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Bayesian correlation now working
This commit is contained in:
BlackLight
parent
0ac6af9921
commit
d7e0b426f4
7 changed files with 324 additions and 54 deletions
14
README
14
README
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@ -152,6 +152,8 @@ preprocessor ai: \
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alert_serialization_interval 3600 \
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alert_bufsize 30 \
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alert_clustering_interval 300 \
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bayesian_correlation_interval 1200 \
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bayesian_correlation_cache_validity 600 \
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correlation_graph_interval 300 \
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correlation_rules_dir "/your/snort/dir/etc/corr_rules" \
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correlated_alerts_dir "/your/snort/dir/log/correlated_alerts" \
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@ -200,6 +202,18 @@ not specified: 30)
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of the alerts in the log according to the provided clustering hierarchies and
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the next one (default if not specified: 300 seconds)
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- bayesian_correlation_interval: Interval, in seconds, that should occur between
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two alerts in the history for considering them as, more or less strongly,
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correlated (default: 1200 seconds). NOTE: A value of 0 will disable the bayesian
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correlation. This setting is strongly suggested when your alert log is still
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"learning", i.e. when you don't have enough alerts yet. After this period, you
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can set the correlation interval to any value.
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- bayesian_correlation_cache_validity: interval, in seconds, for which an entry
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in the bayesian correlation hash table (i.e. a pair of alerts with the
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associated historical bayesian correlation) is considered as valid
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before being updated (default: 600 seconds)
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- correlation_graph_interval: The interval that should occur from the building
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of the correlation graph between the clustered alerts and the next one (default
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if not specified: 300 seconds)
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6
TODO
6
TODO
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@ -2,16 +2,14 @@
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AVERAGE/HIGH PRIORITY:
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======================
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- Add alerts' history serialization to db.c as well
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- Testing more scenarios, making more hyperalert models
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- Bayesian learning among alerts in alert log
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- libgc support
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=============
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LOW PRIORITY:
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=============
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- Managing clusters for addresses, timestamps (and more?)
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- libgc support
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=====
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DONE:
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@ -22,4 +20,6 @@ DONE:
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+ Managing hyperalert graph connection inside the alert structure itself
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+ Keeping track of all the streams and alerts even after clustered
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+ Dynamic cluster_min_size algorithm
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+ Add alerts' history serialization to db.c as well
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+ Bayesian learning among alerts in alert log
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@ -21,26 +21,14 @@
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#include <sys/stat.h>
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typedef struct {
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int gid;
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int sid;
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int rev;
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} AI_alert_event_key;
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typedef struct _AI_alert_event {
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AI_alert_event_key key;
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unsigned int count;
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time_t timestamp;
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struct _AI_alert_event *next;
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UT_hash_handle hh;
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} AI_alert_event;
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/** \defgroup alert_history Manage the serialization and deserialization of alert history to the history file
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* @{ */
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PRIVATE AI_alert_event *alerts_hash = NULL;
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/**
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* FUNCTION: AI_alerts_hash_free
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* \brief Free a hash table of alert events
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* \param events Hash table to be freed
