Muutke küpsiste eelistusi

E-raamat: Anomaly Detection Principles and Algorithms

, ,
Teised raamatud teemal:
  • Formaat - EPUB+DRM
  • Hind: 110,53 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Anomaly Detection Principles and AlgorithmsSuurem pilt
Teised raamatud teemal:

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

This book provides a readable and elegant presentation of the principles of anomaly detection,providing an easy introduction for newcomers to the field. A large number of algorithms are succinctly described, along with a presentation of their strengths and weaknesses.

The authors also cover algorithms that address different kinds of problems of interest with single and multiple time series data and multi-dimensional data. New ensemble anomaly detection algorithms are  described, utilizing the benefits provided by diverse algorithms, each of which work well on some kinds of data.

 With advancements in technology and the extensive use of the internet as a medium for communications and commerce, there has been a tremendous increase in the threats faced by individuals and organizations from attackers and criminal entities. Variations in the observable behaviors of individuals (from others and from their own past behaviors) have been found to be useful in predicting potential problems of various kinds. Hence computer scientists and statisticians have been conducting research on automatically identifying anomalies in large datasets.









 This book will primarily target practitioners and researchers who are newcomers to the area of modern anomaly detection techniques. Advanced-level students in computer science will also find this book helpful with their studies.

Arvustused

This book presents the interesting topic of anomaly detection for a very broad audience. The presentation is really useful: for each technique, some motivation is given, including real-life situations, a comprehensible formalization, and pros and cons, which gives readers an idea of how useful the technique will be in practice. Probably the most important contribution of the book is its citations and references for further reading, which may help casual readers better understand each technique . (Santiago Escobar, Computing Reviews, January, 2019)









