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Begin
Generate random candidates
Match self-samples?
Yes
No
Accept as new detector
No
Enough detectors?
Yes
End
Figure 4.2
Detector generation process—censoring phase of NSAs.
“Matching rules” (described in Chapter 3) are usually designed inspired by T cell/
antigen a
nity measures. A specifi c NSA is characterized by the way candidate
detectors are generated and the criteria used to select the detectors. Figure 4.2
shows the major steps in an NSA.
In the detection stage, the stored detectors (generated in the fi rst stage)
are used to check whether new incoming samples correspond to self or nonself
instances. If an input sample matches a detector, then it is identifi ed as part of
nonself, which in most applications, means that an anomaly/change has occurred
(see Figure 4.3).
Basic NS Algorithm: Generic Negative Selection Algorithm
∊
Input:
S
⊆
U
≡
Self or normal data,
l
,
r
N, where
l
is the string
length and
r
is a matching threshold
Output:
a set of detectors
D
⊆
U
1
begin
2
Generate a set (
D
) of detectors, such that each fails to match any
element in
S
.
∊
3
U by continually checking the detectors
(in
D
) against
δ
. If any detector matches
δ
, classify it as nonself.
4
end
Monitor new sample
δ
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