1. Anomaly Detection Systems

2. Contents Statistical methods Systems with learning
Clustering in anomaly detection systems

3. Anomaly detection
Anomaly detection involves a process of establishing profiles of normal behaviour, comparing actual user/network behaviour to those profiles, and flagging deviations from the normal.
The basis of anomaly detection is the assertion that abnormal behaviour patterns indicate misuse of systems.

4. Anomaly detection
Profiles are defined as sets of metrics. Metrics are measures of particular aspects of user behaviour.
Each metric is associated with a threshold or range of values.

5. Anomaly detection
Anomaly detection depends on an assumption that users exhibit predictable, consistent patterns of system usage.
The approach also accommodates adaptations to changes in user behaviour over time.

6. Anomaly detection
The completeness of anomaly detection has yet to be verified (no one knows whether any given set of metrics is rich enough to express all anomalous behaviour).

7. Statistical methods Parametric methods
Analytical approaches in which assumptions are made about the underlying distribution of the data being analyzed.
The usual assumption is that the distributions of usage patterns are Gaussian:
x0 – mean
 - standard deviation

8. Statistical methods Non-parametric methods
Involve nonparametric data classification techniques - cluster analysis.

9. Statistical methods
The Denning’s model (the IDES model for intrusion).
Four statistical models may be included in the system:
Operational model
Mean and standard deviation model
Multivariate model
Markov process model.
Each model is suitable for a particular type of system metric.

10. Statistical methods Operational model
This model applies to metrics such as event counters for the number of password failures in a particular time interval.
The model compares the metric to a set threshold, triggering an anomaly when the metric exceeds the threshold value.

11. Statistical methods Mean and standard deviation model
A classical mean and standard deviation characterization of data.
The assumption is that all the analyzer knows about system behaviour metrics are the mean and standard deviations.

12. Statistical methods Mean and standard deviation model (cont.)
A new behaviour observation is defined to be abnormal if it falls outside a confidence interval.
This confidence interval is defined as d standard deviations from the mean for some parameter d (usually d=3).

13. Statistical methods Mean and standard deviation model (cont.)
This characterization is applicable to event counters, interval timers, and resource measures (memory, CPU, etc.)
It is possible to assign weights to these computations, such that more recent data are assigned greater weights.

14. Statistical methods Multivariate model
This is an extension to the mean and standard deviation model.
It is based on performing correlations among two or more metrics.
Instead of basing the detection of an anomaly strictly on one measure, one might base it on the correlation of that measure with another measure.

15. Statistical methods Multivariate model (cont.) Example:
Instead of detecting an anomaly based solely on the observed length of a session, one might base it on the correlation of the length of the session with the number of CPU cycles utilized.

16. Statistical methods Markov process model
Under this model, the detector considers each different type of audit event as a state variable and uses a state transition matrix to characterize the transition frequencies between states (not the frequencies of the individual states/audit records).

17. Statistical methods Markov process model (cont.)
A new observation is defined as anomalous if its probability, as determined by the previous state and value in the state transition matrix, is too low/high.
This allows the detector to spot unusual command or event sequences, not just single events.
This introduces the notion of performing stateful analysis of event streams (frequent episodes, etc.)

18. Statistical methods Markov process model (cont.)
Example - NIDES (Next-generation Intrusion Detection Expert System)
Developed by SRI (Stanford Research Institute) in 1990s.
Measures various activity levels.
Combines these into a single “normality” measure.
Checks this against a threshold.
If the measure is above the threshold, the activity is considered abnormal.

19. Statistical methods Markov process model (cont.)
Example - NIDES (cont.)
NIDES measures
Intensity measures
An example would be the number of audit records (log entries) generated within a set time interval.
Several different time intervals are used in order to track short-, medium-, and long-term behaviour.

20. Statistical methods Markov process model (cont.)
Example - NIDES (cont.)
NIDES measures (cont.)
Distribution measures
The overall distribution of the various audit records (log file entries) is tracked via histograms.
A difference measure is defined to determine how close a given short-term histogram is to “normal” behaviour.

