Service-Oriented DDoS Detection Mechanism Using Pseudo State in a Flow Router

Park, PyungKoo; Yoo, SeongMin; Ryu, Ho‐Yong; Kim, Cheol Hong; Choi, Sangdun; Park, Jae-Hyung; Ryou, Jae-Cheol

doi:10.1109/icisa.2013.6579358

Cited by 3 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Park et al proposed a bidirectional flow-based detection scheme know as service-oriented DDoS detection mechanism using a pseudo state (SDM-P) [21]. SDM-P runs a bidirectional key hashing algorithm on flow routers and maintains a hashing table and pseudo state machine.…”

Section: Service-oriented Ddos Detection Mechanism Using Pseudo Statementioning

confidence: 99%

On Distributed Denial of Service Current Defense Schemes

2019

View full text Add to dashboard Cite

Distributed denial of service (DDoS) attacks are a major threat to any network-based service provider. The ability of an attacker to harness the power of a lot of compromised devices to launch an attack makes it even more complex to handle. This complexity can increase even more when several attackers coordinate to launch an attack on one victim. Moreover, attackers these days do not need to be highly skilled to perpetrate an attack. Tools for orchestrating an attack can easily be found online and require little to no knowledge about attack scripts to initiate an attack. Studies have been done severally to develop defense mechanisms to detect and defend against DDoS attacks. As defense schemes are designed and developed, attackers are also on the move to evade these defense mechanisms and so there is a need for a continual study in developing defense mechanisms. This paper discusses the current DDoS defense mechanisms, their strengths and weaknesses.

show abstract

Section: Service-oriented Ddos Detection Mechanism Using Pseudo Statementioning

confidence: 99%

On Distributed Denial of Service Current Defense Schemes

2019

View full text Add to dashboard Cite

show abstract

“…The results shows that the model using LS-SVM as classifier was able to achieve 97% accuracy, sensitivity and specificity for DDoS attack detection [11]. [13] that use four different feature selection algorithms and combine their result to attain ideal selection [14]. [13].…”

Section: A Classifier System For Ddos (Cs_ddos) To Detect Tcp Flood Ddos Attacks In Cloud Was Proposed By Amentioning

confidence: 99%

“…2.3.1. SeyyedMeysam et al [14] proposed a model that use Autoregressive Integrated Moving Average (ARIMA) [42] time series and chaotic system to detect DDoS attacks on a network. It use two traffic features; number of packets and number of source IPs at per minute interval, and form a time-series according to number of packets.…”

Section: Time-series Based / Other Modelsmentioning

confidence: 99%

“…The abnormal behavior is predicted by calculating the maximum Lyapunov exponent. The proposed model TNA [14] requires number of packets and sender IPs as input, and classification process consists of (i) Features selection (ii) pre-processing data (iii) predictions using ARIMA (iv) Analyzing chaotic errors (v) classification of chaotic and non-chaotic (vi) and detection of system state i.e. normal or under attack.…”

Section: Time-series Based / Other Modelsmentioning

confidence: 99%

“…The results shows that O. Osanaiye et al[13] has achieved the highest detection rate 99.56% by utilizing the learning based model and ensemble-based feature selection methods, which is the result of features selected by four different algorithms. Another closer model proposed by SeyyedMeysam et al[14] which detects the 99.5% attacks accurately by using time-series model to analyze the cloud traffic. Xinlei Ma et al[18] achieved 98.56% accuracy to malicious traffic by analyzing the traffic behavior using entropy-based model.…”

mentioning

confidence: 99%

See 2 more Smart Citations

DDOS Attack Detection Strategies in Cloud A Comparative Stud

Alyas¹,

Noor²,

Hassan³

2017

VFAST trans. softw. eng.

View full text Add to dashboard Cite

Cloud is known as a highly-available platform that has become most popular among businesses for all information technology needs. Being a widely used platform, it’s also a hot target for cyber-attacks. Distributed Denial of Services (DDoS) is a great threat to a cloud in which cloud bandwidth, resources, and applications are attacked to cause service unavailability. In a DDoS attack, multiple botnets attack victim using spoofed IPs with a huge number of requests to a server. Since its discovery in 1980, numerous methods have been proposed for detection and prevention of network anomalies. This study provides a background of DDoS attack detection methods in past decade and a survey of some of the latest proposed strategies to detect DDoS attacks in the cloud, the methods are further compared for their detection accuracy.

show abstract

Implementation and Validation of the Web DDoS Shelter System(WDSS)

Park¹,

Kim²

2015

KIPS Transactions on Computer and Communication Systems

View full text Add to dashboard Cite

Service-Oriented DDoS Detection Mechanism Using Pseudo State in a Flow Router

Cited by 3 publications

References 11 publications

On Distributed Denial of Service Current Defense Schemes

On Distributed Denial of Service Current Defense Schemes

DDOS Attack Detection Strategies in Cloud A Comparative Stud

Implementation and Validation of the Web DDoS Shelter System(WDSS)

Contact Info

Product

Resources

About