Part I Principles
1 Introduction
3(18)
1.1 What's an Anomaly?
4(3)
1.2 Cybersecurity
7(2)
1.2.1 Privacy
7(1)
1.2.2 Malware Detection
8(1)
1.2.3 Fraudulent Email
8(1)
1.3 Finance
9(2)
1.3.1 Credit Card Fraud
9(1)
1.3.2 Creditworthiness
10(1)
1.3.3 Bankruptcy Prediction
10(1)
1.3.4 Investing
10(1)
1.4 Healthcare
11(1)
1.4.1 Diagnosis
11(1)
1.4.2 Patient Monitoring
12(1)
1.4.3 Radiology
12(1)
1.4.4 Epidemiology
12(1)
1.5 Defense and Internal Security
12(2)
1.5.1 Personnel Behaviors
13(1)
1.5.2 Battlefield Behaviors
13(1)
1.5.3 Unconventional Attacks
13(1)
1.6 Consumer Home Safety
14(2)
1.6.1 Detecting Occurrence of Falls and Other Problems
14(1)
1.6.2 Home Perimeter Safety
15(1)
1.6.3 Indoor Pollution Monitoring
15(1)
1.7 Manufacturing and Industry
16(2)
1.7.1 Quality Control
16(1)
1.7.2 Retail Sales
16(1)
1.7.3 Inventory Management
17(1)
1.7.4 Customer Behavior
17(1)
1.7.5 Employee Behavior
17(1)
1.8 Science
18(1)
1.9 Conclusion
19(2)
2 Anomaly Detection
21(12)
2.1 Anomalies
21(5)
2.1.1 Metrics for Measurement
23(1)
2.1.2 Old Problems vs. New Problems
24(1)
2.1.3 What Kind of Data?
24(1)
2.1.4 What's a Norm?
25(1)
2.2 Outliers in One-Dimensional Data
26(2)
2.3 Outliers in Multidimensional Data
28(1)
2.4 Anomaly Detection Approaches
29(1)
2.5 Evaluation Criteria
30(2)
2.6 Conclusion
32(1)
3 Distance-Based Anomaly Detection Approaches
33(8)
3.1 Introduction
33(1)
3.2 Similarity Measures
34(2)
3.3 Distance-Based Approaches
36(3)
3.3.1 Distance to All Points
36(1)
3.3.2 Distance to Nearest Neighbor
37(1)
3.3.3 Average Distance to k Nearest Neighbors
37(1)
3.3.4 Median Distance to k Nearest Neighbors
38(1)
3.4 Conclusion
39(2)
4 Clustering-Based Anomaly Detection Approaches
41(16)
4.1 Identifying Clusters
41(8)
4.1.1 Nearest Neighbor Clustering
42(1)
4.1.2 k-Means Clustering
43(2)
4.1.3 Fuzzy Clustering
45(1)
4.1.4 Agglomerative Clustering
46(1)
4.1.5 Density-Based Agglomerative Clustering
47(1)
4.1.6 Divisive Clustering
48(1)
4.2 Anomaly Detection Using Clusters
49(6)
4.2.1 Cluster Membership or Size
49(1)
4.2.2 Proximity to Other Points
50(1)
4.2.3 Proximity to Nearest Neighbor
51(1)
4.2.4 Boundary Distance
51(2)
4.2.5 When Cluster Sizes Differ
53(1)
4.2.6 Distances from Multiple Points
54(1)
4.3 Conclusion
55(2)
5 Model-Based Anomaly Detection Approaches
57(40)
5.1 Models of Relationships Between Variables
57(6)
5.1.1 Model Parameter Space Approach
58(1)
5.1.2 Data Space Approach
59(4)
5.2 Distribution Models
63(4)
5.2.1 Parametric Distribution Estimation
63(1)
5.2.2 Regression Models
64(3)
5.3 Models of Time-Varying Processes
67(11)
5.3.1 Markov Models
70(2)
5.3.2 Time Series Models
72(6)
5.4 Anomaly Detection in Time Series
78(13)
5.4.1 Anomaly Within a Single Time Series
79(5)
5.4.2 Anomaly Detection Among Multiple Time Series
84(7)
5.5 Learning Algorithms Used to Derive Models from Data
91(2)
5.5.1 Regularization
92(1)
5.6 Conclusion
93(4)
Part II Algorithms
6 Distance and Density Based Approaches
97(22)
6.1 Distance from the Rest of the Data
97(5)
6.1.1 Distance Based-Outlier Approach
100(2)
6.2 Local Correlation Integral (LOCI) Algorithm
102(3)
6.2.1 Resolution-Based Outlier Detection
104(1)
6.3 Nearest Neighbor Approach
105(2)
6.4 Density Based Approaches
107(9)
6.4.1 Mixture Density Estimation
109(1)
6.4.2 Local Outlier Factor (LOF) Algorithm
110(2)
6.4.3 Connectivity-Based Outlier Factor (COF) Approach
112(2)
6.4.4 INFLuential Measure of Outlierness by Symmetric Relationship (INFLO)
114(2)
6.5 Performance Comparisons
116(1)
6.6 Conclusions
117(2)
7 Rank Based Approaches
119(16)
7.1 Rank-Based Detection Algorithm (RBDA)
121(3)
7.1.1 Why Does RBDA Work?
122(2)
7.2 Anomaly Detection Algorithms Based on Clustering and Weighted Ranks
124(3)
7.2.1 NC-Clustering
125(1)
7.2.2 Density and Rank Based Detection Algorithms
126(1)
7.3 New Algorithms Based on Distance and Cluster Density
127(3)
7.4 Results
130(4)
7.4.1 RBDA Versus the Kernel Based Density Estimation Algorithm
130(1)
7.4.2 Comparison of RBDA and Its Extensions with LOF, COF, and INFLO
131(2)
7.4.3 Comparison for KDD99 and Packed Executables Datasets
133(1)
7.5 Conclusions
134(1)
8 Ensemble Methods
135(18)
8.1 Independent Ensemble Methods
135(4)
8.2 Sequential Application of Algorithms
139(1)
8.3 Ensemble Anomaly Detection with Adaptive Sampling
140(3)
8.3.1 AdaBoost
141(1)
8.3.2 Adaptive Sampling
142(1)
8.3.3 Minimum Margin Approach
142(1)
8.4 Weighted Adaptive Sampling
143(9)
8.4.1 Weighted Adaptive Sampling Algorithm
147(1)
8.4.2 Comparative Results
147(1)
8.4.3 Dataset Description
148(1)
8.4.4 Performance Comparisons
148(3)
8.4.5 Effect of Model Parameters
151(1)
8.5 Conclusion
152(1)
9 Algorithms for Time Series Data
153(38)
9.1 Problem Definition
154(3)
9.2 Identification of an Anomalous Time Series
157(10)
9.2.1 Algorithm Categories
158(1)
9.2.2 Distances and Transformations
159(8)
9.3 Abnormal Subsequence Detection
167(2)
9.4 Outlier Detection Based on Multiple Measures
169(3)
9.4.1 Measure Selection
169(3)
9.4.2 Identification of Anomalous Series
172(1)
9.5 Online Anomaly Detection for Time Series
172(10)
9.5.1 Online Updating of Distance Measures
173(3)
9.5.2 Multiple Measure Based Abnormal Subsequence Detection Algorithm (MUASD)
176(2)
9.5.3 Finding Nearest Neighbor by Early Abandoning
178(1)
9.5.4 Finding Abnormal Subsequence Based on Ratio of Frequencies (SAXFR)
179(2)
9.5.5 MUASD Algorithm
181(1)
9.6 Experimental Results
182(6)
9.6.1 Detection of Anomalous Series in a Dataset
182(1)
9.6.2 Online Anomaly Detection
183(3)
9.6.3 Anomalous Subsequence Detection
186(2)
9.6.4 Computational Effort
188(1)
9.7 Conclusion
188(3)
Appendix A Datasets for Evaluation
191(4)
A.1 A Datasets for Evaluation
191(1)
A.2 Real Datasets
191(3)
A.3 KDD and PED
194(1)
Appendix B Datasets for Time Series Experiments
195(14)
B.1 Datasets
195(14)
B.1.1 Synthetic Datasets
195(1)
B.1.2 Brief Description of Datasets
195(7)
B.1.3 Datasets for Online Anomalous Time Series Detection
202(1)
B.1.4 Data Sets for Abnormal Subsequence Detection in a Single Series
203(1)
B.1.5 Results for Abnormal Subsequence Detection in a Single Series for Various Datasets
203(6)
References 209(6)
Index 215
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97833196752686e.html