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*/
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@ -237,3 +225,44 @@ AI_serialize_alerts ( AI_snort_alert **alerts_pool, unsigned int alerts_pool_cou
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fclose ( fp );
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} /* ----- end of function AI_serialize_alerts ----- */
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/**
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* \brief Get the sequence of alerts saved in the history file given the ID of the alert
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* \param key Key representing the Snort ID of the alert
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* \return The flow of events of that type of alert saved in the history
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*/
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const AI_alert_event*
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AI_get_alert_events_by_key ( AI_alert_event_key key )
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{
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AI_alert_event *found = NULL;
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HASH_FIND ( hh, alerts_hash, &key, sizeof ( key ), found );
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return found;
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} /* ----- end of function AI_get_alert_events_by_key ----- */
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/**
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* \brief Get the number of alerts saved in the history file
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* \return The number of single alerts (not alert types) saved in the history file
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*/
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unsigned int
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AI_get_history_alert_number ()
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{
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unsigned int alert_count = 0;
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AI_alert_event *event_iterator = NULL;
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if ( !alerts_hash )
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{
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AI_deserialize_alerts();
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}
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for ( event_iterator = alerts_hash; event_iterator; event_iterator = ( AI_alert_event* ) event_iterator->hh.next )
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{
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alert_count += event_iterator->count;
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}
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return alert_count;
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} /* ----- end of function AI_get_history_alert_number ----- */
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/* @} */
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@ -30,6 +30,8 @@
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#include <sys/stat.h>
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#include <pthread.h>
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/** \defgroup alert_parser Parse the alert log into binary structures
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* @{ */
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PRIVATE AI_snort_alert *alerts = NULL;
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PRIVATE FILE *alert_fp = NULL;
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@ -40,10 +42,6 @@ AI_snort_alert **alerts_pool = NULL;
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unsigned int alerts_pool_count = 0;
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/** \defgroup alert_parser Parse the alert log into binary structures
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* @{ */
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/**
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* \brief Serialize the pool of alerts in a separated thread
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* \param arg void* pointer to the alert to be added to the pool, if any
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180
correlation.c
180
correlation.c
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@ -66,10 +66,40 @@ typedef struct {
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UT_hash_handle hh;
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} AI_alert_correlation;
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PRIVATE AI_hyperalert_info *hyperalerts = NULL;
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PRIVATE AI_snort_alert *alerts = NULL;
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PRIVATE AI_alert_correlation *correlation_table = NULL;
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PRIVATE pthread_mutex_t mutex;
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/** Key for the bayesian correlation table */
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typedef struct {
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/** Snort ID of the first alert */
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AI_alert_event_key a;
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/** Snort ID of the second alert */
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AI_alert_event_key b;
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} AI_bayesian_correlation_key;
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/** Bayesian alert correlation hash table */
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typedef struct {
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/** Key for the hash table */
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AI_bayesian_correlation_key key;
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/** Correlation value */
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double correlation;
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/** Timestamp of the last acquired correlation value */
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time_t latest_computation_time;
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/** Make the struct 'hashable' */
