Читать книгу Intelligent Security Systems - Leon Reznik - Страница 4

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 3.14 Detection accuracy (%) of RBF‐based IDS with respect to the trai...Figure 3.15 Training time versus the size of the training set for RBF.Figure 3.16 Investigation of crossover operator choice and the number of gen...Figure 3.17 Investigation of mutation mechanisms.Figure 3.18 Investigation of ES systems.Figure 3.19 Perfect versus good neural networks.Figure 3.20 Employees number vs. attack detection error rate.Figure 3.21 Adaptability vs. attack classification error rate.Figure 3.22 Screenshot of SNORT configuration validation.Figure 3.23 Suricata interface view.Figure 3.24 Zeek interface view.

5 Chapter 4Figure 4.1 Malware history timeline.Figure 4.2 Copy of the screenshot produced by Christma virus on the victim's...Figure 4.3 Malware classification scheme.Figure 4.4 Virus classification.Figure 4.5 Polymorphic engine controls virus execution and mutation.Figure 4.6 Metamorphic virus operation.Figure 4.7 Scanning techniques used by worms for self‐propagation.Figure 4.8 Trojan horses classification.Figure 4.9 Ransomware history timeline.Figure 4.10 Rootkits classification.Figure 4.11 Ensemble classifier architecture.Figure 4.12 A generic time‐based MLP. The inputs are previous values of the ...Figure 4.13 MTBMLP structure with three behavioral signals (X, Y, Z) used as...Figure 4.14 Multiple behavior signals change detection.Figure 4.15 Popular Windows anti‐malware tools market share.Figure 4.16 Diverse file scan modes. (a) The case of virus embedded in the m...

6 Chapter 5Figure 5.1 The late Ralph Barclay shows off his box in 2009.Figure 5.2 Relationship between professional hackers’ groups.Figure 5.3 Hacker’s classification attempt.Figure 5.4 Phases of typical hacker’s activities.Figure 5.5 Advanced Port Scanner tool GUI (https://www.advanced‐port‐scanner...Figure 5.6 Hacker’s attacks scheme and their detection system.Figure 5.7 Colluded application attack data flow model.Figure 5.8 Recordings of the technological signals and their change during t...Figure 5.9 Basic architecture for a simple RNN model.Figure 5.10 Basic architecture for an LSTM model.Figure 5.11 LSTM model parameters generated by Tensor‐Flow.Figure 5.12 Basic architecture for a GRU model.Figure 5.13 GRU model parameters generated by Tensor‐Flow.Figure 5.14 Loss function plot from GRU versus LSTM using preprocessed datas...Figure 5.15 Loss function plot from GRU versus LSTM using raw dataset.Figure 5.16 Detection accuracy of both GRU and LSTM models that use preproce...Figure 5.17 Detection accuracy of both GRU and LSTM models that use raw data...Figure 5.18 Android application screenshots.Figure 5.19 Major occupation representations in survey respondents.Figure 5.20 Antimalware usage in the selected occupations.Figure 5.21 Virus and malware infection reports from selected respondent occ...Figure 5.22 Relative risk of password protection.Figure 5.23 Relative risk of reused passwords.Figure 5.24 Mobile device security evaluation structure.Figure 5.25 Membership functions for input representing OS version.Figure 5.26 Membership functions for Device Feature Security output.Figure 5.27 Authentication system structure and operation.Figure 5.28 KeyCollector utility GUI.Figure 5.29 Major primary typing features.Figure 5.30 The TOR website fingerprinting threat model.Figure 5.31 The WF attack workflow: the black arrow represents the processes...Figure 5.32 DF attack model architecture.Figure 5.33 Attack model performance (closed‐world scenario).Figure 5.34 Visual explanation of extend bursts and break bursts padding. Th...

7 Chapter 6Figure 6.1 AML taxonomy of attacks, defenses, and consequences – from.Figure 6.2 Adversarial machine learning attack taxonomy.Figure 6.3 Adversarial machine learning attack classification.Figure 6.4 Accuracy degradation plot for J48 with missing values induction....Figure 6.5 Accuracy degradation plot for random forest with missing values i...Figure 6.6 Accuracy degradation plot for J48 with invalid values induction....Figure 6.7 Accuracy degradation plot for Random Forest with invalid values i...Figure 6.8 Accuracy degradation plot for J48 with errors induction.Figure 6.9 Accuracy degradation plot for Random Forest with errors induction...Figure 6.10 Basic GAN structure.Figure 6.11 GAN with generator G, which generates images, and discriminator Figure 6.12 Semi‐supervised GAN can be used not only with labeled data in su...Figure 6.13 GAN minimax loss has opposite ideal conditions for each of the n...Figure 6.14 Performance comparison of conventional classifier versus semi‐su...Figure 6.15 Performance comparison of conventional classifier VS semi‐superv...