When a network packet is formed by a computer's protocol stack, there are many components (e.g., Memory, CPU, etc.) of the computer that are involved in the process. The objective of this research is to identify, characterize and analyze the effects of the various components of a device (e.g., Memory, CPU, etc.) on the device's network traffic by measuring the changes in its network traffic with changes in its components. We also show how this characterization can be used to effectively perform counterfeit detection of devices which have counterfeit components (e.g., Memory, CPU, etc.). To obtain this characterization, we measure and apply statistical analyses like probability distribution fucntions (PDFs) on the interarrival times (IATs) of the device's network packets (e.g., ICMP, UDP, TCP, etc.). The device is then modified by changing just one component (e.g., Memory, CPU, etc.) at a time while holding the rest constant and acquiring the IATs again. This, over many such iterations provides an understanding of the effect of each component on the overall device IAT statistics. Such statistics are captured for devices (e.g., field-programmable gate arrays (FPGAs) and personal computers (PCs)) of different types. Some of these statistics remain stable across different IAT captures for the same device and differ for different devices (completely different devices or even the same device with its components changed). Hence, these statistical variations can be used to detect changes in a device's composition, which lends itself well to counterfeit detection. Counterfeit devices are abundant in today's world and cause billions of dollars of loss in revenue. Device components are substituted with inferior quality components or are replaced by lower capacity components. Armed with our understanding of the effects of various device components on the device's network traffic, we show how such substitutions or alterations of legitimate device components can be detected and hence perform effective counterfeit detection by statistically analyzing the deviation of the device's IATs from that of the original legitimate device. We perform such counterfeit detection experiments on various types of device configurations (e.g., PC with changed CPU, RAM, etc.) to prove the technique's efficacy. Since this technique is a fully network-based solution, it is also a non-destructive technique which can quickly, inexpensively and easily verify the device's legitimacy. This research also discusses the limitations of network-based counterfeit detection.

Counterfeit consumption is becoming widespread, developing into a problem of international significance. In an attempt to develop a refined understanding of the motivations and decision-making processes of consumers' deliberate counterfeit-consumption behavior, this empirical study not only integrates the theory of planned behavior and insights from self-regulatory theories, but also extends these theories by re-conceptualizing the relationships among key constructs with the inclusion of action desire. This research also introduces and integrates a new construct, namely consumers' Perceived Counterfeit Detection (PCD) by important others.Using a combination of qualitative and quantitative methods consisting of in-depth interviews and a self-administered paper questionnaire, this research empirically tested a proposed goal-striving model for deliberate counterfeit-consumption behavior, using structural equations modeling. Results demonstrated PCD's existence and supported a refined goal-striving model, based upon which effective strategies to decrease consumer's counterfeit consumption are discussed.

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