Quantifying the reflective DDoS attack capability of household IoT devices

2017 ◽  
Author(s):  
Minzhao Lyu ◽  
Dainel Sherratt ◽  
Arunan Sivanathan ◽  
Hassan Habibi Gharakheili ◽  
Adam Radford ◽  
...  
Keyword(s):  
Ddos Attack ◽  
Iot Devices

Survey on DDoS Attacks and Defense Mechanisms in Cloud and Fog Computing

2018 ◽  
Vol 10 (3) ◽  
pp. 61-83 ◽  
Author(s):  
Deepali Chaudhary ◽  
Kriti Bhushan ◽  
B.B. Gupta
Keyword(s):  
Cloud Computing ◽  
Defense Mechanisms ◽  
Low Cost ◽  
Fog Computing ◽  
Smart Devices ◽  
Cloud Environment ◽  
Security Issue ◽  
Process Data ◽  
Ddos Attack ◽  
Iot Devices

This article describes how cloud computing has emerged as a strong competitor against traditional IT platforms by offering low-cost and “pay-as-you-go” computing potential and on-demand provisioning of services. Governments, as well as organizations, have migrated their entire or most of the IT infrastructure to the cloud. With the emergence of IoT devices and big data, the amount of data forwarded to the cloud has increased to a huge extent. Therefore, the paradigm of cloud computing is no longer sufficient. Furthermore, with the growth of demand for IoT solutions in organizations, it has become essential to process data quickly, substantially and on-site. Hence, Fog computing is introduced to overcome these drawbacks of cloud computing by bringing intelligence to the edge of the network using smart devices. One major security issue related to the cloud is the DDoS attack. This article discusses in detail about the DDoS attack, cloud computing, fog computing, how DDoS affect cloud environment and how fog computing can be used in a cloud environment to solve a variety of problems.

scholarly journals Traffic flow features as metrics (TFFM): detection of application layer level DDOS attack scope of IOT traffic flows

2018 ◽  
Vol 7 (2.7) ◽  
pp. 203 ◽  
Author(s):  
Kalathiripi Rambabu ◽  
N Venkatram
Keyword(s):  
Traffic Flow ◽  
Traffic Flows ◽  
Application Layer ◽  
Continuous Growth ◽  
Loosely Coupled ◽  
Ddos Attack ◽  
Proposed Model ◽  
Flow Features ◽  
Target Network ◽  
Iot Devices

The phenomenal and continuous growth of diversified IOT (Internet of Things) dependent networks has open for security and connectivity challenges. This is due to the nature of IOT devices, loosely coupled behavior of internetworking, and heterogenic structure of the networks.  These factors are highly vulnerable to traffic flow based DDOS (distributed-denial of services) attacks. The botnets such as “mirae” noticed in recent past exploits the IoT devises and tune them to flood the traffic flow such that the target network exhaust to response to benevolent requests. Hence the contribution of this manuscript proposed a novel learning-based model that learns from the traffic flow features defined to distinguish the DDOS attack prone traffic flows and benevolent traffic flows. The performance analysis was done empirically by using the synthesized traffic flows that are high in volume and source of attacks. The values obtained for statistical metrics are evincing the significance and robustness of the proposed model

Security in IoT Devices

2021 ◽  
pp. 312-327
Author(s):  
N. JEYANTHI ◽  
Shreyansh Banthia ◽  
Akhil Sharma
Keyword(s):  
Internet Of Things ◽  
The Internet ◽  
Ddos Attacks ◽  
General Survey ◽  
Internet Infrastructure ◽  
Ddos Attack ◽  
Comprehensive View ◽  
Iot Devices

An attempt to do a comparison between the various DDoS attack types that exist by analysing them in various categories that can be formed, to provide a more comprehensive view of the problem that DDoS poses to the internet infrastructure today. Then DDoS and its relevance with respect to IoT (Internet of Things) devices are analysed where attack types have been explained and possible solutions available are analysed. This chapter does not propose any new solutions to mitigating the effects of DDoS attacks but just provides a general survey of the prevailing attack types along with analysis of the underlying structures that make these attacks possible, which would help researchers in understanding the DDoS problem better.

Survey on DDoS Attacks and Defense Mechanisms in Cloud and Fog Computing

2019 ◽  
pp. 1927-1951
Author(s):  
Deepali Chaudhary ◽  
Kriti Bhushan ◽  
B.B. Gupta
Keyword(s):  
Cloud Computing ◽  
Defense Mechanisms ◽  
Low Cost ◽  
Fog Computing ◽  
Smart Devices ◽  
Cloud Environment ◽  
Security Issue ◽  
Process Data ◽  
Ddos Attack ◽  
Iot Devices

This article describes how cloud computing has emerged as a strong competitor against traditional IT platforms by offering low-cost and “pay-as-you-go” computing potential and on-demand provisioning of services. Governments, as well as organizations, have migrated their entire or most of the IT infrastructure to the cloud. With the emergence of IoT devices and big data, the amount of data forwarded to the cloud has increased to a huge extent. Therefore, the paradigm of cloud computing is no longer sufficient. Furthermore, with the growth of demand for IoT solutions in organizations, it has become essential to process data quickly, substantially and on-site. Hence, Fog computing is introduced to overcome these drawbacks of cloud computing by bringing intelligence to the edge of the network using smart devices. One major security issue related to the cloud is the DDoS attack. This article discusses in detail about the DDoS attack, cloud computing, fog computing, how DDoS affect cloud environment and how fog computing can be used in a cloud environment to solve a variety of problems.

