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Snort_AIPreproc/cluster.c

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/*
* =====================================================================================
*
* Filename: cluster.c
*
* Description: Module for managing alarm clustering and cluter hierarchies
*
* Version: 0.1
* Created: 12/08/2010 12:43:28
* Revision: none
* Compiler: gcc
*
* Author: BlackLight (http://0x00.ath.cx), <blacklight@autistici.org>
* Licence: GNU GPL v.3
* Company: DO WHAT YOU WANT CAUSE A PIRATE IS FREE, YOU ARE A PIRATE!
*
* =====================================================================================
*/
#include "spp_ai.h"
#include <limits.h>
#include <math.h>
#include <stdio.h>
#include <unistd.h>
/** \defgroup cluster Manage the clustering of alarms
* @{ */
/** Identifier key for a cluster attribute value */
typedef struct {
int min;
int max;
} attribute_key;
/** Representation of a cluster attribute value */
typedef struct {
attribute_key key;
cluster_type type;
unsigned int count;
UT_hash_handle hh;
} attribute_value;
/** Structure containing the count of occurrences of the single alerts in the log */
typedef struct {
AI_hyperalert_key key;
unsigned int count;
UT_hash_handle hh;
} AI_alert_occurrence;
PRIVATE hierarchy_node *h_root[CLUSTER_TYPES] = { NULL };
PRIVATE AI_snort_alert *alert_log = NULL;
PRIVATE pthread_mutex_t mutex;
/**
* \brief Function that picks up the heuristic value for a clustering attribute in according to Julisch's heuristic (ACM, Vol.2, No.3, 09 2002, pag.124)
* \param type Attribute type
* \return The heuristic coefficient for that attribute, -1 if no clustering information is available for that attribute
*/
PRIVATE int
__AI_heuristic_func ( cluster_type type )
{
AI_snort_alert *alert_iterator;
attribute_key key;
attribute_value *values = NULL;
attribute_value *value = NULL;
attribute_value *found = NULL;
int max = 0;
if ( type == none || !alert_log || !h_root[type] )
return -1;
for ( alert_iterator = alert_log; alert_iterator; alert_iterator = alert_iterator->next )
{
if ( !alert_iterator->h_node[type] )
continue;
key.min = alert_iterator->h_node[type]->min_val;
key.max = alert_iterator->h_node[type]->max_val;
if ( values )
{
HASH_FIND ( hh, values, &key, sizeof ( attribute_key ), found );
}
if ( !found )
{
if ( !( value = ( attribute_value* ) malloc ( sizeof ( attribute_value )) ))
{
AI_fatal_err ( "Fatal dynamic memory allocation failure", __FILE__, __LINE__ );
}
memset ( value, 0, sizeof ( attribute_value ));
value->key = key;
value->type = type;
value->count = 1;
HASH_ADD ( hh, values, key, sizeof ( attribute_key ), value );
} else {
found->count++;
}
}
for ( value = values; value; value = ( attribute_value* ) value->hh.next )
{
if ( value->count > max )
{
max = value->count;
}
}
while ( values )
{
value = values;
HASH_DEL ( values, value );
free ( value );
}
return max;
} /* ----- end of function __AI_heuristic_func ----- */
/**
* \brief Create a new clustering hierarchy node
* \param label Label for the node
* \param min_val Minimum value for the range represented by the node
* \param max_val Maximum value for the range represented by the node
* \return The brand new node if the allocation was ok, otherwise abort the application
*/
PRIVATE hierarchy_node*
__AI_hierarchy_node_new ( char *label, int min_val, int max_val )
{
hierarchy_node *n = NULL;
if ( !( n = ( hierarchy_node* ) malloc ( sizeof ( hierarchy_node )) ))
{
AI_fatal_err ( "Fatal dynamic memory allocation failure", __FILE__, __LINE__ );
}
n->min_val = min_val;
n->max_val = max_val;
n->nchildren = 0;
n->children = NULL;
n->parent = NULL;
strncpy ( n->label, label, sizeof ( n->label ));
return n;
} /* ----- end of function __AI_hierarchy_node_new ----- */
/**
