first
first
This commit is contained in:
Huoji's
339
ai.sp
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339
ai.sp
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#pragma semicolon 1
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#pragma newdecls required
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/* SM Includes */
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#include <sourcemod>
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#include <sdktools>
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#include <sdktools_gamerules>
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#define TIME_TO_TICK(%1) (RoundToNearest((%1) / GetTickInterval()))
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#define TICK_TO_TIME(%1) ((%1) * GetTickInterval())
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#define IS_CLIENT(%1) (1 <= %1 <= MaxClients)
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#define ABS(%1) ((%1)>0 ? (%1) : -(%1))
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/* Plugin Info */
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public Plugin myinfo =
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{
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name = "Crow Aimbot Detector",
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author = "huoji",
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description = "Analyzes clients to detect aimbots",
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version = "0.0.1",
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url = "http://key08.com/"
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};
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/* Globals */
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#define AIM_ANGLE_CHANGE 15 // Max angle change that a player should snap
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#define AIM_BAN_MIN 4 // Minimum number of detections before an auto-ban is allowed
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#define AIM_MIN_DISTANCE 50.0 // Minimum distance acceptable for a detection.
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Handle g_IgnoreWeapons = INVALID_HANDLE;
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float g_fEyeAngles[MAXPLAYERS+1][64][3];
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float g_Sensitivity[MAXPLAYERS + 1];
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float g_mYaw[MAXPLAYERS + 1];
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float g_mPitch[MAXPLAYERS + 1];
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int g_iEyeIndex[MAXPLAYERS+1];
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int g_iMouse[MAXPLAYERS+1][64][2];
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int g_iAimDetections[MAXPLAYERS+1];
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int g_iAimbotBan = 0;
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int g_iMaxAngleHistory;
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int g_cheat = 0;
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bool g_bIsShoting[MAXPLAYERS+1][64];
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/* Plugin Functions */
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public void OnPluginStart()
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{
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// Store no more than 300ms worth of angle history.
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if ((g_iMaxAngleHistory = TIME_TO_TICK(0.3)) > sizeof(g_fEyeAngles[]))
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{
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g_iMaxAngleHistory = sizeof(g_fEyeAngles[]);
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}
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// Weapons to ignore when analyzing.
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g_IgnoreWeapons = CreateTrie();
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SetTrieValue(g_IgnoreWeapons, "weapon_knife", 1);
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SetTrieValue(g_IgnoreWeapons, "weapon_taser", 1);
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// Hooks.
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HookEntityOutput("trigger_teleport", "OnEndTouch", Teleport_OnEndTouch);
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HookEvent("player_spawn", Event_PlayerSpawn, EventHookMode_Post);
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HookEvent("player_death", Event_PlayerDeath, EventHookMode_Post);
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HookEventEx("cs_intermission", Event_Intermission);
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g_cheat = CreateConVar("sm_crowai_cheat", "0", "0 = no cheat , 1 = cheating.", _, true, 0.0, true, 1.0);
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PrintToServer("[CrowAi] Init Success!");
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}
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void OnFileUploadCallBack(HTTPStatus status, any value)
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{
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if (status != HTTPStatus_OK) {
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PrintToServer("文件上传失败");
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return;
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}
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PrintToServer("Upload complete");
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}
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public Action Event_Intermission(Handle event, const char[] name, bool dontBroadcast)
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{
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PrintToChatAll("比赛结束!上传反作弊数据中!");
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//上传数据
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return Plugin_Continue;
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}
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stock bool IsClientNew(int client)
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{
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// Client must be ingame.
