Mastering Machine Learning for Penetration Testing : Develop an Extensive Skill Set to Break Self-Learning Systems Using Python.

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We live in an era where cyber security plays an important role. As systems are getting smarter, we now see machine learning interrupting computer security. With the adoption of machine learning in upcoming security products, it's important for pentesters and security researchers to understand h...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Chebbi, Chiheb
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham : Packt Publishing Ltd, 2018.
Temas:
Python.
Penetration testing.
Computer security.
Cybernetics & systems theory.
Machine learning.
Mathematical theory of computation.
Computers > Cybernetics.
Computers > Machine Theory.
Computers > Security > General.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Cover; Title Page; Copyright and Credits; Dedication; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Introduction to Machine Learning in Pentesting; Technical requirements; Artificial intelligence and machine learning ; Machine learning models and algorithms ; Supervised; Bayesian classifiers; Support vector machines; Decision trees ; Semi-supervised; Unsupervised; Artificial neural networks ; Linear regression ; Logistic regression; Clustering with k-means ; Reinforcement; Performance evaluation ; Dimensionality reduction; Improving classification with ensemble learning.
  • Machine learning development environments and Python librariesNumPy; SciPy; TensorFlow; Keras; pandas; Matplotlib; scikit-learn; NLTK; Theano; Machine learning in penetration testing
  • promises and challenges; Deep Exploit; Summary; Questions; Further reading; Chapter 2: Phishing Domain Detection; Technical requirements; Social engineering overview; Social Engineering Engagement Framework; Steps of social engineering penetration testing; Building real-time phishing attack detectors using different machine learning models; Phishing detection with logistic regression.
  • Phishing detection with decision treesNLP in-depth overview; Open source NLP libraries; Spam detection with NLTK; Summary; Questions; Chapter 3: Malware Detection with API Calls and PE Headers; Technical requirements; Malware overview; Malware analysis ; Static malware analysis; Dynamic malware analysis; Memory malware analysis; Evasion techniques; Portable Executable format files ; Machine learning malware detection using PE headers ; Machine learning malware detection using API calls; Summary; Questions; Further reading; Chapter 4: Malware Detection with Deep Learning.
  • Technical requirementsArtificial neural network overview; Implementing neural networks in Python; Deep learning model using PE headers; Deep learning model with convolutional neural networks and malware visualization; Convolutional Neural Networks (CNNs); Recurrent Neural Networks (RNNs); Long Short Term Memory networks; Hopfield networks; Boltzmann machine networks; Malware detection with CNNs; Promises and challenges in applying deep learning to malware detection; Summary; Questions; Further reading; Chapter 5: Botnet Detection with Machine Learning; Technical requirements; Botnet overview.
  • Building a botnet detector model with multiple machine learning techniquesHow to build a Twitter bot detector; Visualization with seaborn; Summary; Questions; Further reading; Chapter 6: Machine Learning in Anomaly Detection Systems; Technical requirements; An overview of anomaly detection techniques; Static rules technique; Network attacks taxonomy; The detection of network anomalies; HIDS; NIDS; Anomaly-based IDS; Building your own IDS; The Kale stack; Summary; Questions; Further reading; Chapter 7: Detecting Advanced Persistent Threats; Technical requirements; Threats and risk analysis.

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