Cloud-Based DDoS HTTP Attack Detection Using Covariance Matrix Approach

Aborujilah, Abdulaziz; Musa, Shahrulniza

doi:10.1155/2017/7674594

Cited by 34 publications

(21 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The final class is the traditional, incremental learning model that is being updated to detect other types of cyber-attacks. And IDTL classified with distance in the testing phase by recognizing the values of the training phase, similar to the research of Aborujilah and Musa [45] which has the same high efficiency.…”

Section: Discussionmentioning

confidence: 81%

A New Incremental Decision Tree Learning for Cyber Security based on ILDA and Mahalanobis Distance

Jaiyen¹,

Sornsuwit²

2019

View full text Add to dashboard Cite

A cyber-attack detection is currently essential for computer network protection. The fundamentals of protection are to detect cyber-attack effectively with the ability to combat it in various ways and with constant data learning such as internet traffic. With these functions, each cyber-attack can be memorized and protected effectively any time. This research will present procedures for a cyber-attack detection system Incremental Decision Tree Learning (IDTL) that use the principle through Incremental Linear Discriminant Analysis (ILDA) together with Mahalanobis distance for classification of the hierarchical tree by reducing data features that enhance classification of a variety of malicious data. The proposed model can learn a new incoming datum without involving the previous learned data and discard this datum after being learned. The results of the experiments revealed that the proposed method can improve classification accuracy as compare with other methods. They showed the highest accuracy when compared to other methods. If comparing with the effectiveness of each class, it was found that the proposed method can classify both intrusion datasets and other datasets efficiently.

show abstract

Section: Discussionmentioning

confidence: 81%

A New Incremental Decision Tree Learning for Cyber Security based on ILDA and Mahalanobis Distance

Jaiyen¹,

Sornsuwit²

2019

View full text Add to dashboard Cite

show abstract

“…Aborujilah and Musa [31] introduced cloud-based detection of HTTP DDoS by using statistical approach with the covariance matrix. e detection introduced two algorithms known as training and testing to recognise a di erent type of HTTP ooding attack based on attack behaviour.…”

Section: Recent Detection Methods For Http Ddos Attackmentioning

confidence: 99%

“…is review also found that a few researchers evaluated their proposed work by using simulation software [30][31][32]36]. On the other hand, some [2,19,28,[33][34][35] performed real experiments to evaluate their outcomes and found only one study [37] assessed their work by using simulation software and experimental works.…”

Section: Evaluation Methodmentioning

confidence: 99%

“…Based on the results reported by each author, only Sreeram and Vuppala [30] evaluated their proposed solution by using a complete matrix of detection (e.g., true positive, true negative, false positive, false negative, precision, recall, specificity, and accuracy). is review found that Aborujilah and Musa and Singh and De [31,32] only employed true and false detection matrix to assess their proposed methods. Meanwhile, other studies were uncertain as some evaluated detection rate, false positive, and accuracy, failed in providing a full detection matrix, as explained above.…”

Section: Journal Of Computer Network and Communicationsmentioning

confidence: 99%

“…e use of only one dataset as reference (e.g., benchmark and analysis) seems insufficient as HTTP DDoS attack patterns are varied. Further analysis performed [2,28,29,[31][32][33][34][35] took into account more than one dataset. Nevertheless, the used datasets were obsolete to perform comparison and analysis.…”

Section: Datasetmentioning

confidence: 99%

See 2 more Smart Citations

Review of Recent Detection Methods for HTTP DDoS Attack

Jaafar

Abdullah

Ismail

2019

Journal of Computer Networks and Communications

View full text Add to dashboard Cite

With increment in dependency on web technology, a commensurate increase has been noted in destructive attempts to disrupt the essential web technologies, hence leading to service failures. Web servers that run on Hypertext Transfer Protocol (HTTP) are exposed to denial-of-service (DoS) attacks. A sophisticated version of this attack known as distributed denial of service (DDOS) is among the most dangerous Internet attacks, with the ability to overwhelm a web server, thereby slowing it down and potentially taking it down completely. This paper reviewed 12 recent detection of DDoS attack at the application layer published between January 2014 and December 2018. A summary of each detection method is summarised in table view, along with in-depth critical analysis, for future studies to conduct research pertaining to detection of HTTP DDoS attack.

show abstract