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return IsFakeClient(client) || GetGameTime() > GetClientTime(client);
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}
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stock void ZeroVector(float vec[3])
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{
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vec[0] = vec[1] = vec[2] = 0.0;
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}
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stock bool IsVectorZero(const float vec[3])
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{
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return vec[0] == 0.0 && vec[1] == 0.0 && vec[2] == 0.0;
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}
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public void OnClientPutInServer(int client)
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{
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if (IsClientNew(client))
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{
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g_iAimDetections[client] = 0;
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Aimbot_ClearAngles(client);
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}
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}
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public void Teleport_OnEndTouch(const char[] output, int caller, int activator, float delay)
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{
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/* A client is being teleported in the map. */
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if (IS_CLIENT(activator) && IsClientConnected(activator))
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{
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Aimbot_ClearAngles(activator);
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CreateTimer(0.1 + delay, Timer_ClearAngles, GetClientUserId(activator), TIMER_FLAG_NO_MAPCHANGE);
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}
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}
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public Action Event_PlayerSpawn(Event event, const char[] name, bool dontBroadcast)
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{
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int userid = event.GetInt("userid");
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int client = GetClientOfUserId(userid);
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if (IS_CLIENT(client))
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{
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Aimbot_ClearAngles(client);
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CreateTimer(0.1, Timer_ClearAngles, userid, TIMER_FLAG_NO_MAPCHANGE);
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}
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}
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public Action Event_PlayerDeath(Event event, const char[] name, bool dontBroadcast)
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{
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char sWeapon[32], dummy;
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//GetEventString(event, "weapon", sWeapon, sizeof(sWeapon));
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event.GetString("weapon", sWeapon, sizeof(sWeapon));
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if (GetTrieValue(g_IgnoreWeapons, sWeapon, dummy))
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{
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return;
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}
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if (GameRules_GetProp("m_bWarmupPeriod") == 1)
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{
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return;
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}
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int victim = GetClientOfUserId(event.GetInt("userid"));
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int attacker = GetClientOfUserId(event.GetInt("attacker"));
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if (IS_CLIENT(attacker) && victim != attacker && IsClientInGame(attacker))
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{
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float vVictim[3], vAttacker[3];
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GetClientAbsOrigin(victim, vVictim);
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GetClientAbsOrigin(attacker, vAttacker);
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if (GetVectorDistance(vVictim, vAttacker) >= AIM_MIN_DISTANCE)
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{
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Aimbot_AnalyzeAngles(attacker);
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}
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}
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}
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public Action Timer_ClearAngles(Handle timer, any userid)
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{
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/* Delayed because the client's angles can sometimes "spin" after being teleported. */
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int client = GetClientOfUserId(userid);
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if (IS_CLIENT(client))
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{
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Aimbot_ClearAngles(client);
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}
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return Plugin_Stop;
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}
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public Action Timer_DecreaseCount(Handle timer, any userid)
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{
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/* Decrease the detection count by 1. */
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int client = GetClientOfUserId(userid);
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if (IS_CLIENT(client) && g_iAimDetections[client])
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{
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g_iAimDetections[client]--;
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}
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return Plugin_Stop;
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}
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int m_iShotsFired(int client)
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{
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return GetEntPropEnt(client, Prop_Send, "m_iShotsFired");
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}
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float m_aimPunchAngleX(int client)
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{
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float punch[3];
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GetEntPropVector(client, Prop_Send, "m_aimPunchAngle",punch);
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return punch[0];
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}
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float m_aimPunchAngleY(int client)
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{
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float punch[3];
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GetEntPropVector(client, Prop_Send, "m_aimPunchAngle",punch);