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UT_hash_handle hh;
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} AI_bayesian_correlation;
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PRIVATE AI_bayesian_correlation *bayesian_cache = NULL;
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PRIVATE AI_hyperalert_info *hyperalerts = NULL;
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PRIVATE AI_snort_alert *alerts = NULL;
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PRIVATE AI_alert_correlation *correlation_table = NULL;
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PRIVATE double k_exp_value = 0.0;
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PRIVATE pthread_mutex_t mutex;
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/**
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* \brief Clean up the correlation hash table
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@ -92,11 +122,10 @@ _AI_correlation_table_cleanup ()
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* \brief Recursively write a flow of correlated alerts to a .dot file, ready for being rendered as graph
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* \param corr Correlated alerts
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* \param fp File pointer
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* \param strong Boolean value set if the correlation between the alerts is 'strong' (greater than avg + 2*k*deviation)
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*/
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PRIVATE void
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_AI_print_correlated_alerts ( AI_alert_correlation *corr, FILE *fp, BOOL strong )
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_AI_print_correlated_alerts ( AI_alert_correlation *corr, FILE *fp )
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{
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char src_addr1[INET_ADDRSTRLEN],
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dst_addr1[INET_ADDRSTRLEN],
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@ -141,7 +170,7 @@ _AI_print_correlated_alerts ( AI_alert_correlation *corr, FILE *fp, BOOL strong
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"\"[%d.%d.%d] %s\\n"
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"%s:%s -> %s:%s\\n"
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"%s\\n"
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"(%d alerts grouped)\"%s;\n",
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"(%d alerts grouped)\";\n",
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corr->key.a->gid, corr->key.a->sid, corr->key.a->rev, corr->key.a->desc,
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src_addr1, src_port1, dst_addr1, dst_port1,
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@ -151,8 +180,7 @@ _AI_print_correlated_alerts ( AI_alert_correlation *corr, FILE *fp, BOOL strong
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corr->key.b->gid, corr->key.b->sid, corr->key.b->rev, corr->key.b->desc,
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src_addr2, src_port2, dst_addr2, dst_port2,
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timestamp2,
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corr->key.b->grouped_alerts_count,
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strong ? "" : "[style=dotted]"
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corr->key.b->grouped_alerts_count
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);
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} /* ----- end of function _AI_correlation_flow_to_file ----- */
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@ -233,14 +261,125 @@ _AI_get_function_arguments ( char *orig_stmt, int *n_args )
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} /* ----- end of function _AI_get_function_arguments ----- */
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/**
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* \brief Compute the correlation coefficient between two alerts, as #INTERSECTION(pre(B), post(A) / #UNION(pre(B), post(A))
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* \brief Function used for computing the correlation probability A->B of two alerts (A,B) given their timestamps: f(ta, tb) = exp ( -(tb - ta)^2 / k )
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* \param ta Timestamp of A
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* \param tb Timestamp of B
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* \return The correlation probability A->B
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*/
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PRIVATE double
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_AI_bayesian_correlation_function ( time_t ta, time_t tb )
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{
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if ( k_exp_value == 0.0 )
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k_exp_value = - (double) (config->bayesianCorrelationInterval * config->bayesianCorrelationInterval) / log ( CUTOFF_Y_VALUE );
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return exp ( -((ta - tb) * (ta - tb)) / k_exp_value );
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} /* ----- end of function _AI_bayesian_correlation_function ----- */
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/**
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* \brief Compute the correlation between two alerts, A -> B: p[A|B] = p[Corr(A,B)] / P[B]
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* \param a First alert
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* \param b Second alert
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* \return A real coefficient representing p[A|B] using the historical information
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*/
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PRIVATE double
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_AI_alert_bayesian_correlation ( AI_snort_alert *a, AI_snort_alert *b )
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{
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double corr = 0.0;
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unsigned int corr_count = 0,
