Электронный каталог

DDC 005.133
K 42
Khrais, Hussam.
    Python for Offensive PenTest : : a practical guide to ethical hacking and penetration testing using Python. / Hussam. Khrais. - Birmingham : : Packt Publishing,, 2018. - 1 online resource (169 pages). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/6D06ADF0-4ECF-4C75-8AF8-41A16E945C21. - ISBN 9781788832465 (electronic bk.). - ISBN 1788832469 (electronic bk.)
Print version record.
Параллельные издания: Print version: : Khrais, Hussam. Python for Offensive PenTest : A practical guide to ethical hacking and penetration testing using Python. - Birmingham : Packt Publishing, ©2018
    Содержание:
Cover; Title Page; Copyright and Credits; Packt Upsell; Contributors; Table of Contents; Preface; Chapter 1: Warming up -- Your First Antivirus-Free Persistence Shell; Preparing the attacker machine; Setting up internet access; Preparing the target machine; TCP reverse shell; Coding a TCP reverse shell; Server side; Client side; Data exfiltration -- TCP; Server side; Client side; Exporting to EXE; HTTP reverse shell; Coding the HTTP reverse shell; Server side; Client side; Data exfiltration -- HTTP; Client side; Server side; Exporting to EXE; Persistence; Making putty.exe persistent.
Making a persistent HTTP reverse shellTuning the connection attempts; Tips for preventing a shell breakdown; Countermeasures; Summary; Chapter 2: Advanced Scriptable Shell; Dynamic DNS; DNS aware shell; Interacting with Twitter; Parsing a tweet in three lines; Countermeasures; Replicating Metasploit's screen capturing; Replicating Metasploit searching for content; Target directory navigation; Integrating low-level port scanner; Summary; Chapter 3: Password Hacking; Antivirus free keylogger; Installing pyHook and pywin; Adding code to keylogger; Hijacking KeePass password manager.
Man in the browserFirefox process; Firefox API hooking with Immunity Debugger; Python in Firefox proof of concept (PoC); Python in Firefox EXE; Dumping saved passwords out of Google Chrome; Acquiring the password remotely; Submitting the recovered password over HTTP session; Testing the file against antivirus; Password phishing -- DNS poisoning; Using Python script; Facebook password phishing; Countermeasures; Securing the online account; Securing your computer; Securing your network; Keeping a watch on any suspicious activity; Summary; Chapter 4: Catch Me If You Can!
Bypassing host-based firewallsHijacking IE; Bypassing reputation filtering in next generation firewalls; Interacting with SourceForge; Interacting with Google Forms; Bypassing botnet filtering; Bypassing IPS with handmade XOR encryption; Summary; Chapter 5: Miscellaneous Fun in Windows; Privilege escalation -- weak service file; Privilege escalation -- preparing vulnerable software; Privilege escalation -- backdooring legitimate windows service; Privilege escalation -- creating a new admin account and covering the tracks; Summary; Chapter 6: Abuse of Cryptography by Malware.
Introduction to encryption algorithmsProtecting your tunnel with AES -- stream mode; Cipher Block Chaining (CBC) mode encryption; Counter (CTR) mode encryption ; Protecting your tunnel with RSA; Hybrid encryption key; Summary; Other Books You May Enjoy; Index.

Khrais, Hussam. Python for Offensive PenTest : [Электронный ресурс] : a practical guide to ethical hacking and penetration testing using Python. / Hussam. Khrais, 2018. - 1 online resource (169 pages) с. (Введено оглавление)

DDC 005.13/3
O-81
Ortega, José Manuel.
    Mastering Python for networking and security [[electronic resource] :] : leverage Python scripts and libraries to overcome networking and security issues. / José Manuel. Ortega. - Birmingham : : Packt,, 2018. - 1 online resource (415 p.). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/B22D13CB-B8C5-4AF2-BA19-30DCB3B0FE39. - ISBN 9781788990707 (electronic bk.). - ISBN 1788990706 (electronic bk.)
Online resource; title from PDF title page (EBSCO, viewed October 17, 2018)

Ortega, José Manuel. Mastering Python for networking and security [[electronic resource] :] : leverage Python scripts and libraries to overcome networking and security issues. / José Manuel. Ortega, 2018. - 1 online resource (415 p.) с.

