Cyber-security Risk Assessment

Cyber-security domain is inherently dynamic. Not only does system configuration changes frequently (with new releases and patches), but also new attacks and vulnerabilities are regularly discovered. The threat in cyber-security is human, and hence intelligent in nature. The attacker adapts to the situation, target environment, and countermeasures. Attack actions are also driven by attacker's exploratory nature, thought process, motivation, strategy, and preferences. Current security risk assessment is driven by cyber-security expert's theories about this attacker behavior. The goal of this dissertation is to automatically generate the cyber-security risk scenarios by: * Capturing diverse and dispersed cyber-security knowledge * Assuming that there are unknowns in the cyber-security domain, and new knowledge is available frequently * Emulating the attacker's exploratory nature, thought process, motivation, strategy, preferences and his/her interaction with the target environment * Using the cyber-security expert's theories about attacker behavior The proposed framework is designed by using the unique cyber-security domain requirements identified in this dissertation and by overcoming the limitations of current risk scenario generation frameworks. The proposed framework automates the risk scenario generation by using the knowledge as it becomes available (or changes). It supports observing, encoding, validating, and calibrating cyber-security expert's theories. It can also be used for assisting the red-teaming process. The proposed framework generates ranked attack trees and encodes the attacker behavior theories. These can be used for prioritizing vulnerability remediation. The proposed framework is currently being extended for developing an automated threat response framework that can be used to analyze and recommend countermeasures. This framework contains behavior driven countermeasures that uses the attacker behavior theories to lead the attacker away from the system to be protected

An experimental evaluation to determine if port scans are precursors to an attack

This paper describes an experimental approach to determine the correlation between port scans and attacks. Discussions in the security community often state that port scans should be considered as precursors to an attack. However, very few studies have been conducted to quantify the validity of this hypothesis. In this paper, attack data were collected using a test-bed dedicated to monitoring attackers. The data collected consist of port scans, ICMP scans, vulnerability scans, successful attacks and management traffic. Two experiments were performed to validate the hypothesis of linking port scans and vulnerability scans to the number of packets observed per connection. Customized scripts were then developed to filter the collected data and group them on the basis of scans and attacks between a source and destination IP address pair. The correlation of the filtered data groups was assessed. The analyzed data consists of forty-eight days of data collection for two target computers on a heavily utilized subnet. 1