Predicting Malicious Behavior

Predicting Malicious Behavior

A groundbreaking exploration of how to identify and fight security threats at every level This revolutionary book combines real-world security scenarios with actual tools to predict and prevent incidents of terrorism, network hacking, individual criminal behavior, and more. Written by an expert with intelligence officer experience who invented the technology, it explores the keys to understanding the dark side of human nature, various types of security threats (current and potential), and how to construct a methodology to predict and combat malicious behavior. The companion CD demonstrates available detection and prediction systems and presents a walkthrough on how to conduct a predictive analysis that highlights proactive security measures. Guides you through the process of predicting malicious behavior, using real world examples and how malicious behavior may be prevented in the future Illustrates ways to understand malicious intent, dissect behavior, and apply the available tools and methods for enhancing security Covers the methodology for predicting malicious behavior, how to apply a predictive methodology, and tools for predicting the likelihood of domestic and global threats CD includes a series of walkthroughs demonstrating how to obtain a predictive analysis and how to use various available tools, including Automated Behavior Analysis Predicting Malicious Behavior fuses the behavioral and computer sciences to enlighten anyone concerned with security and to aid professionals in keeping our world safer.

You Shall Not Pass! Measuring, Predicting, and Detecting Malware Behavior

Researchers have been fighting malicious behavior on the Internet for several decades. The arms race is far from being close to an end, but this PhD work is intended to be another step towards the goal of making the Internet a safer place. My PhD has focused on measuring, predicting, and detecting malicious behavior on the Internet; we focused our efforts towards three different paths: establishing causality relations into malicious actions, predicting the actions taken by an attacker, and detecting malicious software. This work tried to understand the causes of malicious behavior in different scenarios (sandboxing, web browsing), by applying a novel statistical framework and statistical tests to determine what triggers malware. We also used deep learning algorithms to predict what actions an attacker would perform, with the goal of anticipating and countering the attacker"s moves. Moreover, we worked on malware detection for Android, by modeling sequences of API with Markov Chains and applying machine learning algorithms to classify benign and malicious apps. The methodology, design, and results of our research are relevant state of the art in the field; we will go through the different contributions that we worked on during my PhD to explain the design choices, the statistical methods and the takeaways characterizing them. We will show how these systems have an impact on current tools development and future research trends.

Exposing Internet Address Use to Enhance Network Security