DSpace Repository

Using Auxiliary Inputs in Deep Learning Models for Detecting DGA-based Domain Names

Show simple item record

dc.contributor.author Bhatia, Ashutosh
dc.date.accessioned 2024-10-15T08:51:31Z
dc.date.available 2024-10-15T08:51:31Z
dc.date.issued 2021
dc.identifier.uri https://ieeexplore.ieee.org/abstract/document/9333979
dc.identifier.uri http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16088
dc.description.abstract Command-and-Control (C&C) servers use Domain Generation Algorithms (DGAs) to communicate with bots for uploading malware and coordinating attacks. Manual detection methods and sinkholing fail to work against these algorithms, which can generate thousands of domain names within a short period. This creates a need for an automated and intelligent system that can detect such malicious domains. LSTM (Long Short Term Memory) is one of the most popularly used deep learning architectures for DGA detection, but it performs poorly against Dictionary Domain Generation Algorithms. This work explores the application of various machine learning techniques to this problem, including specialized approaches such as Auxiliary Loss Optimization for Hypothesis Augmentation (ALOHA), with a particular focus on their performance against Dictionary Domain Generation Algorithms. The ALOHA-LSTM model improves the accuracy of Dictionary Domain Generation Algorithms compared to the state of the art LSTM model. Improvements were observed in the case of word-based DGAs as well. Addressing this issue is of paramount importance, as they are used extensively in carrying out Distributed Denial-of-Service (DDoS) attacks. DDoS and its variants comprise one of the most significant and damaging cyber-attacks that have been carried out in the past. en_US
dc.language.iso en en_US
dc.publisher IEEE en_US
dc.subject Computer Science en_US
dc.subject ALOHA en_US
dc.subject Auxiliary Labels en_US
dc.subject Domain generations algorithms en_US
dc.subject Network security en_US
dc.title Using Auxiliary Inputs in Deep Learning Models for Detecting DGA-based Domain Names en_US
dc.type Article en_US


Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account