With the increase in globalization of Integrated Circuit (IC) design and production, hardware trojans have become a serious threat to manufacturers as well as consumers. These trojans could be intensionally or accidentally embedded in ICs to make a system vulnerable to hardware attacks. The implementation of critical applications using ICs makes the effect of trojans an even more serious problem. Moreover, the presence of untrusted foundries and designs cannot be eliminated since the need for ICs is growing exponentially and the use of third party software tools to design the circuits is now common. In addition if a trusted foundry for fabrication has to be developed, it involves a huge investment. Therefore, hardware trojan detection techniques are essential. Very Large Scale Integration (VLSI) system designers must now consider the security of a system against internal and external hardware attacks. Many hardware attacks rely on system vulnerabilities. Moreover, an attacker may rely on deprocessing and reverse engineering to study the internal structure of a system to reveal the system functionality in order to steal secret keys or copy the system. Thus hardware security is a major challenge for the hardware industry. Many hardware attack mitigation techniques have been proposed to help system designers build secure systems that can resist hardware attacks during the design stage, while others protect the system against attacks during operation. In this dissertation, the idea of quantifying hardware attacks, hardware trojans, and hardware trojan detection techniques is introduced. We analyze and classify hardware attacks into risk levels based on three dimensions Accessibility/Resources/Time (ART). We propose a methodology and algorithms to aid the attacker/defender to select/predict the hardware attacks that could use/threaten the system based on the attacker/defender capabilities. Because many of these attacks depends on hardware trojans embedded in the system, we propose a comprehensive hardware trojan classification based on hardware trojan attributes divided into eight categories. An adjacency matrix is generated based on the internal relationship between the attributes within a category and external relationship between attributes in different categories. We propose a methodology to generate a trojan life-cycle based on attributes determined by an attacker/defender to build/investigate a trojan. Trojan identification and severity are studied to provide a systematic way to compare trojans. Trojan detection identification and coverage is also studied to provide a systematic way to compare detection techniques and measure their e effectiveness related to trojan severity. We classify hardware attack mitigation techniques based on the hardware attack risk levels. Finally, we match these techniques to the attacks the could countermeasure to help defenders select appropriate techniques to protect their systems against potential hardware attacks. / Graduate / 0544 / 0984 / samerm@uvic.ca

Software Defined Networks (SDN) has created great potential and hope to overcome the need for secure, reliable and well managed next generation networks to drive effective service delivery on the go and meet the demand for high data rate and seamless connectivity expected by users. Thus, it is a network technology that is set to enhance our day-to-day activities. As network usage and reliance on computer technology are increasing and popular, users with bad intentions exploit the inherent weakness of this technology to render targeted services unavailable to legitimate users. Among the security weaknesses of SDN is Distributed Denial of Service (DDoS) attacks. Even though DDoS attack strategy is known, the number of successful DDoS attacks launched has seen an increment at an alarming rate over the last decade. Existing detection mechanisms depend on signatures of known attacks which has not been successful in detecting unknown or different shades of DDoS attacks. Therefore, a novel detection mechanism that relies on deviation from confidence interval obtained from the normal distribution of throughput polled without attack from the server. Furthermore, sensitivity analysis to determine which of the network metrics (jitter, throughput and response time) is more sensitive to attack by introducing white Gaussian noise and evaluating the local sensitivity using feed-forward artificial neural network is evaluated. All metrics are sensitive in detecting DDoS attacks. However, jitter appears to be the most sensitive to attack. As a result, the developed framework provides an avenue to make the SDN technology more robust and secure to DDoS attacks.

This thesis is aimed at monitoring of computer networks using NetFlow data. It describes main aspects of detection network anomalies using IDS systems. Next part describes Nemea framework, which is used for creating modules. These modules are able to detect network incidents and attacks. Following chapters contain a brief overview of common network attacks with their specific remarks which can help in process of their detection. Based on this analysis, the concept of mitigation rules was created. These rules can be used for mitigation of DDoS attack. This method was tested on several data sets and it produced multiple mitigation rules. These rules were applied on data sets and they marked most of the suspicious flows.