Detailed analysis of captured phishing page
Used to detect similar phishing pages based on HTML content
| Algorithm | Hash Value |
|---|---|
|
CONTENT
TLSH
|
T17D630F21730576BB017F94C0AC60BF9970C7E35AC21B41506ABDA2654FD3EA2FE095BE |
|
CONTENT
ssdeep
|
1536:T4VWKRzA0143RGdqLDa0HlrLurLygLk5Ln7LB:T4VhfcXKt4vB |
Used to detect visually similar phishing pages based on screenshots
| Algorithm | Hash Value |
|---|---|
|
VISUAL
pHash
|
8153de2df640a55b |
|
VISUAL
aHash
|
00004840033b7fff |
|
VISUAL
dHash
|
d0d2d29a92d6f300 |
|
VISUAL
wHash
|
000848ea0b3bffff |
|
VISUAL
colorHash
|
0fc00000000 |
|
VISUAL
cropResistant
|
f2e890e292a1c1c8,92d6d6d6d3f0000c,ec7cc0c286c76638,d6d2d29a9a92d6d3 |
Victim enters username and password into fake login form. Credentials are captured via JavaScript and exfiltrated to attacker's server in real-time.
Malicious code is obfuscated using 15 techniques to evade detection by security scanners and make reverse engineering more difficult.
Drainer supports multiple blockchain networks and checks for high-value tokens on each chain before executing drain operations.
Pages with identical visual appearance (based on perceptual hash)