21. Statistical methods Markov process model (cont.)
Example - NIDES (cont.)
NIDES measures (cont.)
Categorical data
The names of files accessed or the names of remote computers accessed are examples of categorical data used.

22. Statistical methods Markov process model (cont.)
Example - NIDES (cont.)
NIDES measures (cont.)
Counting measures
These are numerical values that measure parameters such as the number of seconds of CPU time used.
They are generally taken over a fixed amount of time or over a specific event, such as a single login.
Thus, they are similar in character to intensity measures, although they measure a different kind of activity.

23. Statistical methods Markov process model (cont.)
Example - NIDES (cont.)
The different measurements each define a statistic Sj .
These measurements are assumed (constructed to be) appropriate (this includes normalization), and are combined to produce a 2-like statistic:

24. Statistical methods Markov process model (cont.)
Example - NIDES (cont.)
A more complicated measure would include the correlation between the events (as was done with IDES):
Here, C is the correlation matrix between Si and Sj for all i and j. IS is called the IDES score.

25. Statistical methods Markov process model (cont.)
Example - NIDES (cont.)
NIDES compares recent activity with past activity, using a methodology that amounts to a sliding window on the past.
Thus it is designed to detect changes in activity and to adapt to new activity levels.

26. Statistical methods Markov process model (cont.)
Example - NIDES (cont.)
NIDES intensity measures are counts of audit records per time unit etc.
This provides an overall activity level for the system.
These are updated continuously rather than recomputed at each time interval.

27. Statistical methods Markov process model (cont.)
Example - NIDES (cont.)
Possible elements that can be monitored with this basic idea:
Average system load.
Number of active processes.
Number of s received.
Different types of audit records (can be tracked separately).

28. Statistical methods Markov process model (cont.)
Example - NIDES (cont.)
The obvious extension of the intensity measures idea is to track the different types of audit records.
This leads to a distribution (histogram) for the audit records.
Similarly, one could track the sizes of messages received, or the types of files accessed.
These can be updated continuously.
Distributions are then compared by means of a squared error metric.

29. Statistical methods Markov process model (cont.)
Example - NIDES (cont.)
Categorical measures can be for example the names of files accessed.
They are treated just like distributional measures.
Now each bin corresponds to a categorical, while with distributional measures the bin can correspond to a range of values.
The updates are still done continuously.

30. Statistical methods Markov process model (cont.)
Example - NIDES (cont.)
All the measures are combined into the T2 statistic.
The value is compared with a threshold to determine if the activity is “abnormal”.
The threshold is usually set empirically, based on the observed network behaviour in some period of time.

31. Statistical methods Markov process model (cont.)
Example - NIDES (cont.)
NIDES produces a single, overall measure of “normality”, which could allow further investigation into the components that make up the statistic upon an alert.
The problem with this is that an unusually low value for one statistic can mask a high one for another – multifaceted measures are more useful.

32. Statistical methods Advantages of parametric approach
Statistical anomaly detection could reveal interesting, sometimes suspicious, activities that could lead to discoveries of security breaches.

33. Statistical methods Advantages of parametric approach (cont.)
Parametric statistical systems do not require the constant updates and maintenance that misuse detection systems do.
However, metrics must be well chosen, adequate for good discrimination, and well-adapted to changes in behaviour (that is, changes in user behaviour must produce a consistent, noticeable change in the corresponding metrics).

34. Statistical methods Disadvantages of parametric approach
Batch mode processing of audit records, which eliminates the capability to perform automated responses to block damage.
Although more recent systems attempt to perform real-time analysis of audit data, the memory and processing loads involved in using and maintaining the user profile knowledge base usually cause the system to lag behind audit record generation.

35. Statistical methods Disadvantages of parametric approach (cont.)
The nature of statistical analysis reduces the capability of taking into account the sequential relationships between events.
The exact order of the occurrence of events is not provided as an attribute in most of these systems.

36. Statistical methods Disadvantages of parametric approach (cont.)
Because many anomalies indicating attack depend on such sequential event relationships, this situation represents a serious limitation to the approach.
In cases when quantitative methods (Denning's operational model) are utilized, it is also difficult to select appropriate values for thresholds and ranges.