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return punch[1];
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}
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public Action OnPlayerRunCmd(int client, int& buttons, int& impulse, float vel[3], float angles[3], int& weapon, int& subtype, int& cmdnum, int& tickcount, int& seed, int mouse[2])
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{
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g_fEyeAngles[client][g_iEyeIndex[client]] = angles;
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g_iMouse[client][g_iEyeIndex[client]] = mouse;
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g_bIsShoting[client][g_iEyeIndex[client]] = false;
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if(m_iShotsFired(client) > 2)
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{
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g_fEyeAngles[client][g_iEyeIndex[client]][0] += m_aimPunchAngleX(client) * 2;
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g_fEyeAngles[client][g_iEyeIndex[client]][1] += m_aimPunchAngleY(client) * 2;
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g_bIsShoting[client][g_iEyeIndex[client]] = true;
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}
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if (++g_iEyeIndex[client] == g_iMaxAngleHistory)
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{
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g_iEyeIndex[client] = 0;
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}
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return Plugin_Continue;
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}
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public void ConVar_QueryClient(QueryCookie cookie, int client, ConVarQueryResult result, const char[] cvarName, const char[] cvarValue)
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{
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if(!IsFakeClient(client))
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{
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if(result == ConVarQuery_Okay)
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{
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if(StrEqual("sensitivity", cvarName))
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{
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g_Sensitivity[client] = StringToFloat(cvarValue);
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}else if(StrEqual("m_yaw", cvarName))
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{
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g_mYaw[client] = StringToFloat(cvarValue);
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}
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else if(StrEqual("m_pitch", cvarName))
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{
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g_mPitch[client] = StringToFloat(cvarValue);
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}
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}
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}
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}
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void Aimbot_AnalyzeAngles(int client)
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{
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if(IsFakeClient(client))
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return;
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/* Analyze the client to see if their angles snapped. */
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float vLastAngles[3], vAngles[3], fAngleDiff,Last_AngleDiff;
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int idx = g_iEyeIndex[client];
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QueryClientConVar(client, "sensitivity", ConVar_QueryClient, client);
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QueryClientConVar(client, "m_yaw", ConVar_QueryClient, client);
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QueryClientConVar(client, "m_pitch", ConVar_QueryClient, client);
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int num_of_detect = 0;
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int num_of_detect_smooth = 0;
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//PrintToChatAll("[CrowAntiCheat] 开始分析 %N",client);
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char steamid[64];
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GetClientAuthId(client, AuthId_SteamID64, steamid, sizeof(steamid));
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char path_name[PLATFORM_MAX_PATH];
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Format(path_name, sizeof(path_name), "addons\\sourcemod\\ai\\ai_%s.csv",steamid);
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bool file_exists = true;
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if(!FileExists(path_name)){
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file_exists = false;
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}
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Handle handle_file = OpenFile(path_name, "a+");
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char file_head[19999];
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if(file_exists == false){
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bool first_meme = false;
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for (int i = 0; i < g_iMaxAngleHistory; i++)
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{
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if (i == 0)
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{
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continue;
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}
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if(first_meme == false){
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first_meme = true;
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Format(file_head, sizeof(file_head), "%d_x,%d_y,%d_diff,%d_mx,%d_my",i,i,i,i,i);
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}else{
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Format(file_head, sizeof(file_head), "%s,%d_x,%d_y,%d_diff,%d_mx,%d_my",file_head,i,i,i,i,i);
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}
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}
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WriteFileLine(handle_file, "%s,is_cheat",file_head);
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//PrintToChatAll("max_angle_tick: %d",g_iMaxAngleHistory);
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}
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bool first_meme2 = false;
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char cheat_data[19999];
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for (int i = 0; i < g_iMaxAngleHistory; i++)
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{
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if (idx == g_iMaxAngleHistory)
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{
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idx = 0;
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}
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if (IsVectorZero(g_fEyeAngles[client][idx]))
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{
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break;
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}
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// Nothing to compare on the first iteration.
