EKHunter: A Counter-Offensive Toolkit for Exploit Kit Infiltration

2015 ◽  
Author(s):  
Birhanu Eshete ◽  
Abeer Alhuzali ◽  
Maliheh Monshizadeh ◽  
Phillip Porras ◽  
V.N. Venkatakrishnan ◽  
...  
Keyword(s):  
Exploit Kit

scholarly journals LSTM RNN: detecting exploit kits using redirection chain sequences

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Jonah Burgess ◽  
Philip O’Kane ◽  
Sakir Sezer ◽  
Domhnall Carlin
Keyword(s):  
Cross Validation ◽  
Short Term Memory ◽  
Ground Truth ◽  
Unique Combination ◽  
Short Term ◽  
Web Based ◽  
Long Short Term Memory ◽  
Lstm Network ◽  
Exploit Kit ◽  
Fold Cross Validation

AbstractWhile consumers use the web to perform routine activities, they are under the constant threat of attack from malicious websites. Even when visiting ‘trusted’ sites, there is always a risk that site is compromised, and, hosting a malicious script. In this scenario, the injected script would typically force the victim’s browser to undergo a series of redirects before reaching an attacker-controlled domain, which, delivers the actual malware. Although these malicious redirection chains aim to frustrate detection and analysis efforts, they could be used to help identify web-based attacks. Building upon previous work, this paper presents the first known application of a Long Short-Term Memory (LSTM) network to detect Exploit Kit (EK) traffic, utilising the structure of HTTP redirects. Samples are processed as sequences, where each timestep represents a redirect and contains a unique combination of 48 features. The experiment is conducted using a ground-truth dataset of 1279 EK and 5910 benign redirection chains. Hyper-parameters are tuned via K-fold cross-validation (5f-CV), with the optimal configuration achieving an F1 score of 0.9878 against the unseen test set. Furthermore, we compare the results of isolated feature categories to assess their importance.

scholarly journals The Image Game: Exploit Kit Detection Based on Recursive Convolutional Neural Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 18808-18821 ◽  
Author(s):  
Suyeon Yoo ◽  
Sungjin Kim ◽  
Brent Byunghoon Kang
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Exploit Kit
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