Modern day computer networks have become very complex and attackers have benefited due to this complexity and have found vulnerabilities and loopholes in the network architecture. In order to identify the attacks from an attacker all aspects of network architecture needs to be carefully examined such as packet headers, network scans, versions of applications, network scans, network anomalies etc. and after the examination attributes playing a significant impact on the security posture of the organization needs to be highlighted so that resources and efforts are directed towards those attributes. In this work we extensively look at network traffic at dormitory network of a large campus and try to identify the attributes that play a significant role in the infection of a machine. Our scheme is to collect as much attributes from the network traffic applying the heuristic of network infection and then devise a scheme called decision centric rank ordering of security metric that gives the priority to the security metrics so that network administrators can channel their efforts in the right direction. Another aspect of this research is to identify the probability of an attack on a communication infrastructure. A communication infrastructure becomes prone to attack if certain elements exist in it, such as vulnerabilities in the comprising elements of the system, existence of an attacker and motivation for him to attack. Focus of this study is on vulnerability assessment and security metrics such as user behavior, operating systems, user applications, and software updates. To achieve a quantified value of risk, a set of machines is carefully observed for the security metrics. Statistical analysis is applied on the data collected from compromised machines and the quantified value of risk is achieved.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/47527 |
| Date | 30 April 2013 |
| Creators | Khan, Moazzam |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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