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if (i == 0)
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{
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vLastAngles = g_fEyeAngles[client][idx];
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idx++;
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continue;
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}
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vAngles = g_fEyeAngles[client][idx];
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fAngleDiff = GetVectorDistance(vLastAngles, vAngles);
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// If the difference is being reported higher than 180, get the 'real' value.
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if (fAngleDiff > 180)
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{
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fAngleDiff = FloatAbs(fAngleDiff - 360);
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}
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if (fAngleDiff > AIM_ANGLE_CHANGE)
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{
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PrintToChatAll("[CrowAI] %N 暴力自瞄",client);
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break;
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}
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int mouse[2];
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mouse = g_iMouse[client][idx];
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if(first_meme2 == false){
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first_meme2 = true;
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Format(cheat_data, sizeof(cheat_data), "%f,%f,%f,%d,%d",g_fEyeAngles[client][idx][0],g_fEyeAngles[client][idx][1],fAngleDiff,mouse[0],mouse[1]);
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}else{
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Format(cheat_data, sizeof(cheat_data), "%s,%f,%f,%f,%d,%d",cheat_data,g_fEyeAngles[client][idx][0],g_fEyeAngles[client][idx][1],fAngleDiff,mouse[0],mouse[1]);
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}
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vLastAngles = vAngles;
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Last_AngleDiff = fAngleDiff;
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idx++;
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}
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int is_cheat = GetConVarInt(g_cheat);
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WriteFileLine(handle_file, "%s,%d",cheat_data,is_cheat);
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//PrintToChatAll("[CrowAI] %N 分析完毕",client);
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CloseHandle(handle_file);
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}
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void Aimbot_ClearAngles(int client)
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{
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/* Clear angle history and reset the index. */
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g_iEyeIndex[client] = 0;
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for (int i = 0; i < g_iMaxAngleHistory; i++)
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{
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ZeroVector(g_fEyeAngles[client][i]);
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g_iMouse[client][i][0] = 0;
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g_iMouse[client][i][1] = 0;
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}
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}
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107
test.py
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107
test.py
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@@ -0,0 +1,107 @@
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import os
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import tensorflow as tf
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import functools
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import csv
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test_file_path = [
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r"I:\csgoserver\steamcmd\steamapps\common\Counter-Strike Global Offensive Beta - Dedicated Server\csgo\addons\sourcemod\测试数据1.csv"]
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train_file_path = [
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r"I:\csgoserver\steamcmd\steamapps\common\Counter-Strike Global Offensive Beta - Dedicated Server\csgo\addons\sourcemod\训练数据.csv"]
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csv_data = []
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csv_data_x = []
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csv_data_y = []
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csv_data_all_num = []
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csv_data_y_avg = {}
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with open(train_file_path[0], 'r') as f:
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reader = csv.reader(f)
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for row in reader:
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csv_data.append(row)
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csv_data_x = csv_data[0]
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for index in range(1, len(csv_data)):
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for zeus in range(len(csv_data[index])):
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if csv_data_x[zeus] == 'is_cheat':
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continue
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if csv_data_x[zeus] in csv_data_y_avg.keys():
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csv_data_y_avg[csv_data_x[zeus]] = float(
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csv_data_y_avg[csv_data_x[zeus]]) + float(csv_data[index][zeus])
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else:
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csv_data_y_avg[csv_data_x[zeus]] = float(csv_data[index][zeus])
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for key in csv_data_y_avg:
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csv_data_y_avg[key] = float(csv_data_y_avg[key]) / float(len(csv_data) - 1)
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print(csv_data_y_avg)
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def process_continuous_data(mean, data):
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# 标准化数据
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data = tf.cast(data, tf.float32) * 1/(2*mean)
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return tf.reshape(data, [-1, 1])
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def create_model():
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numerical_columns = []
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for feature in csv_data_y_avg.keys():
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num_col = tf.feature_column.numeric_column(
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feature, normalizer_fn=functools.partial(process_continuous_data, csv_data_y_avg[feature]))
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numerical_columns.append(num_col)
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preprocessing_layer = tf.keras.layers.DenseFeatures(numerical_columns)
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model = tf.keras.models.Sequential([
