, and identified a Distributed Denial of Service (DDoS) attacks as a threat of the highest risk directly affecting the availability of users' (patients') data.…”

Section: Previous Researchmentioning

confidence: 99%

Classification of Security Risks in the IoT Environment

Cvitić¹,

Vujić²,

Husnjak³

2016

DAAAM Proceedings

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The concept of Internet of Things (IoT) is based on a layered architecture. Each of the layers includes the application of a range of diverse technologies for the data transmission, processing and storage. This paper will explore the vulnerabilities and threats in IoT environment and protection methods that can be implemented within such an environment due to the hardware limitations of the existing equipment and technology used for data transfer. Based on the results of the research, classification of security risks of the architectures' particular layer, as well as security risks depending on the type of use of IoT concept will be proposed. The classification of risk will provide the opportunity to direct further research on the most vulnerable layers of the architecture and implementation of appropriate methods of protection, depending on the application of this concept. This research was conducted in order to provide accurate information for visually impaired people.

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Section: Previous Researchmentioning

confidence: 99%

Classification of Security Risks in the IoT Environment

Cvitić¹,

Vujić²,

Husnjak³

2016

DAAAM Proceedings

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“…Latif et al [50] review approaches to protect against DDoS attacks focussing on Wireless Body Area Networks (WBANs). WBANs devices are limited in computational power, available bandwidth, security and battery life.…”

Section: Systems For Defending Against Ddos In the Cloudmentioning

confidence: 99%

Intrusion Detection and Countermeasure of Virtual Cloud Systems - State of the Art and Current Challenges

Carlin¹,

Hammoudeh²,

Aldabbas³

2015

ijacsa

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Abstract-Clouds are distributed Internet-based platforms that provide highly resilient and scalable environments to be used by enterprises in a multitude of ways. Cloud computing offers enterprises technology innovation that business leaders and IT infrastructure managers can choose to apply based on how and to what extent it helps them fulfil their business requirements. It is crucial that all technical consultants have a rigorous understanding of the ramifications of cloud computing as its influence is likely to spread the complete IT landscape. Security is one of the major concerns that is of practical interest to decision makers when they are making critical strategic operational decisions. Distributed Denial of Service (DDoS) attacks are becoming more frequent and effective over the past few years, since the widely publicised DDoS attacks on the financial services industry that came to light in September and October 2012 and resurfaced in the past two years. In this paper, we introduce advanced cloud security technologies and practices as a series of concepts and technology architectures, from an industry-centric point of view. This is followed by classification of intrusion detection and prevention mechanisms that can be part of an overall strategy to help understand identify and mitigate potential DDoS attacks on business networks. The paper establishes solid coverage of security issues related to DDoS and virtualisation with a focus on structure, clarity, and well-defined blocks for mainstream cloud computing security solutions and platforms. In doing so, we aim to provide industry technologists, who may not be necessarily cloud or security experts, with an effective tool to help them understand the security implications associated with cloud adoption in their transition towards more knowledge-based systems.

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“…A detailed comparison of these techniques along with their limitations can be found in [11]. The major drawback of data mining techniques is that they are not suitable for real-time data mining of network traffic and require a huge amount of memory to store the datasets.…”

Section: Mobile Information Systemsmentioning

confidence: 99%

“…Observing the network traffic flow shows that there is no regular structure of patterns existing in the network and therefore statistical pattern identification techniques are needed. Integrating existing attack detection and defense mechanism in a resource constrained WBAN network increases the computation and communication cost [2].…”

Section: Introductionmentioning

confidence: 99%

“…Among the data mining techniques, VFDT is proved to be the most prevalent due to the simplicity and interpretability of their rules and thus considered as more appropriate for 2 Mobile Information Systems low-power sensor networks. The underlying reasons for the selection of VFDT are as follows: (1) it lightweight; that is, it does not require a dataset to be stored in memory, thus making it suitable for resource constraint WBAN; (2) it can progressively build decision tree from scratch which helps in detecting DDoS attack at any stage; (3) each time a new segment of sensor data arrives, a testing and training process is performed over it keeping the stored data up to date; (4) it does not require reading full dataset and yet adjusts decision tree according to the newly incoming and gathered statistical attributes, thus consuming less memory space; (5) it is appropriate for huge amount of nonstationary and streaming data obtained from WBAN sensors; (6) it provides a transparent learning process. These features make VFDT a suitable candidate for implementing an autonomous decision maker for DDoS attack detection in cloud-assisted WBAN.…”

Section: Introductionmentioning

confidence: 99%

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EVFDT: An Enhanced Very Fast Decision Tree Algorithm for Detecting Distributed Denial of Service Attack in Cloud-Assisted Wireless Body Area Network

Latif

Abbas

Latif

et al. 2015

Mobile Information Systems

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Due to the scattered nature of DDoS attacks and advancement of new technologies such as cloud-assisted WBAN, it becomes challenging to detect malicious activities by relying on conventional security mechanisms. The detection of such attacks demands an adaptive and incremental learning classifier capable of accurate decision making with less computation. Hence, the DDoS attack detection using existing machine learning techniques requires full data set to be stored in the memory and are not appropriate for real-time network traffic. To overcome these shortcomings, Very Fast Decision Tree (VFDT) algorithm has been proposed in the past that can handle high speed streaming data efficiently. Whilst considering the data generated by WBAN sensors, noise is an obvious aspect that severely affects the accuracy and increases false alarms. In this paper, an enhanced VFDT (EVFDT) is proposed to efficiently detect the occurrence of DDoS attack in cloud-assisted WBAN. EVFDT uses an adaptive tie-breaking threshold for node splitting. To resolve the tree size expansion under extreme noise, a lightweight iterative pruning technique is proposed. To analyze the performance of EVFDT, four metrics are evaluated: classification accuracy, tree size, time, and memory. Simulation results show that EVFDT attains significantly high detection accuracy with fewer false alarms.

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Distributed Denial of Service (DDoS) Attack in Cloud- Assisted Wireless Body Area Networks: A Systematic Literature Review

Cited by 44 publications

References 19 publications

Classification of Security Risks in the IoT Environment

Classification of Security Risks in the IoT Environment

Intrusion Detection and Countermeasure of Virtual Cloud Systems - State of the Art and Current Challenges

EVFDT: An Enhanced Very Fast Decision Tree Algorithm for Detecting Distributed Denial of Service Attack in Cloud-Assisted Wireless Body Area Network

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