Data · dataset · 2026
Investigation of browser and web-based threats
Listed in ZivaHub and Deakin Research Online and DMU Figshare — shown once because both records carry DOI 10.17034/32634684.v1
This thesis explores the evolution of browser and web-based threats, focusing on the emerging CryptoJacking threat and the well-established Exploit Kit (EK) industry.
Description
Two ground-truth datasets are compiled for the experiments. The first is a set of 887 CryptoJacking samples comprising 11 miner families, extracted from the Alexa top 1m websites using a lightweight, static crawler and subject to manual analysis.
The second is a set of 1279 EK samples compiled from network traffic samples obtained from reputable sources, representing various public and private networks, and encompassing the entire history of EK families.<br><br>Subsequently, knowledge gained from a large-scale analysis of EK samples is used to develop REdiREKT, a system that utilises the open-source Zeek Intrusion Detection System (IDS) to map HTTP redirection chains and extract distinguishing features for machine learning (ML).
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By processing a unique combination of 9 redirection techniques, REdiREKT correctly extracted 96.52% of malicious domains from 1279 EK samples, spanning 28 families and 8 campaigns, and only failed to extract 0.7% of malicious chains. <br><br>REdiREKT extracted 12,783 domains from 5910 redirection chains when applied to the benign dataset. A range of 48 HTTP, URL, redirect, and content-based features are subsequently extracted for each node (domain) in each redirection chain and stored appropriately to ensure the intrinsic, sequential structure is maintained.
The malicious and benign features are assessed to identify common trends, as is the evolution of EK families.<br><br>Finally, the first known application of a Long Short-Term Memory (LSTM) network to detect EK traffic is presented. Samples are processed as sequences, where each timestep represents a redirect and contains a unique combination of 48 features. Hyper-parameters are tuned via 5-fold cross-validation (CV), with the optimal configuration achieving an F1 score of 0.9878 against the unseen test set.
Furthermore, isolated feature categories are contrasted to assess their importance.<br><br>
Links
Where it is published
- DOI doi.org/10.17034/32634684.v1 ↗
DOI / persistent id · from zivahub uct ac za
Catalogue records · 1
- OAI-PMH record api.figshare.com/v2/oai?verb=GetRecord&metadataPrefix=oai_dc&identifier=oai%3Af… ↗
metadata API · from zivahub uct ac za
Topics
Provenance · 3 source records, 16 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| ZivaHub | oai:figshare.com:article/32634684 | 5 d ago | JSON v1 |
| Deakin Research Online | oai:figshare.com:article/32634684 | 5 d ago | JSON v1 |
| DMU Figshare | oai:figshare.com:article/32634684 | 5 d ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
| concepts[field].anzsrc:field:440207 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['cybercrime'] |
| concepts[field].anzsrc:field:440207 | mapping · dro deakin edu au | vocabulary-mapper@1.0.0 | keywords['cybercrime'] |
| concepts[field].anzsrc:field:440207 | mapping · figshare dmu ac uk | vocabulary-mapper@1.0.0 | keywords['cybercrime'] |
| concepts[field].anzsrc:group:4611 | mapping · dro deakin edu au | vocabulary-mapper@1.0.0 | keywords['machine learning'] |
| concepts[field].anzsrc:group:4611 | mapping · figshare dmu ac uk | vocabulary-mapper@1.0.0 | keywords['machine learning'] |
| concepts[field].anzsrc:group:4611 | mapping · zivahub uct ac za | vocabulary-mapper@1.0.0 | keywords['machine learning'] |
| concepts[field].local:field:computer-science-ai | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| concepts[field].local:field:computer-science-ai | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · dro deakin edu au | connector:dro_deakin_edu_au@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| concepts[field].local:field:earth-environmental | mapping · figshare dmu ac uk | connector:figshare_dmu_ac_uk@1.0.0 | |
| description | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/description |
| license_text | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| publication_date | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | |
| title | source · zivahub uct ac za | connector:zivahub_uct_ac_za@1.0.0 | /metadata/dc/title |