Security in IoT Devices

2017 ◽  
pp. 96-116
Author(s):  
N. Jeyanthi ◽  
Shreyansh Banthia ◽  
Akhil Sharma
Keyword(s):  
Internet Of Things ◽  
The Internet ◽  
Ddos Attacks ◽  
General Survey ◽  
Internet Infrastructure ◽  
Ddos Attack ◽  
Comprehensive View ◽  
Iot Devices

An attempt to do a comparison between the various DDoS attack types that exist by analysing them in various categories that can be formed, to provide a more comprehensive view of the problem that DDoS poses to the internet infrastructure today. Then DDoS and its relevance with respect to IoT (Internet of Things) devices are analysed where attack types have been explained and possible solutions available are analysed. This chapter does not propose any new solutions to mitigating the effects of DDoS attacks but just provides a general survey of the prevailing attack types along with analysis of the underlying structures that make these attacks possible, which would help researchers in understanding the DDoS problem better.

scholarly journals IFACNN: efficient DDoS attack detection based on improved firefly algorithm to optimize convolutional neural networks

2021 ◽  
Vol 19 (2) ◽  
pp. 1280-1303
Author(s):  
Jiushuang Wang ◽  
◽  
Ying Liu ◽  
Huifen Feng
Keyword(s):  
Internet Of Things ◽  
Firefly Algorithm ◽  
Denial Of Service ◽  
Attack Detection ◽  
Ddos Attacks ◽  
Detection Scheme ◽  
Ddos Attack ◽  
Improved Firefly Algorithm ◽  
Iot Devices ◽  
Ddos Attack Detection

<abstract><p>Network security has become considerably essential because of the expansion of internet of things (IoT) devices. One of the greatest hazards of today's networks is distributed denial of service (DDoS) attacks, which could destroy critical network services. Recent numerous IoT devices are unsuspectingly attacked by DDoS. To securely manage IoT equipment, researchers have introduced software-defined networks (SDN). Therefore, we propose a DDoS attack detection scheme to secure the real-time in the software-defined the internet of things (SD-IoT) environment. In this article, we utilize improved firefly algorithm to optimize the convolutional neural network (CNN), to provide detection for DDoS attacks in our proposed SD-IoT framework. Our results demonstrate that our scheme can achieve higher than 99% DDoS behavior and benign traffic detection accuracy.</p></abstract>

scholarly journals Software Defined Networking Based Protection against DDOS in IoT

2020 ◽  
Vol 9 (5) ◽  
pp. 739-745
Keyword(s):  
Internet Of Things ◽  
Software Defined Networking ◽  
Data Packets ◽  
Ddos Attack ◽  
Neuro Fuzzy ◽  
Simulation Results ◽  
Iot Devices

Safety has become enormously important with the proliferation of internet of Things(IoT) technologies. Most of the IoT devices are linked with the DDoS attack, there are many risk nowadays for IoT because of DDoS attack.The new software-defined everything(SDx) model offers a way to handle IoT devices securely. The proposed S-IOT framework consists of a pool that includes S-IoT controllers, S-IoT switches and IoT devices. A new ENeFS algorithm is proposed to identify and reduce the DDoS attack. The proposed algorithm uses neuro fuzzy instruct rule to identify the DDoS attack and the number of data packets count also considered for the identification. The simulation results shows that the proposed algorithm performs better to improve the reliability of the IoT with different and unsafe gadgets.

scholarly journals DDoS attack detection using deep learning

2021 ◽  
Vol 10 (2) ◽  
pp. 382
Author(s):  
Thapanarath Khempetch ◽  
Pongpisit Wuttidittachotti
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Denial Of Service ◽  
Attack Detection ◽  
Ddos Attacks ◽  
Ddos Attack ◽  
Attack Surface ◽  
Iot Devices ◽  
Ddos Attack Detection ◽  
Flow Of Information

<span id="docs-internal-guid-58e12f40-7fff-ea30-01f6-fbbed132b03c"><span>Nowadays, IoT devices are widely used both in daily life and in corporate and industrial environments. The use of these devices has increased dramatically and by 2030 it is estimated that their usage will rise to 125 billion devices causing enormous flow of information. It is likely that it will also increase distributed denial-of-service (DDoS) attack surface. As IoT devices have limited resources, it is impossible to add additional security structures to it. Therefore, the risk of DDoS attacks by malicious people who can take control of IoT devices, remain extremely high. In this paper, we use the CICDDoS2019 dataset as a dataset that has improved the bugs and introducing a new taxonomy for DDoS attacks, including new classification based on flows network. We propose DDoS attack detection using the deep neural network (DNN) and long short-term memory (LSTM) algorithm. Our results show that it can detect more than 99.90% of all three types of DDoS attacks. The results indicate that deep learning is another option for detecting attacks that may cause disruptions in the future.</span></span>

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