* \brief Append a node to a clustering hierarchy node
* \param parent Parent node
* \param child Child node
*/
PRIVATE void
__AI_hierarchy_node_append ( hierarchy_node *parent, hierarchy_node *child )
{
if ( !( parent->children = ( hierarchy_node** ) realloc ( parent->children, (++(parent->nchildren)) * sizeof ( hierarchy_node* )) ))
{
AI_fatal_err ( "Fatal dynamic memory allocation failure", __FILE__, __LINE__ );
}
parent->children[ parent->nchildren - 1 ] = child;
child->parent = parent;
} /* ----- end of function __AI_hierarchy_node_append ----- */
/**
* \brief Get the minimum node in a hierarchy tree that matches a certain value
* \param val Value to be matched in the range
* \param root Root of the hierarchy
* \return The minimum node that matches the value if any, NULL otherwise
*/
PRIVATE hierarchy_node*
__AI_get_min_hierarchy_node ( int val, hierarchy_node *root )
{
int i;
hierarchy_node *next = NULL;
if ( !root )
{
return NULL;
}
if ( (unsigned) val < (unsigned) root->min_val || (unsigned) val > (unsigned) root->max_val )
{
return NULL;
}
for ( i=0; i < root->nchildren && !next; i++ )
{
if ( root->children[i]->min_val <= val && root->children[i]->max_val >= val )
{
next = root->children[i];
}
}
if ( !next )
return root;
return __AI_get_min_hierarchy_node ( val, next );
} /* ----- end of function __AI_get_min_hierarchy_node ----- */
/**
* \brief Check if two alerts are semantically equal
* \param a1 First alert
* \param a2 Second alert
* \return True if they are equal, false otherwise
*/
PRIVATE bool
__AI_equal_alerts ( AI_snort_alert *a1, AI_snort_alert *a2 )
{
if ( a1->gid != a2->gid || a1->sid != a2->sid || a1->rev != a2->rev )
{
return false;
}
if ( a1->h_node[src_addr] && a2->h_node[src_addr] )
{
if ( a1->h_node[src_addr]->min_val != a2->h_node[src_addr]->min_val ||
a1->h_node[src_addr]->max_val != a2->h_node[src_addr]->max_val )
return false;
}
if ( a1->h_node[dst_addr] && a2->h_node[dst_addr] )
{
if ( a1->h_node[dst_addr]->min_val != a2->h_node[dst_addr]->min_val ||
a1->h_node[dst_addr]->max_val != a2->h_node[dst_addr]->max_val )
return false;
}
if ( a1->h_node[src_port] && a2->h_node[src_port] )
{
if ( a1->h_node[src_port]->min_val != a2->h_node[src_port]->min_val ||
a1->h_node[src_port]->max_val != a2->h_node[src_port]->max_val )
return false;
}
if ( a1->h_node[dst_port] && a2->h_node[dst_port] )
{
if ( a1->h_node[dst_port]->min_val != a2->h_node[dst_port]->min_val ||
a1->h_node[dst_port]->max_val != a2->h_node[dst_port]->max_val )
return false;
}
return true;
} /* ----- end of function __AI_equal_alerts ----- */
/**
* \brief Merge the alerts marked as equal in the log
* \param log Alert log reference
* \return The number of merged couples
*/
PRIVATE int
__AI_merge_alerts ( AI_snort_alert **log )
{
AI_snort_alert *tmp, *tmp2, *tmp3;
AI_alerts_couple *alerts_couple;
int count = 0;
for ( tmp = *log; tmp; tmp = tmp->next )
{
for ( tmp2 = *log; tmp2; )
{
if ( tmp2->next )
{
/* If the two alerts are in the same clustering time window (if a time window was defined...) */
if ( config->clusterMaxAlertInterval == 0 ||
( config->clusterMaxAlertInterval > 0 && abs ( tmp->timestamp - tmp2->next->timestamp ) <= config->clusterMaxAlertInterval ))
{
if ( tmp != tmp2->next )
{
/* If the two alerts are equal... */
if ( __AI_equal_alerts ( tmp, tmp2->next ))
{
/* If we are storing the outputs of the module to a database, save the cluster containing the two alerts */
if ( config->outdbtype != outdb_none )
{
if ( !( alerts_couple = (AI_alerts_couple*) malloc ( sizeof ( AI_alerts_couple ))))
AI_fatal_err ( "Fatal dynamic memory allocation error", __FILE__, __LINE__ );
alerts_couple->alert1 = tmp;
alerts_couple->alert2 = tmp2->next;
AI_store_cluster_to_db ( alerts_couple );
}
/* Merge the two alerts */
