80 lines
3.9 KiB
Markdown
80 lines
3.9 KiB
Markdown
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# IW4MAdmin Anticheat
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**Initial document draft | 8.30.19**
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**IW4MAdmin** anticheat for IW4x uses data from in-game logs to track player locations, hit locations, and view angles
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to validate against a known set of play styles.
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Every hit event ocurring in the game (Damage or Kill from a gun) is captured by IW4MAdmin and analyzed.
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**Session analysis** occurs against all hit events since the player connected to the server.
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**Lifetime analysis** occurs against all hit events ever occuring for a given player.
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## Detection Types
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### Bone
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Compares the number of times a particular bone is hit against the number of hits on all other bones.
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Many rudimentary aimbots lock onto a particular player bone position such as _Head_, or _Upper Torso_.
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This detection method has the highest chance of a false positive, as non-cheaters can play abnormally
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(e.g. going for headshots, or using a weapon that unconciously changes their playstyle)
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#### Hit Location Reference
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| Number | Hit Location |
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|--------|-----------------|
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| 2 | Head |
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| 3 | Neck |
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| 4 | Upper Torso |
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| 5 | Lower Torso |
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| 6 | Upper Right Arm |
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| 7 | Upper Left Arm |
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| 8 | Lower Right Arm |
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| 9 | Lower Left Arm |
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| 10 | Right Hand |
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| 11 | Left Hand |
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| 12 | Upper Right Leg |
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| 13 | Upper Left Leg |
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| 14 | Lower Right Leg |
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| 15 | Right Foot |
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| 16 | Left Foot |
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### Chest
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Identical to *Bone* detection, except it specifically compares the ratio of Upper Torso / Lower Torso hit counts.
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It can be thought of as a focused bone detection type, as most aimbots don't aim to unusual bones such as a foot.
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As with *Bone* detection, this is prone to false positives.
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### Offset
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Compares the player angles from three snapshots. The first snapshot being the snapshot immediately before the server registered the hit for a player.
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The second snapshot is the "frame" the server registered the kill. The final snapshot is the snapshot immediately after the server registers the hit.
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The algorithm is:
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```
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let a = first snapshot angles
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let b = second snapshot angles
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let c = third snapshot angles
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offset = ((a - b) + (c - b)) - (a-c)
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```
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This detection method is very effective at detecting silent aimbots.
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Silent aimbots "fake" a shot by changing a player's view angles for a single snapshot. From a spectator's position, the single snapshot view angle altering is not visible.
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The larger "FOV" (field of view) from a silent aimbot, the more absolute difference between the three snapshots.
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Over time if the average distance between these two viewangles is higher than expected, a detection is triggered.
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False positives are very rare with this detection. However, extreme client lag can trigger a false positive in special situations.
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### Strain
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Analyzes the frequency, viewangle distance, and player distance between hits.
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The algorithm is:
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```
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let v = view angle distance between two hits
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let t = delta time (time between hits)
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let d = distance between the attacker and victim
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let s = decay over time
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strain = ((v / t) * d)^s
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```
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A high value indicates fast target switching at large distance intervals, which if done naturally, requires an extreme level of mechanical effort.
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"Rage" aimbots commonly switch to targets as fast as possible in any direction.
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### Recoil
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Compares the average of the last few snapshots of player angles to 0. Client sided no recoil prevents the view angle "Z" axis from changing.
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If the average view angle "Z" axis value remains 0, the detection is triggered.
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As of 8.30.19, there are no known false positives for this detection.
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Several weapons which do not have any recoil from the game are excluded in this detection.
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### Format
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Detection penalty reasons are as follow:
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<_detectionType_>-<_location/value_>@<_hitCount_>
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Example:
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- detectionType = Strain
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- value = 1.39
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- hitCount = 136
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Result: **Strain-1.39@136**
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This reason is only visible to logged in privileged users.
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