Defending against Distributed Denial of Service Attacks: Issues and Challenges

Gupta, Brij B.; Joshi, R. C.; Misra, Manoj

doi:10.1080/19393550903317070

Cited by 60 publications

(32 citation statements)

References 26 publications

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“…This leads to the production of a large amount of network traffic addressed to the target host [1]. The reflectors are often used as amplifiers by sending the stream of packets to the broadcast address 4 of the reflector network and triggering reply packets from each host within their LAN. A Reflector can be any host in the Internet able to respond to IP requests (e.g., a web server that responds to TCP SYN requests) because the attacker does not PROCEEDINGS OF THE FEDCSIS.…”

Section: A Architecture Modelmentioning

confidence: 99%

“…Based on the strategy that is used to deny services, it is possible to classify them in two different categories [1]- [4], [7], [10], [13]: Bandwidth Depletion (or Brute-Force) and Resource Depletion.…”

Section: B Exploited Vulnerabilitymentioning

confidence: 99%

“…The locations used as source of attack packets can be utilized to classify DDoS attacks into two Attack Traffic Distribution categories [4], [12]: Isotropic and Non-isotropic. 1) Isotropic: In Isotropic DDoS attacks, the attacker tries to distribute as much as possible uniformly the origin of its malicious packets.…”

Section: K Attack Traffic Distributionmentioning

confidence: 99%

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Analysis of DDoS-Capable IoT Malwares

Donno¹,

Dragoni²,

Giaretta³

et al. 2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Abstract-The Internet of Things (IoT) revolution promises to make our lives easier by providing cheap and always connected smart embedded devices, which can interact on the Internet and create added values for human needs. But all that glitters is not gold. Indeed, the other side of the coin is that, from a security perspective, this IoT revolution represents a potential disaster. This plethora of IoT devices that flooded the market were very badly protected, thus an easy prey for several families of malwares that can enslave and incorporate them in very large botnets. This, eventually, brought back to the top Distributed Denial of Service (DDoS) attacks, making them more powerful and easier to achieve than ever. This paper aims at provide an up-to-date picture of DDoS attacks in the specific subject of the IoT, studying how these attacks work and considering the most common families in the IoT context, in terms of their nature and evolution through the years. It also explores the additional offensive capabilities that this arsenal of IoT malwares has available, to mine the security of Internet users and systems. We think that this up-to-date picture will be a valuable reference to the scientific community in order to take a first crucial step to tackle this urgent security issue.

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Section: A Architecture Modelmentioning

confidence: 99%

Section: B Exploited Vulnerabilitymentioning

confidence: 99%

See 1 more Smart Citation

Analysis of DDoS-Capable IoT Malwares

Donno¹,

Dragoni²,

Giaretta³

et al. 2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

View full text Add to dashboard Cite

show abstract

“…So, when a huge a number of fake request or queries come to the server, the server gets busy and the actual user request cannot be entertained in that period. Hence the denial of service occurred [25].Usually, the cloud network is a distributed system, therefore, distributed denial of service happens more often [20].…”

Section: Introductionmentioning

confidence: 99%

A Review of Techniques to Detect and Prevent Distributed Denial of Service (DDoS) Attack in Cloud Computing Environment

Sattar¹,

Shahid²,

Abbas³

2015

IJCA

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Cloud computing has become one of the most demanding services over the internet. It has gained remarkable fame for past few years.But it is under the severe threats of internet security. One of the severe threats is Distributed Denial of Service (DDoS). DDoS occurs when a huge amount of packets are sent to a server from various computers. Botnet is one of the major causes that launch DDoS attack. Botnet is actually a network of bots or zombie computers that are under the control of attacker. Several techniques are available in the literature that provides solution to avoid DDoS attack.A survey on DDoS detection and prevention techniques and their comparative analysishave been done in this paper.

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“…DDoS attacks compromise availability of the information system through various means [1,2]. One of the major challenges in defending against DDoS attacks is to accurately detect their occurrences in the first place.…”

Section: Introductionmentioning

confidence: 99%

Predicting Number of Zombies in DDoS Attacks Using Pace Regression Model

Gupta

2012

CIT

Self Cite

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A DDoS attacker attempts to disrupt a target, by flooding it with illegitimate packets which are generated from a large number of zombies, usurping its bandwidth and overtaxing it to prevent legitimate inquiries from getting through. This paper reports the evaluation results of proposed approach that is used to predict number of zombies using Pace Regression Model. A relationship is established between number of zombies and observed deviation in sample entropy. Various statistical performance measures, such as R 2 , CC, SSE, MSE, RMSE, NMSE, η, MAE are used to measure the performance of the regression model. Network topologies similar to Internet used for simulation are generated using Transit-Stub model of GT-ITM topology generator. NS-2 network simulator on Linux platform is used as simulation test bed for launching DDoS attacks with varied number of zombies. The simulation results are promising as we are able to predict number of zombies efficiently using Pace Regression Model with considerably less error rate.

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Defending against Distributed Denial of Service Attacks: Issues and Challenges

Cited by 60 publications

References 26 publications

Analysis of DDoS-Capable IoT Malwares

Analysis of DDoS-Capable IoT Malwares

A Review of Techniques to Detect and Prevent Distributed Denial of Service (DDoS) Attack in Cloud Computing Environment

Predicting Number of Zombies in DDoS Attacks Using Pace Regression Model

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