if ( !( tmp->grouped_alerts = ( AI_snort_alert** ) realloc ( tmp->grouped_alerts, (++(tmp->grouped_alerts_count)) * sizeof ( AI_snort_alert* ))))
AI_fatal_err ( "Fatal dynamic memory allocation error", __FILE__, __LINE__ );
tmp->grouped_alerts[ tmp->grouped_alerts_count - 1 ] = tmp2->next;
count++;
tmp3 = tmp2->next->next;
tmp2->next = tmp3;
}
}
}
tmp2 = tmp2->next;
} else
break;
}
}
return count;
} /* ----- end of function __AI_merge_alerts ----- */
/**
* \brief Get the average heterogeneity coefficient of the set of alerts
* \return The average heterogeneity coefficient of the set of alerts
*/
double
__AI_get_alerts_heterogeneity ( int *alert_count )
{
double heterogeneity = 0.0;
int distinct_count = 0;
AI_hyperalert_key key;
AI_snort_alert *alert_iterator = NULL;
AI_alert_occurrence *table = NULL,
*found = NULL;
*alert_count = 0;
for ( alert_iterator = alert_log; alert_iterator; alert_iterator = alert_iterator->next )
{
found = NULL;
*alert_count += alert_iterator->grouped_alerts_count;
key.gid = alert_iterator->gid;
key.sid = alert_iterator->sid;
key.rev = alert_iterator->rev;
HASH_FIND ( hh, table, &key, sizeof ( AI_hyperalert_key ), found );
if ( !found )
{
if ( !( found = (AI_alert_occurrence*) malloc ( sizeof ( AI_alert_occurrence ))))
AI_fatal_err ( "Fatal dynamic memory allocation error", __FILE__, __LINE__ );
found->key = key;
found->count = 1;
HASH_ADD ( hh, table, key, sizeof ( AI_hyperalert_key ), found );
} else {
found->count++;
}
}
for ( found = table; found; found = (AI_alert_occurrence*) found->hh.next )
distinct_count++;
if ( *alert_count > 0 )
heterogeneity = (double) distinct_count / (double) *alert_count;
else
heterogeneity = 0.0;
while ( table )
{
found = table;
HASH_DEL ( table, found );
free ( found );
}
return heterogeneity;
} /* ----- end of function __AI_get_alerts_heterogeneity ----- */
/**
* \brief Print the clustered alerts to a log file
* \param log Log containing the alerts
* \param fp File pointer where the alerts will be printed
*/
PRIVATE void
__AI_print_clustered_alerts ( AI_snort_alert *log, FILE *fp )
{
AI_snort_alert *tmp;
char ip[INET_ADDRSTRLEN];
char *timestamp;
for ( tmp = log; tmp; tmp = tmp->next )
{
fprintf ( fp, "[**] [%d:%d:%d] %s [**]\n", tmp->gid, tmp->sid, tmp->rev, tmp->desc );
if ( tmp->classification )
fprintf ( fp, "[Classification: %s] ", tmp->classification );
fprintf ( fp, "[Priority: %d]\n", tmp->priority );
timestamp = ctime ( &( tmp->timestamp ));
timestamp [ strlen ( timestamp ) - 1 ] = 0;
fprintf ( fp, "[Grouped alerts: %d] [Starting from: %s]\n", tmp->grouped_alerts_count, timestamp );
if ( h_root[src_addr] && tmp->h_node[src_addr] )
{
fprintf ( fp, "[%s]:", (tmp->h_node[src_addr]->label) ? tmp->h_node[src_addr]->label : "no label" );
} else {
inet_ntop ( AF_INET, &(tmp->ip_src_addr), ip, INET_ADDRSTRLEN );
fprintf ( fp, "%s:", ip );
}
if ( h_root[src_port] && tmp->h_node[src_port] )
{
fprintf ( fp, "[%s] -> ", (tmp->h_node[src_port]->label) ? tmp->h_node[src_port]->label : "no label" );
} else {
fprintf ( fp, "%d -> ", htons ( tmp->tcp_src_port ));
}
if ( h_root[dst_addr] && tmp->h_node[dst_addr] )
{
fprintf ( fp, "[%s]:", (tmp->h_node[dst_addr]->label) ? tmp->h_node[dst_addr]->label : "no label" );
} else {
inet_ntop ( AF_INET, &(tmp->ip_dst_addr), ip, INET_ADDRSTRLEN );
fprintf ( fp, "%s:", ip );
}
if ( h_root[dst_port] && tmp->h_node[dst_port] )
{
fprintf ( fp, "[%s]\n", (tmp->h_node[dst_port]->label) ? tmp->h_node[dst_port]->label : "no label" );
} else {
fprintf ( fp, "%d\n", htons ( tmp->tcp_dst_port ));
}
fprintf ( fp, "\n" );
}
} /* ----- end of function __AI_print_clustered_alerts ----- */
/**
* \brief Thread for periodically clustering the log information
*/
PRIVATE void*
__AI_cluster_thread ( void* arg )
{
AI_snort_alert *tmp;
hierarchy_node *node, *child;
cluster_type type;