37. Statistical methods Disadvantages of parametric approach (cont.)
The false positive rates associated with statistical analysis systems are high, which sometimes leads to users ignoring or disabling the systems.
The false negative rates are also difficult to reduce in these systems.

38. Statistical methods Nonparametric measures
One of the problems of parametric methods is that error rates are high when the assumptions about the distribution are incorrect.

39. Statistical methods Nonparametric measures (cont.)
When researchers began collecting information about system usage patterns that included attributes such as system resource usage, the distributions were discovered not to be normal.
Then, including normal distribution assumption into the measures led to high error rates.

40. Statistical methods Nonparametric measures (cont.)
A way of overcoming these problems is to utilize nonparametric techniques for performing anomaly detection.
This approach provides the capability of accommodating users with less predictable usage patterns and allows the analyzer to take into account system measures that are not easily accommodated by parametric schemes.

41. Statistical methods Nonparametric measures (cont.)
The nonparametric approach involves nonparametric data classification techniques, specifically cluster analysis.
In cluster analysis, large quantities of historical data are collected (a sample set) and organized into clusters according to some evaluation criteria.

42. Statistical methods Nonparametric measures (cont.)
Preprocessing is performed in which features associated with a particular event stream (often mapped to a specific user) are converted into a vector representation (for example, Xi = [f1, f2, ..., fn] in an n-dimensional state).

43. Statistical methods Nonparametric measures (cont.)
A clustering algorithm is used to group vectors into classes by behaviours, attempting to group them so that members of each class are as close as possible to each other while different classes are as far apart as they can be.

44. Statistical methods Nonparametric measures (cont.)
In nonparametric statistical anomaly detection, the premise is that a user's activity data, as expressed in terms of the features, falls into two distinct clusters: one indicating anomalous activity and the other indicating normal activity.

45. Statistical methods Nonparametric measures (cont.)
Various clustering algorithms are available. These range from algorithms that use simple distance measures to determine whether an object falls into a cluster, to more complex concept-based measures (in which an object is "scored“ according to a set of conditions and that score is used to determine membership in a particular cluster) .
Different clustering algorithms usually best serve different data sets and analysis goals.

46. Statistical methods Nonparametric measures (cont.)
The advantages of nonparametric approaches include the capability of performing reliable reduction of event data (in the transformation of raw event data to vectors).
This effect may reach as high as two orders of magnitude compared to the classical approach that does not include vectors.

47. Statistical methods Nonparametric measures (cont.)
Other benefits are improvement in the speed of detection and improvement in accuracy over parametric statistical analysis.
Disadvantages involve concerns that expanding features beyond resource usage would reduce the efficiency and the accuracy of the analysis.

48. Systems with learning Two phases of system operation:
The learning phase, in which the system is taught what a normal behaviour is.
The recognition phase, in which the system classifies the input vectors according to the knowledge acquired in the learning process.
These systems also include a conversion of raw data into feature vectors.

49. Systems with learning Example: Neural networks
Neural networks use adaptive learning techniques to characterize anomalous behaviour.
This analysis technique operates on historical sets of training data, which are presumably cleansed of any data indicating intrusions or other undesirable user behaviour.

50. Systems with learning Example: Neural networks (cont.)
Neural networks consist of numerous simple processing elements called neurons that interact by using weighted connections.
The knowledge of a neural network is encoded in the structure of the net in terms of connections between units and their weights.
The actual learning process takes place by changing weights and adding or removing connections.

51. Systems with learning

52. Systems with learning Example: Neural networks (cont.)
Neural network processing involves two stages.
In the first stage, the network is populated by a training set of historical or other sample data that is representative of user behaviour.
In the second stage, the network accepts event data and compares it to historical behaviour references, determining similarities and differences.

53. Systems with learning Example: Neural networks (cont.)
The network indicates that an event is abnormal by changing the state of the units, changing the weights of connections, adding connections, or removing them.
The network also modifies its definition of what constitutes a normal event by performing stepwise corrections.