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corr_count_a = 0;
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BOOL is_a_correlated = false;
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AI_bayesian_correlation_key bayesian_key;
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AI_bayesian_correlation *found = NULL;
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AI_alert_event_key key_a,
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key_b;
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AI_alert_event *events_a = NULL,
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*events_b = NULL;
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AI_alert_event *events_iterator_a,
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*events_iterator_b;
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if ( !a || !b )
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return 0.0;
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key_a.gid = a->gid;
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key_a.sid = a->sid;
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key_a.rev = a->rev;
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key_b.gid = b->gid;
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key_b.sid = b->sid;
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key_b.rev = b->rev;
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/* Check if this correlation value is already in our cache */
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bayesian_key.a = key_a;
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bayesian_key.b = key_b;
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HASH_FIND ( hh, bayesian_cache, &bayesian_key, sizeof ( bayesian_key ), found );
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if ( found )
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{
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/* Ok, the abs() is not needed until the time starts running backwards, but it's better going safe... */
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if ( abs ( time ( NULL ) - found->latest_computation_time ) <= config->bayesianCorrelationCacheValidity )
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/* If our alert couple is there, just return it */
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return found->correlation;
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}
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if ( !( events_a = (AI_alert_event*) AI_get_alert_events_by_key ( key_a )) ||
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!( events_b = (AI_alert_event*) AI_get_alert_events_by_key ( key_b )))
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return 0.0;
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for ( events_iterator_a = events_a; events_iterator_a; events_iterator_a = events_iterator_a->next )
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{
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is_a_correlated = false;
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for ( events_iterator_b = events_b; events_iterator_b; events_iterator_b = events_iterator_b->next )
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{
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if ( abs ( events_iterator_a->timestamp - events_iterator_b->timestamp ) <= config->bayesianCorrelationInterval )
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{
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is_a_correlated = true;
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corr_count++;
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corr += _AI_bayesian_correlation_function ( events_iterator_a->timestamp, events_iterator_b->timestamp );
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}
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}
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if ( is_a_correlated )
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corr_count_a++;
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}
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corr /= (double) corr_count;
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corr -= ( events_a->count - corr_count_a ) / events_a->count;
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/* _dpd.logMsg ( " Number of '%s' alerts correlated to '%s': %u over %u\\n", a->desc, b->desc, corr_count_a, events_a->count ); */
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if ( found )
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{
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found->correlation = corr;
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found->latest_computation_time = time ( NULL );
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} else {
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if ( !( found = ( AI_bayesian_correlation* ) malloc ( sizeof ( AI_bayesian_correlation ))))
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_dpd.fatalMsg ( "AIPreproc: Fatal dynamic memory allocation error at %s:%d\n", __FILE__, __LINE__ );
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found->key = bayesian_key;
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found->correlation = corr;
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found->latest_computation_time = time ( NULL );
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}
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/* _dpd.logMsg ( "Correlation ('%s') -> ('%s'): %f\\n", a->desc, b->desc, corr ); */
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return corr;
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} /* ----- end of function _AI_alert_bayesian_correlation ----- */
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/**
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* \brief Compute the correlation coefficient between two alerts, as #INTERSECTION(pre(B), post(A)) / #UNION(pre(B), post(A)), on the basis of preconditions and postconditions in the knowledge base's correlation rules
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* \param a Alert a
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* \param b Alert b
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* \return The correlation coefficient between A and B as coefficient in [0,1]