cluster_type best_type;
FILE *cluster_fp;
char label[256];
int hostval;
int netval;
int minval;
int heuristic_val;
int cluster_min_size = 1;
int alert_count = 0;
int old_alert_count = 0;
int single_alerts_count = 0;
double heterogeneity = 0;
pthread_mutex_init ( &mutex, NULL );
while ( 1 )
{
/* Between an execution of the thread and the next one, sleep for alert_clustering_interval seconds */
sleep ( config->alertClusteringInterval );
/* Set the lock over the alert log until it's done with the clustering operation */
pthread_mutex_lock ( &mutex );
/* Free the current alert log and get the latest one */
if ( alert_log )
{
AI_free_alerts ( alert_log );
alert_log = NULL;
}
/* get_alerts() is a function pointer that can point to the function for getting the alerts from
* the plain alert log file or from the database. Calling it the source of the alerts is
* completely transparent to this level */
if ( !( alert_log = get_alerts() ))
{
pthread_mutex_unlock ( &mutex );
continue;
}
for ( tmp = alert_log, alert_count=0; tmp; tmp = tmp->next, alert_count++ )
{
/* If an alert has an unitialized "grouped alarms count", set its counter to 1 (it only groupes the current alert) */
if ( tmp->grouped_alerts_count == 0 )
{
tmp->grouped_alerts_count = 1;
}
/* Initialize the clustering hierarchies in the current alert */
for ( type=0; type < CLUSTER_TYPES; type++ )
{
/* If "type" is a valid clustering hierarchy but the corresponding node in the alert is not initialized, initialize it */
if ( h_root[type] && !tmp->h_node[type] )
{
switch ( type )
{
case src_addr:
case dst_addr:
netval = ( type == src_addr ) ? tmp->ip_src_addr : tmp->ip_dst_addr;
hostval = ntohl ( netval );
inet_ntop ( AF_INET, &(netval), label, INET_ADDRSTRLEN );
break;
case src_port:
case dst_port:
netval = ( type == src_port ) ? tmp->tcp_src_port : tmp->tcp_dst_port;
hostval = ntohs ( netval );
snprintf ( label, sizeof(label), "%d", hostval );
break;
default:
pthread_exit (( void* ) 0 );
return (void*) 0;
}
node = __AI_get_min_hierarchy_node ( hostval, h_root[type] );
if ( node )
{
if ( node->min_val < node->max_val )
{
child = __AI_hierarchy_node_new ( label, hostval, hostval);
__AI_hierarchy_node_append ( node, child );
node = child;
}
tmp->h_node[type] = node;
}
}
}
}
alert_count -= __AI_merge_alerts ( &alert_log );
heterogeneity = __AI_get_alerts_heterogeneity ( &single_alerts_count );
/* Get the minimum size for the clusters in function of the heterogeneity of alerts' set */
if ( heterogeneity > 0 )
cluster_min_size = (int) round ( 1/heterogeneity );
else
cluster_min_size = 1;
do
{
old_alert_count = alert_count;
minval = INT_MAX;
best_type = none;
/* Choose the best attribute to cluster using the heuristic function */
for ( type = 0; type < CLUSTER_TYPES; type++ )
{
if ( type != none && h_root[type] )
{
if (( heuristic_val = __AI_heuristic_func ( type )) > 0 && heuristic_val < minval )
{
minval = heuristic_val;
best_type = type;
}
}
}
/* For all the alerts, the corresponing clustering value is the parent of the current one in the hierarchy */
for ( tmp = alert_log; tmp; tmp = tmp->next )
{
if ( tmp->grouped_alerts_count < cluster_min_size && tmp->h_node[best_type] )
{
if ( tmp->h_node[best_type]->parent )
{
tmp->h_node[best_type] = tmp->h_node[best_type]->parent;
}
}
}
alert_count -= __AI_merge_alerts ( &alert_log );
} while ( old_alert_count != alert_count );
pthread_mutex_unlock ( &mutex );
if ( !( cluster_fp = fopen ( config->clusterfile, "w" )) )
{
pthread_exit ((void*) 0 );
return (void*) 0;
}
__AI_print_clustered_alerts ( alert_log, cluster_fp );
fclose ( cluster_fp );
}
pthread_exit ((void*) 0 );
return (void*) 0;
} /* ----- end of function AI_cluster_thread ----- */
/**
* \brief Check if a certain node's range (minimum and maximum value) are already present in a clustering hierarchy