DDC 005.133
S 17
Saleh, Hyatt,.
    Machine learning fundamentals : : Use Python and scikit-learn to get up and running with the hottest developments in machine learning / / Hyatt Saleh. - Birmingham : : Packt Publishing,, ©2018. - 1 online resource (240 p.). - URL: https://library.dvfu.ru/lib/document/SK_ELIB/CE938EA4-6316-42FD-8CB4-3B924C055972. - ISBN 1789801761 (electronic bk.). - ISBN 9781789801767 (electronic bk.)
Description based upon print version of record. Supervised Learning Algorithms: Predict Annual Income
Параллельные издания: Print version: : Saleh, Hyatt Machine Learning Fundamentals : Use Python and Scikit-Learn to Get up and Running with the Hottest Developments in Machine Learning. - Birmingham : Packt Publishing Ltd,c2018. - ISBN 9781789803556
    Содержание:
Intro; Preface; Introduction to Scikit-Learn; Introduction; Scikit-Learn; Advantages of Scikit-Learn; Disadvantages of Scikit-Learn; Data Representation; Tables of Data; Features and Target Matrices; Exercise 1: Loading a Sample Dataset and Creating the Features and Target Matrices; Activity 1: Selecting a Target Feature and Creating a Target Matrix; Data Preprocessing; Messy Data; Exercise 2: Dealing with Messy Data; Dealing with Categorical Features; Exercise 3: Applying Feature Engineering over Text Data; Rescaling Data; Exercise 4: Normalizing and Standardizing Data
Activity 2: Preprocessing an Entire DatasetScikit-Learn API; How Does It Work?; Supervised and Unsupervised Learning; Supervised Learning; Unsupervised Learning; Summary; Unsupervised Learning: Real-Life Applications; Introduction; Clustering; Clustering Types; Applications of Clustering; Exploring a Dataset: Wholesale Customers Dataset; Understanding the Dataset; Data Visualization; Loading the Dataset Using Pandas; Visualization Tools; Exercise 5: Plotting a Histogram of One Feature from the Noisy Circles Dataset; Activity 3: Using Data Visualization to Aid the Preprocessing Process
K-means AlgorithmUnderstanding the Algorithm; Exercise 6: Importing and Training the k-means Algorithm over a Dataset; Activity 4: Applying the k-means Algorithm to a Dataset; Mean-Shift Algorithm; Understanding the Algorithm; Exercise 7: Importing and Training the Mean-Shift Algorithm over a Dataset; Activity 5: Applying the Mean-Shift Algorithm to a Dataset; DBSCAN Algorithm; Understanding the Algorithm; Exercise 8: Importing and Training the DBSCAN Algorithm over a Dataset; Activity 6: Applying the DBSCAN Algorithm to the Dataset; Evaluating the Performance of Clusters
Available Metrics in Scikit-LearnExercise 9: Evaluating the Silhouette Coefficient Score and Calinski-Harabasz Index; Activity 7: Measuring and Comparing the Performance of the Algorithms; Summary; Supervised Learning: Key Steps; Introduction; Model Validation and Testing; Data Partition; Split Ratio; Exercise 10: Performing Data Partition over a Sample Dataset; Cross Validation; Exercise 11: Using Cross-Validation to Partition the Train Set into a Training and a Validation Set; Activity 8: Data Partition over a Handwritten Digit Dataset; Evaluation Metrics
Evaluation Metrics for Classification TasksExercise 12: Calculating Different Evaluation Metrics over a Classification Task; Choosing an Evaluation Metric; Evaluation Metrics for Regression Tasks; Exercise 13: Calculating Evaluation Metrics over a Regression Task; Activity 9: Evaluating the Performance of the Model Trained over a Handwritten Dataset; Error Analysis; Bias, Variance, and Data Mismatch; Exercise 14: Calculating the Error Rate over Different Sets of Data; Activity 10: Performing Error Analysis over a Model Trained to Recognize Handwritten Digits; Summary

Saleh, Hyatt,. Machine learning fundamentals : [Электронный ресурс] : Use Python and scikit-learn to get up and running with the hottest developments in machine learning / / Hyatt Saleh., ©2018. - 1 online resource (240 p.) с. (Введено оглавление)

DDC 005.133
L 82
Liu, Yuxi (Hayden),.
    Python machine learning by example : : easy-to-follow examples that get you up and running with machine learning / / Yuxi (Hayden) Liu. - Second edition. - Birmingham, UK : : Packt Publishing,, 2019. - 1 online resource. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/13BEB123-CD2E-42D2-AE3F-47D088541895. - ISBN 9781789617559 (electronic bk.). - ISBN 1789617553 (electronic bk.)
Online resource; title from PDF title page (EBSCO, viewed April 8, 2019)

Liu, Yuxi (Hayden),. Python machine learning by example : [Электронный ресурс] : easy-to-follow examples that get you up and running with machine learning / / Yuxi (Hayden) Liu., 2019. - 1 online resource с.

DDC 005.133
C 51
Chebbi, Chiheb,.
    Mastering machine learning for penetration testing : : develop an extensive skill set to break self-learning systems using Python / / Chiheb Chebbi. - Birmingham, UK : : Packt Publishing,, ©2018. - 1 online resource (1 volume) : : il. - Includes bibliographical references. - URL: https://library.dvfu.ru/lib/document/SK_ELIB/542F28A9-1026-41E5-900D-D221B10A03C9. - ISBN 9781788993111 (electronic book). - ISBN 178899311X (electronic book)
Description based on online resource; title from digital title page (viewed on April 15, 2019).

Chebbi, Chiheb,. Mastering machine learning for penetration testing : [Электронный ресурс] : develop an extensive skill set to break self-learning systems using Python / / Chiheb Chebbi., ©2018. - 1 online resource (1 volume) : с.