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*/
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PRIVATE double
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_AI_correlation_coefficient ( AI_snort_alert *a, AI_snort_alert *b )
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_AI_kb_correlation_coefficient ( AI_snort_alert *a, AI_snort_alert *b )
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{
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unsigned int i, j, k, l,
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n_intersection = 0,
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@ -444,7 +583,7 @@ _AI_correlation_coefficient ( AI_snort_alert *a, AI_snort_alert *b )
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}
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return (double) ((double) n_intersection / (double) n_union );
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} /* ----- end of function _AI_correlation_coefficient ----- */
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} /* ----- end of function _AI_kb_correlation_coefficient ----- */
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/**
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@ -691,7 +830,8 @@ AI_alert_correlation_thread ( void *arg )
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double avg_correlation = 0.0,
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std_deviation = 0.0,
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corr_threshold = 0.0,
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corr_strong_threshold = 0.0;
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kb_correlation = 0.0,
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bayesian_correlation = 0.0;
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FILE *fp = NULL;
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@ -800,7 +940,16 @@ AI_alert_correlation_thread ( void *arg )
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corr_key.b = alert_iterator2;
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corr->key = corr_key;
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corr->correlation = _AI_correlation_coefficient ( corr_key.a, corr_key.b );
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kb_correlation = _AI_kb_correlation_coefficient ( corr_key.a, corr_key.b );
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bayesian_correlation = _AI_alert_bayesian_correlation ( corr_key.a, corr_key.b );
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if ( bayesian_correlation == 0.0 || config->bayesianCorrelationInterval == 0 )
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corr->correlation = kb_correlation;
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else if ( kb_correlation == 0.0 )
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corr->correlation = bayesian_correlation;
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else
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corr->correlation = ( kb_correlation + bayesian_correlation ) / 2;
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HASH_ADD ( hh, correlation_table, key, sizeof ( AI_alert_correlation_key ), corr );
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}
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}
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@ -827,7 +976,6 @@ AI_alert_correlation_thread ( void *arg )
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std_deviation = sqrt ( std_deviation / (double) HASH_COUNT ( correlation_table ));
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corr_threshold = avg_correlation + ( config->correlationThresholdCoefficient * std_deviation );
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corr_strong_threshold = avg_correlation + ( 2.0 * config->correlationThresholdCoefficient * std_deviation );
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snprintf ( corr_dot_file, sizeof ( corr_dot_file ), "%s/correlated_alerts.dot", config->corr_alerts_dir );
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if ( stat ( config->corr_alerts_dir, &st ) < 0 )
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@ -862,7 +1010,7 @@ AI_alert_correlation_thread ( void *arg )
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corr->key.a->derived_alerts[ corr->key.a->n_derived_alerts - 1 ] = corr->key.b;
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corr->key.b->parent_alerts [ corr->key.b->n_parent_alerts - 1 ] = corr->key.a;
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_AI_print_correlated_alerts ( corr, fp, ( corr->correlation >= corr_strong_threshold ));
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_AI_print_correlated_alerts ( corr, fp );
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}
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}
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73
spp_ai.c
73
spp_ai.c
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@ -157,18 +157,20 @@ static AI_config * AI_parse(char *args)
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hierarchy_node **hierarchy_nodes = NULL;
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int n_hierarchy_nodes = 0;
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unsigned long cleanup_interval = 0,
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stream_expire_interval = 0,
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alertfile_len = 0,
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alert_history_file_len = 0,
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alert_serialization_interval = 0,
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alert_bufsize = 0,
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clusterfile_len = 0,
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corr_rules_dir_len = 0,
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corr_alerts_dir_len = 0,
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alert_clustering_interval = 0,
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database_parsing_interval = 0,
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correlation_graph_interval = 0;
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unsigned long cleanup_interval = 0,
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stream_expire_interval = 0,