54. Systems with learning Example: Neural networks (cont.)
Neural networks don't make prior assumptions on expected statistical distribution of metrics, so this method retains some of the advantages over classical statistical analysis associated with statistical nonparametric techniques.

55. Systems with learning Example: Neural networks (cont.)
Among the problems associated with utilizing neural networks for intrusion detection is a tendency to form mysterious unstable configurations in which the network fails to learn certain things for no apparent reason.

56. Systems with learning Example: Neural networks (cont.)
The major drawback to utilizing neural networks for intrusion detection is that neural networks don't provide any explanation of the anomalies they find.

57. Systems with learning Example: Neural networks (cont.)
This practice impedes the ability of users to establish accountability or otherwise address the roots of the security problems that allowed the detected intrusion.
This made neural networks poorly suited to the needs of security managers.

58. Systems with learning
General problems related to all systems with learning
The problem with all learning-based approaches is in the fact that the effectiveness of the approach depends on the quality of the training data.
In learning-based systems, the training data must reflect normal activity for the users of the system.

59. Systems with learning
General problems related to all systems with learning (cont.)
This approach may not be comprehensive enough to reflect all possible normal user behaviour patterns.
This weakness produces a large false positive error rate.
The error rate is high because if an event does not match the learnt knowledge completely, a false alarm is often generated, although it does not always happen.

60. Clustering in anomaly detection
Clustering definition:
“Cluster analysis is the art of finding groups in data”
The aim: group the given objects in such a way that the objects within a group are mutually similar and at the same time dissimilar from other groups.

61. Clustering in anomaly detection
Formal definition:
Let P be a set of vectors, whose cardinality is m, and whose elements are p1,…,pm , of dimensions n1,…,nm , respectively.
The task: partition, optimizing a partition criterion, the set P into k subsets P1,…,Pk , such that the following holds:

62. Clustering in anomaly detection
Incoming traffic/logs
Data pre-processor
Activity data
Detection
model(s)
Detection algorithm
Clustering!
Alerts
Action/Report
Decision
criteria
Alert filter

63. Clustering in anomaly detection
Why should we do clustering instead of supervised learning?
Labelling a large set of samples is often costly.
Very large data sets – train the system with a large amount of unlabelled data and then label with supervision.
Track slow changes of patterns in time without supervision – improves performances.
Smart feature extraction.
Initial exploratory data analysis.

64. Clustering in anomaly detection
Appropriate cluster analysis algorithms:
Two main classes of clustering algorithms
Hierarchical
Non-hierarchical (partitioning)
Less efficient
More biased results in general
Non-hierarchical
Results often depend on the initial partition.

65. Clustering in anomaly detection
Appropriate cluster analysis algorithms (cont.)
A trade-off between correctness and efficiency of the CA algorithm must be found in order to achieve the real-time operation of an IDS.
K-means algorithm – could be a good candidate for implementation in IDS.

66. Clustering in anomaly detection
Appropriate cluster analysis algorithms (cont.)
An outline of the K-means algorithm
Initialization: Randomly choose K vectors from the data set and make them initial cluster centres.
Assignment: Assign each vector to its closest centre.
Updating: Replace each centre with the mean of its members.
Iteration: Repeat steps 2 and 3 until there is no more updating.

67. Clustering in anomaly detection
K-means algorithm
A local optimization algorithm – hill climbing.
Clustering depends on initial centres, but this can be overcome in several ways.
Time complexity linear in the number of input vectors.

68. Clustering in anomaly detection
Problems to solve
Determine the number of clusters
Determine the appropriate distance measure

69. Clustering in anomaly detection
Determine the number of clusters
2 clusters if we want only to tell “abnormal” from “normal” behaviour.
More complex clustering evaluation algorithms should be used to detect the number of clusters at which the most compact and separated clusters are obtained.
Use hierarchical clustering + clustering evaluation algorithms (inefficient).

70. Clustering in anomaly detection
Determine the appropriate distance measure
It must be a metric:
a,b, d(a,b)0
a,b, d(a,b)=0a=b
a,b, d(a,b)=d(b,a)
a,b,c, d(a,c)d(a,b)+d(b,c), i.e. the triangle inequality must hold.