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preprocessing_layer,
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tf.keras.layers.Dense(128, activation='relu'),
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tf.keras.layers.Dense(128, activation='relu'),
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tf.keras.layers.Dense(1, activation='sigmoid'),
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])
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model.compile(optimizer='adam',
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loss=tf.losses.BinaryCrossentropy(
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from_logits=True),
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metrics=['accuracy'])
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return model
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def get_dataset(file_path):
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dataset = tf.data.experimental.make_csv_dataset(
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file_path,
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batch_size=32,
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label_name='is_cheat',
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na_value="?",
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num_epochs=1,
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ignore_errors=True)
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return dataset
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def start_predict():
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test_data = get_dataset(test_file_path)
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print(test_data)
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test_loss, test_accuracy = model.evaluate(test_data)
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predict_data = model.predict(test_data)
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print("anti-aimbot model, accuracy:{:5.2f}%".format(100 * test_accuracy))
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num_cheat = 0
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for pre, result in zip(predict_data[:20], list(test_data)[0][1][:20]):
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num_access = 100 * pre[0]
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if num_access >= 60:
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num_cheat = num_cheat + 1
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print("player is aimbot :{:5.2f}% ".format(
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num_access), " is cheat: ", ("yes" if bool(result) else "no"))
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print("result:", num_cheat)
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if len(predict_data) < 20:
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print("数据太少,无法辨别")
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if num_cheat >= (len(predict_data) / 2) - 2:
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print("此人微自瞄")
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else:
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print("此人不是微自瞄")
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model = create_model()
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model.load_weights('./save/model_weight')
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start_predict()
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while True:
|
||||
input("按任意键开始预测")
|
||||
start_predict()
|
||||
103
train.py
Normal file
103
train.py
Normal file
@@ -0,0 +1,103 @@
|
||||
import tensorflow as tf
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import functools
|
||||
import csv
|
||||
import time
|
||||
from tensorflow.keras.callbacks import TensorBoard
|
||||
|
||||
|
||||
LABEL_COLUMN = 'is_cheat'
|
||||
|
||||
# "I:\csgoserver\steamcmd\steamapps\common\Counter-Strike Global Offensive Beta - Dedicated Server\csgo\addons\sourcemod\训练数据.csv"
|
||||
train_file_path = [
|
||||
r"I:\csgoserver\steamcmd\steamapps\common\Counter-Strike Global Offensive Beta - Dedicated Server\csgo\addons\sourcemod\训练数据.csv"]
|
||||
test_file_path = [
|
||||
r"I:\csgoserver\steamcmd\steamapps\common\Counter-Strike Global Offensive Beta - Dedicated Server\csgo\addons\sourcemod\测试数据1.csv"]
|
||||
csv_data = []
|
||||
csv_data_x = []
|
||||
csv_data_y = []
|
||||
csv_data_all_num = []
|
||||
csv_data_y_avg = {}
|
||||
with open(train_file_path[0], 'r') as f:
|
||||
reader = csv.reader(f)
|
||||
for row in reader:
|
||||
csv_data.append(row)
|
||||
csv_data_x = csv_data[0]
|
||||
for index in range(1, len(csv_data)):
|
||||
for zeus in range(len(csv_data[index])):
|
||||
if csv_data_x[zeus] == 'is_cheat':
|
||||
continue
|
||||
if csv_data_x[zeus] in csv_data_y_avg.keys():
|
||||
csv_data_y_avg[csv_data_x[zeus]] = float(
|
||||
csv_data_y_avg[csv_data_x[zeus]]) + float(csv_data[index][zeus])
|
||||
|
||||
else:
|
||||
csv_data_y_avg[csv_data_x[zeus]] = float(csv_data[index][zeus])
|
||||
for key in csv_data_y_avg:
|
||||
csv_data_y_avg[key] = float(csv_data_y_avg[key]) / float(len(csv_data) - 1)
|
||||
|
||||
print(csv_data_y_avg)
|
||||
|
||||
|
||||
def get_dataset(file_path):
|
||||
dataset = tf.data.experimental.make_csv_dataset(
|
||||
file_path,
|
||||
batch_size=32,
|
||||
label_name=LABEL_COLUMN,
|
||||
na_value="?",
|
||||
num_epochs=1,
|
||||
ignore_errors=True)
|
||||
return dataset
|
||||
|
||||
|
||||
def process_continuous_data(mean, data):
|
||||
# 标准化数据
|
||||
data = tf.cast(data, tf.float32) * 1/(2*mean)
|
||||
return tf.reshape(data, [-1, 1])
|
||||
|
||||
|
||||
raw_train_data = get_dataset(train_file_path)
|
||||
raw_test_data = get_dataset(test_file_path)
|
||||
examples, labels = next(iter(raw_train_data)) # 第一个批次
|
||||
print("EXAMPLES: \n", examples, "\n")
|
||||
print("LABELS: \n", labels)
|
||||
|
||||
numerical_columns = []
|
||||
for feature in csv_data_y_avg.keys():
|
||||
num_col = tf.feature_column.numeric_column(
|
||||
feature, normalizer_fn=functools.partial(process_continuous_data, csv_data_y_avg[feature]))
|
||||
numerical_columns.append(num_col)
|
||||
|
||||
preprocessing_layer = tf.keras.layers.DenseFeatures(numerical_columns)
|
||||
model = tf.keras.Sequential([
|
||||
preprocessing_layer,
|
||||
tf.keras.layers.Dense(128, activation='relu'),
|
||||
tf.keras.layers.Dense(128, activation='relu'),
|
||||
tf.keras.layers.Dense(1, activation='sigmoid'),
|
||||
])
|
||||
|
||||
model_name = "anti_aimbot-{}".format(int(time.time()))
|
||||
TensorBoardcallback = TensorBoard(
|
||||
log_dir='logs/{}'.format(model_name),
|
||||
histogram_freq=1, batch_size=32,
|
||||
write_graph=True, write_grads=False, write_images=True,
|
||||
embeddings_freq=0, embeddings_layer_names=None,
|
||||
embeddings_metadata=None, embeddings_data=None, update_freq=500
|
||||
)
|
||||
|
||||
model.compile(
|
||||
loss='binary_crossentropy',
|
||||
optimizer='adam',
|
||||
metrics=['accuracy'])
|
||||
train_data = raw_train_data.shuffle(1500)
|
||||
test_data = raw_test_data
|
||||
history = model.fit(train_data, epochs=85, callbacks=[TensorBoardcallback])
|
||||
test_loss, test_accuracy = model.evaluate(test_data)
|
||||
predict_data = model.predict(test_data)
|
||||
print('anti-aimbot: \n\nTest Loss {}, Test Accuracy {}'.format(test_loss, test_accuracy))
|
||||
for pre, result in zip(predict_data[:20], list(test_data)[0][1][:20]):
|
||||
print("player is aimbot :{:5.2f}% ".format(
|
||||
100 * pre[0]), " is cheat: ", ("yes" if bool(result) else "no"))
|
||||
model.save_weights('./save/model_weight')
|
||||
Reference in New Issue
Block a user