Intelligent Security Systems. Leon Reznik. Читать онлайн. Newlib. NEWLIB.NET

Автор: Leon Reznik
Издательство: John Wiley & Sons Limited
Серия:
Жанр произведения: Программы
Год издания: 0
isbn: 9781119771562
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rel="nofollow" href="#u5d62f943-21c2-59ba-93aa-ccc2c1ef9a80">6 Adversarial Machine Learning 6.1 Adversarial Machine Learning Definition 6.2 Adversarial Attack Taxonomy 6.3 Defense Strategies 6.4 Investigation of the Adversarial Attacks Influence on the Classifier Performance Use Case 6.5 Generative Adversarial Networks Review Questions Exercises References

      14  Index

      15  End User License Agreement

      List of Tables

      1 Chapter 1Table 1.1 Possible security threats against assets.Table 1.2 Comparison between artificial intelligence, machine learning, and...Table 1.3 Comparison between conventional and AI approaches to coding and s...Table 1.4 Comparison between human, expert system, and conventional program...Table 1.5 Expert systems: good and bad.Table 1.6 Input variables for air quality control neuro‐fuzzy system.Table 1.7 Comparison between supervised and unsupervised ML technique approa...

      2 Chapter 2Table 2.1 Firewall generations and types.Table 2.2 Basic firewall architectures.Table 2.3 Policy excerpt example.Table 2.4 Motorola SBG9000 Router rules presentation.Table 2.5 Protocol/service and port number association.Table 2.6 Conventional vs. NG firewalls.

      3 Chapter 3Table 3.1 Features of network‐based and host‐based IDS.Table 3.2 Features of varios IDS.Table 3.3 Comparison of various IDS configurations.Table 3.4 Comparison of intrusion detection methodologies.Table 3.5 Signature vs. anomaly.Table 3.6 The four different outcomes of IDS classification.Table 3.7 One attack recognition performance with RBF and MLP models.Table 3.8 Unknown attack recognition performance with RBF and MLP models.Table 3.9 Speeding up training with GA optimization.Table 3.10 IDS tools and systems.Table 3.11 Comparison of NIDS development tools capabilities.Table 3.12 Comparison of HIDS development tools capabilities.

      4 Chapter 4Table 4.1 Virus genealogy.Table 4.2 Malware types comparison.Table 4.3 Comparison between metamorphic and polymorphic viruses.Table 4.4 Malware detection techniques comparison.

      5 Chapter 5Table 5.1 Affirmative responses to password‐related questions.Table 5.2 Error margins for responses.Table 5.3 Number of responses.Table 5.4 Range of password use rates.Table 5.5 Range of filesharing rates.Table 5.6 Dangerous permission groups.Table 5.7 System’s parameters gathered by the library.Table 5.8 CNN hyperparameters choice.

      6 Chapter 6Table 6.1 Classifier performance changes.

      List of Illustrations

      1 IntroductionFigure I.1 Book organization.

      2 Chapter 1Figure 1.1 Security threats get close to you through networking.Figure 1.2 New technologies and applications, such as self‐driving cars and ...Figure 1.3 Attack sophistication vs. Intruder knowledge.Figure 1.4 Cyberattack category rates.Figure 1.5 Median dwell time (how long it takes to detect an intrusion).Figure 1.6 Computer security = confidentiality + integrity + availability.Figure 1.7 IoT, wireless devices, and sensor network attacks classification....Figure 1.8 Brief history of AI achievements.Figure 1.9 Data science model: from plan to implementation and performance m...Figure 1.10 Relationship between various disciplines and fields.Figure 1.11 Comparison between conventional and AI approaches to coding and ...Figure 1.12 AI techniques.Figure 1.13 An expert system composition and operation.Figure 1.14 Fuzzification of crisp inputs.Figure 1.15 Typical architecture of the neuro‐fuzzy system.Figure 1.16 ML algorithms families and classification.Figure 1.17 Examples of ML algorithms.Figure 1.18 Processing element (neuron) with an output determined as (a) wei...Figure 1.19 Multilayer perceptron topology.Figure 1.20 The list of ANN models.Figure 1.21 Recurrent neural network topology (see the feedback loop arrow a...Figure 1.22 A basic architecture of convolutional neural networks (CNN).Figure 1.23 Autoencoder structure.Figure 1.24 Genetic algorithm operations.

      3 Chapter 2Figure 2.1 Firewall ancestors and history of development.Figure 2.2 Software vs. hardware firewalls.Figure 2.3 TCP/IP application stack.Figure 2.4 Firewall design and implementation process.Figure 2.5 Screenshot of Windows Firewall rules sample and their interpretat...Figure 2.6 Netgear Router Firewall policy rules.Figure 2.7 Avast Firewall set up.Figure 2.8 Rule assignment in Zone Alarm.Figure 2.9 Rules management and automatic generation in McAfee Firewall.Figure 2.10 Rules order based on group policies in Windows Firewall.Figure 2.11 Indication of rules conflict.Figure 2.12 Gartner Magic quadrants: (a) Network firewallsand (b) web ap...Figure 2.13 Comparing firewall solutions guidelines.Figure 2.14 Dynamic firewall modification with a machine learning‐based anal...

      4 Chapter 3Figure 3.1 An IDS place and functionality.Figure 3.2 The typical intrusion process unfold in time.Figure 3.3 IDS history: from a concept to implementations.Figure 3.4 A typical IDS structure and functionality.Figure 3.5 The various IDS implementation options.Figure 3.6 Boyer–Moore string‐search algorithm.Figure 3.7 Anomaly based intrusion detection typical structure.Figure 3.8 IDS performance major metrics.Figure 3.9 IDS performance evaluation with the confusion matrix.Figure 3.10 k‐Means data points and centroids on an example dataset.Figure 3.11 The effects of a varying distance on IDS classification.Figure 3.12 GA method flowchart.Figure 3.13 The average training error change on the number of training epoc...Figure