71. Clustering in anomaly detection
Determine the appropriate distance measure (cont.)
Typical metrics:
For equal length input vectors – the Minkowski metric.
For unequal length input vectors – the edit distance (which is also a metric).

72. Clustering in anomaly detection
Minkowski metric
q=1, Manhattan (city block) distance
q=2, Euclidean distance

73. Clustering in anomaly detection
Edit distance
Elementary edit operations
Deletions
Insertions
Substitutions
Minimum number of elementary edit operations needed to transform one vector into another.
Computed recursively, by filling the matrix of partial edit distances – edit distance matrix.
The definition can include constraints.

74. Clustering in anomaly detection
Labelling clusters
Way to determine which cluster contains normal instances and which contain attacks.
1st assumption
Associate the label “normal” with the cluster of the greatest cardinality.
Fails with massive attacks, as for example the Syn-flood attack.
Fails with KDD cup data without filtering out the attacks.

75. Clustering in anomaly detection
To label properly, we need to explore the structure of the clusters.
The clustering quality criteria are used, combined with some characteristics of clusters:
Silhouette index
Davies-Bouldin index
Dunn’s index
Clusters’ diameters

76. Clustering in anomaly detection
Intra-cluster distance
The measure of compactness of a cluster (complete diameter, average diameter, centroid diameter)
Inter-cluster distance
The measure of separation between clusters (single linkage, complete linkage, average linkage, centroid linkage).

77. Example – Davies-Bouldin
Data set for clustering
Clustering into L clusters
Distance between the vectors and

78. Example – Davies-Bouldin
Davies-Bouldin index:
Inter-cluster distance
Intra-cluster distance

79. Example – Davies-Bouldin
Intra-cluster distance – Centroid diameter

80. Example – Davies-Bouldin
Inter-cluster distance – Centroid linkage

81. Clustering in anomaly detection
A clusters labelling algorithm:
Uses a combination of the Davies-Bouldin index of the clustering and the centroid diameters of the clusters.
Two clusters: “normal” and “abnormal”.
Main idea:
Attack vectors are often mutually very similar, if not identical.
Consequently, the attack cluster in the case of a massive attack is very compact.

82. Clustering in anomaly detection
Main idea (continued):
The Davies-Bouldin index of such a clustering is either zero (non-attack cluster is empty) or very close to zero.
The expected value of the centroid diameter of the attack cluster is smaller than that of the non-attack cluster.

83. Clustering in anomaly detection
Main idea (continued):
Small value of the Davies-Bouldin index indicates the existence of a massive attack
Small value of the centroid diameter indicates the attack cluster.

84. Clustering in anomaly detection
Main idea (continued):
A higher value of the Davies-Bouldin index indicates that no massive attack is taking place.
Then the attack cluster is expected to be less compact than the non-attack cluster, i.e. its centroid diameter is greater than that of the non-attack cluster (because non-massive attack vectors are very different in general).
In this case, even the cluster cardinality can be used for proper labelling.

85. Clustering in anomaly detection
The clusters labelling algorithm:
Input:
A clustering C of N vectors into 2 clusters, C1 and C2; C1 is the “non-attack” cluster, labelled with “1”.
The Davies-Bouldin index threshold, DB.
The centroid diameters difference thresholds, CD1 and CD2.
Output:
The eventually relabelled input clustering, if relabelling conditions are met.

86. Clustering in anomaly detection
The clusters labelling algorithm (cont.):
db = DaviesBouldingIndex(C) ;
cd1 = CentroidDiameter(C1) ;
cd2 = CentroidDiameter(C2) ;
if (db==0)&&(cd2==0)
Relabel(C) ;
else if (db>DeltaDB)&&(cd1>(cd2+DeltaCD1))
else if (db<DeltaDB)&&((cd1+DeltaCD2)<cd2)
Relabel(C);

87. Example – KDD cup data base
Sample size: N=1000
Number of clusters: 2
Record No. DB
CD1
CD2
Intrusion
Good labelling
(K- means)
Relabel
0-1000
1.13 N 2
0.96 Y
0.14
69.6
Y-376 3
25.19
Y-1000 1