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alertfile_len = 0,
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alert_history_file_len = 0,
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alert_serialization_interval = 0,
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alert_bufsize = 0,
|
||||
bayesian_correlation_interval = 0,
|
||||
bayesian_correlation_cache_validity = 0,
|
||||
clusterfile_len = 0,
|
||||
corr_rules_dir_len = 0,
|
||||
corr_alerts_dir_len = 0,
|
||||
alert_clustering_interval = 0,
|
||||
database_parsing_interval = 0,
|
||||
correlation_graph_interval = 0;
|
||||
|
||||
BOOL has_cleanup_interval = false,
|
||||
has_stream_expire_interval = false,
|
||||
|
|
@ -336,11 +338,56 @@ static AI_config * AI_parse(char *args)
|
|||
}
|
||||
|
||||
corr_threshold_coefficient = strtod ( arg, NULL );
|
||||
_dpd.logMsg( " Correlation threshold coefficient: %d\n", corr_threshold_coefficient );
|
||||
_dpd.logMsg( " Correlation threshold coefficient: %f\n", corr_threshold_coefficient );
|
||||
}
|
||||
|
||||
config->correlationThresholdCoefficient = corr_threshold_coefficient;
|
||||
|
||||
/* Parsing the bayesian_correlation_interval option */
|
||||
if (( arg = (char*) strcasestr( args, "bayesian_correlation_interval" ) ))
|
||||
{
|
||||
for ( arg += strlen("bayesian_correlation_interval");
|
||||
*arg && (*arg < '0' || *arg > '9');
|
||||
arg++ );
|
||||
|
||||
if ( !(*arg) )
|
||||
{
|
||||
_dpd.fatalMsg("AIPreproc: bayesian_correlation_interval option used but "
|
||||
"no value specified\n");
|
||||
}
|
||||
|
||||
bayesian_correlation_interval = strtoul ( arg, NULL, 10 );
|
||||
config->bayesianCorrelationInterval = bayesian_correlation_interval;
|
||||
} else {
|
||||
bayesian_correlation_interval = DEFAULT_BAYESIAN_CORRELATION_INTERVAL;
|
||||
}
|
||||
|
||||
config->bayesianCorrelationInterval = bayesian_correlation_interval;
|
||||
_dpd.logMsg( " Bayesian correlation interval: %u\n", config->bayesianCorrelationInterval );
|
||||
|
||||
/* Parsing the bayesian_correlation_cache_validity option */
|
||||
if (( arg = (char*) strcasestr( args, "bayesian_correlation_cache_validity" ) ))
|
||||
{
|
||||
for ( arg += strlen("bayesian_correlation_cache_validity");
|
||||
*arg && (*arg < '0' || *arg > '9');
|
||||
arg++ );
|
||||
|
||||
if ( !(*arg) )
|
||||
{
|
||||
_dpd.fatalMsg("AIPreproc: bayesian_correlation_cache_validity option used but "
|
||||
"no value specified\n");
|
||||
}
|
||||
|
||||
bayesian_correlation_cache_validity = strtoul ( arg, NULL, 10 );
|
||||
config->bayesianCorrelationCacheValidity = bayesian_correlation_cache_validity;
|
||||
} else {
|
||||
bayesian_correlation_cache_validity = DEFAULT_BAYESIAN_CORRELATION_CACHE_VALIDITY;
|
||||
}
|
||||
|
||||
config->bayesianCorrelationCacheValidity = bayesian_correlation_cache_validity;
|
||||
_dpd.logMsg( " Bayesian cache validity interval: %u\n", config->bayesianCorrelationCacheValidity );
|
||||
|
||||
|
||||
/* Parsing the alertfile option */
|
||||
if (( arg = (char*) strcasestr( args, "alertfile" ) ))
|
||||
{
|
||||
|
|
|
|||
42
spp_ai.h
42
spp_ai.h
|
|
@ -69,6 +69,15 @@
|
|||
/** Default timeout in seconds between a serialization of the alerts' buffer and the next one */
|
||||
#define DEFAULT_ALERT_SERIALIZATION_INTERVAL 3600
|
||||
|
||||
/** Default interval between two alerts (a,b) for considering them correlated */
|
||||
#define DEFAULT_BAYESIAN_CORRELATION_INTERVAL 1200
|
||||
|
||||
/** Default interval of validity in seconds for an entry in the cache of correlated alerts */
|
||||
#define DEFAULT_BAYESIAN_CORRELATION_CACHE_VALIDITY 600
|
||||
|
||||
/** Cutoff y value in the exponential decay for considering two alerts not correlated */
|
||||
#define CUTOFF_Y_VALUE 0.01
|
||||
|
||||
/****************************/
|
||||
/* Database support */
|
||||
#ifdef HAVE_LIBMYSQLCLIENT
|
||||
|
|
@ -143,6 +152,12 @@ typedef struct
|
|||
/** Interval in seconds between a serialization of the alerts' buffer and the next one */
|
||||
unsigned long alertSerializationInterval;
|
||||
|
||||
/** Interval in seconds between two alerts (a,b) for considering them correlated */
|
||||
unsigned long bayesianCorrelationInterval;
|
||||
|
||||
/** Interval in seconds for which an entry in the cache of correlated alerts is valid */
|
||||
unsigned long bayesianCorrelationCacheValidity;
|
||||
|
||||
/** Size of the alerts' buffer to be periodically sent to the serialization thread */
|
||||
unsigned long alert_bufsize;
|
||||
|
||||
|
|
@ -299,6 +314,23 @@ typedef struct _AI_snort_alert {
|
|||
unsigned int n_derived_alerts;
|
||||
} AI_snort_alert;
|
||||
/*****************************************************************/
|
||||
/** Key for the AI_alert_event structure, containing the Snort ID of the alert */
|
||||
typedef struct {
|
||||
int gid;
|
||||
int sid;
|
||||
int rev;
|
||||
} AI_alert_event_key;
|
||||
/*****************************************************************/
|
||||
/** Structure representing the historical information of an alert saved in alert_history */
|
||||
typedef struct _AI_alert_event {
|
||||
AI_alert_event_key key;
|
||||
unsigned int count;
|
||||
time_t timestamp;
|
||||
struct _AI_alert_event *next;
|
||||
UT_hash_handle hh;
|
||||
} AI_alert_event;
|
||||
/*****************************************************************/
|
||||
|
||||
|
||||
int preg_match ( const char*, char*, char***, int* );
|
||||
char* str_replace ( char*, char*, char *);
|
||||
|
|
@ -323,10 +355,12 @@ struct pkt_info* AI_get_stream_by_key ( struct pkt_key );
|
|||
AI_snort_alert* AI_get_alerts ( void );
|
||||
AI_snort_alert* AI_get_clustered_alerts ( void );
|
||||
|
||||
void AI_serialize_alerts ( AI_snort_alert**, unsigned int );
|
||||
void* AI_deserialize_alerts ();
|
||||
void* AI_alerts_pool_thread ( void *arg );
|
||||
void* AI_serializer_thread ( void *arg );
|
||||
void AI_serialize_alerts ( AI_snort_alert**, unsigned int );
|
||||
void* AI_deserialize_alerts ();
|
||||
void* AI_alerts_pool_thread ( void *arg );
|
||||
void* AI_serializer_thread ( void *arg );
|
||||
const AI_alert_event* AI_get_alert_events_by_key ( AI_alert_event_key );
|
||||
unsigned int AI_get_history_alert_number ();
|
||||
|
||||
/** Function pointer to the function used for getting the alert list (from log file, db, ...) */
|
||||
extern AI_snort_alert* (*get_alerts)(void);
|
||||
|
|
|
|||
Loading…
Reference in a new issue