Keeping bayesian correlation in bayesian.c
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BlackLight
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@ -18,6 +18,7 @@ include/sfPolicyUserData.c
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libsf_ai_preproc_la_SOURCES = \
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alert_history.c \
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alert_parser.c \
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bayesian.c \
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cluster.c \
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correlation.c \
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db.c \
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@ -77,7 +77,7 @@ LTLIBRARIES = $(lib_LTLIBRARIES)
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libsf_ai_preproc_la_LIBADD =
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am_libsf_ai_preproc_la_OBJECTS = libsf_ai_preproc_la-alert_history.lo \
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libsf_ai_preproc_la-alert_parser.lo \
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libsf_ai_preproc_la-cluster.lo \
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libsf_ai_preproc_la-bayesian.lo libsf_ai_preproc_la-cluster.lo \
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libsf_ai_preproc_la-correlation.lo libsf_ai_preproc_la-db.lo \
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libsf_ai_preproc_la-mysql.lo libsf_ai_preproc_la-postgresql.lo \
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libsf_ai_preproc_la-regex.lo libsf_ai_preproc_la-spp_ai.lo \
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@ -253,6 +253,7 @@ include/sfPolicyUserData.c
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libsf_ai_preproc_la_SOURCES = \
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alert_history.c \
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alert_parser.c \
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bayesian.c \
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cluster.c \
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correlation.c \
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db.c \
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@ -379,6 +380,9 @@ libsf_ai_preproc_la-alert_history.lo: alert_history.c
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libsf_ai_preproc_la-alert_parser.lo: alert_parser.c
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$(LIBTOOL) --tag=CC $(AM_LIBTOOLFLAGS) $(LIBTOOLFLAGS) --mode=compile $(CC) $(DEFS) $(DEFAULT_INCLUDES) $(INCLUDES) $(AM_CPPFLAGS) $(CPPFLAGS) $(libsf_ai_preproc_la_CFLAGS) $(CFLAGS) -c -o libsf_ai_preproc_la-alert_parser.lo `test -f 'alert_parser.c' || echo '$(srcdir)/'`alert_parser.c
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libsf_ai_preproc_la-bayesian.lo: bayesian.c
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$(LIBTOOL) --tag=CC $(AM_LIBTOOLFLAGS) $(LIBTOOLFLAGS) --mode=compile $(CC) $(DEFS) $(DEFAULT_INCLUDES) $(INCLUDES) $(AM_CPPFLAGS) $(CPPFLAGS) $(libsf_ai_preproc_la_CFLAGS) $(CFLAGS) -c -o libsf_ai_preproc_la-bayesian.lo `test -f 'bayesian.c' || echo '$(srcdir)/'`bayesian.c
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libsf_ai_preproc_la-cluster.lo: cluster.c
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$(LIBTOOL) --tag=CC $(AM_LIBTOOLFLAGS) $(LIBTOOLFLAGS) --mode=compile $(CC) $(DEFS) $(DEFAULT_INCLUDES) $(INCLUDES) $(AM_CPPFLAGS) $(CPPFLAGS) $(libsf_ai_preproc_la_CFLAGS) $(CFLAGS) -c -o libsf_ai_preproc_la-cluster.lo `test -f 'cluster.c' || echo '$(srcdir)/'`cluster.c
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8
TODO
8
TODO
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@ -2,6 +2,12 @@
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AVERAGE/HIGH PRIORITY:
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======================
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- Clustering alerts with time constraints
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- Web interface
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- Code profiling
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- Saving packet flows as .pcap
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- Neural network for computing k
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- Isolating independant subgraphs from hyperalert correlation graphs
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- Testing more scenarios, making more hyperalert models
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=============
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@ -9,6 +15,7 @@ LOW PRIORITY:
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=============
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- Managing clusters for addresses, timestamps (and more?)
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- Splitting the distinct subgraphs of the output graph
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- libgc support
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=====
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@ -22,4 +29,5 @@ DONE:
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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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+ Split bayesian correlation out of correlation.c
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140
correlation.c
140
correlation.c
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@ -67,37 +67,9 @@ typedef struct {
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} AI_alert_correlation;
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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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@ -260,116 +232,6 @@ _AI_get_function_arguments ( char *orig_stmt, int *n_args )
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return args;
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} /* ----- end of function _AI_get_function_arguments ----- */
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/**
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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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@ -941,7 +803,7 @@ AI_alert_correlation_thread ( void *arg )
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corr->key = corr_key;
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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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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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1
spp_ai.h
1
spp_ai.h
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@ -361,6 +361,7 @@ void* AI_alerts_pool_thread ( void *arg );
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void* AI_serializer_thread ( void *arg );
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const AI_alert_event* AI_get_alert_events_by_key ( AI_alert_event_key );
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unsigned int AI_get_history_alert_number ();
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double AI_alert_bayesian_correlation ( AI_snort_alert *a, AI_snort_alert *b );
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/** Function pointer to the function used for getting the alert list (from log file, db, ...) */
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extern AI_snort_alert* (*get_alerts)(void);
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