* \param node Node to be checked
* \param root Clustering hierarchy
* \return True if 'node' is already in 'root', false otherwise
*/
PRIVATE bool
__AI_check_duplicate ( hierarchy_node *node, hierarchy_node *root )
{
int i;
if ( !node || !root )
return false;
if ( root->min_val == node->min_val && root->max_val == node->max_val )
return true;
for ( i=0; i < root->nchildren; i++ )
{
if ( __AI_check_duplicate ( node, root->children[i] ))
return true;
}
return false;
} /* ----- end of function _AI_check_duplicate ----- */
/**
* \brief Build the clustering hierarchy trees
* \param nodes Nodes containing the information about the clustering ranges
* \param n_nodes Number of nodes
*/
void
AI_hierarchies_build ( hierarchy_node **nodes, int n_nodes )
{
int i, j;
int min_range = 0;
pthread_t cluster_thread;
hierarchy_node *root = NULL;
hierarchy_node *cover = NULL;
for ( i=0; i < n_nodes; i++ )
{
switch ( nodes[i]->type )
{
case src_port:
case dst_port:
if ( !h_root[ nodes[i]->type ] )
h_root[ nodes[i]->type ] = __AI_hierarchy_node_new ( "1-65535", 1, 65535 );
min_range = 65534;
break;
case src_addr:
case dst_addr:
if ( !h_root[ nodes[i]->type ] )
h_root[ nodes[i]->type ] = __AI_hierarchy_node_new ( "0.0.0.0/0", 0x0, 0xffffffff );
min_range = 0xffffffff;
break;
/* TODO Manage ranges for timestamps (and something more?) */
default:
return;
}
root = h_root[ nodes[i]->type ];
cover = NULL;
if ( __AI_check_duplicate ( nodes[i], root ))
{
AI_fatal_err ( "Parse error: duplicate cluster range in module configuration", __FILE__, __LINE__ );
}
for ( j=0; j < n_nodes; j++ )
{
if ( i != j )
{
if ( (unsigned) nodes[j]->min_val <= (unsigned) nodes[i]->min_val &&
(unsigned) nodes[j]->max_val >= (unsigned) nodes[i]->max_val )
{
if (( (unsigned) nodes[i]->min_val - (unsigned) nodes[j]->min_val +
(unsigned) nodes[j]->max_val - (unsigned) nodes[i]->max_val ) <= min_range )
{
cover = nodes[j];
min_range = nodes[i]->min_val - nodes[j]->min_val +
nodes[j]->max_val - nodes[i]->max_val;
}
}
}
}
if ( cover )
{
__AI_hierarchy_node_append ( cover, nodes[i] );
} else {
if ( (unsigned) nodes[i]->min_val >= (unsigned) root->min_val && (unsigned) nodes[i]->max_val <= (unsigned) root->max_val &&
( (unsigned) nodes[i]->min_val != (unsigned) root->min_val || (unsigned) nodes[i]->max_val != (unsigned) root->max_val ))
{
__AI_hierarchy_node_append ( root, nodes[i] );
}
}
}
if ( pthread_create ( &cluster_thread, NULL, __AI_cluster_thread, NULL ) != 0 )
{
AI_fatal_err ( "Failed to create the hash cleanup thread", __FILE__, __LINE__ );
}
} /* ----- end of function AI_hierarchies_build ----- */
/**
* \brief Return a copy of the clustered alerts
* \return An AI_snort_alert pointer identifying the list of clustered alerts
*/
PRIVATE AI_snort_alert*
__AI_copy_clustered_alerts ( AI_snort_alert *node )
{
AI_snort_alert *current = NULL, *next = NULL;
if ( !node )
{
return NULL;
}
if ( node->next )
{
next = __AI_copy_clustered_alerts ( node->next );
}
if ( !( current = ( AI_snort_alert* ) malloc ( sizeof ( AI_snort_alert )) ))
{
AI_fatal_err ( "Fatal dynamic memory allocation failure", __FILE__, __LINE__ );
}
memcpy ( current, node, sizeof ( AI_snort_alert ));
current->next = next;
return current;
} /* ----- end of function __AI_copy_clustered_alerts ----- */
/**
* \brief Return the alerts parsed so far as a linked list
* \return An AI_snort_alert pointer identifying the list of clustered alerts
*/
AI_snort_alert*
AI_get_clustered_alerts ()
{
AI_snort_alert *alerts_copy = NULL;
pthread_mutex_lock ( &mutex );
alerts_copy = __AI_copy_clustered_alerts ( alert_log );
pthread_mutex_unlock ( &mutex );
return alerts_copy;
} /* ----- end of function AI_get_clustered_alerts ----- */
/** @} */
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