A Deep Learning Approach for Identifying User Interest from Targeted Advertising
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In the Internet of Things (IoT) era, the types of devices used by one user are becoming more diverse and thenumber of devices is also increasing. However, a forensic investigator is restricted to exploit or collect all theuser’s devices; there are legal issues (e.g., privacy, jurisdiction) and technical issues (e.g., computing resources,the increase in storage capacity). Therefore, in the digital forensics field, it has been a challenge to acquireinformation that remains on the devices that could not be collected, by analyzing the seized devices. In thisstudy, we focus on the fact that multiple devices share data through account synchronization of the onlineplatform. We propose a novel way of identifying the user's interest through analyzing the remnants of targetedadvertising which is provided based on the visited websites or search terms of logged-in users. We introduce adetailed methodology to pick out the targeted advertising from cache data and infer the user’s interest usingdeep learning. In this process, an improved learning model considering the unique characteristics ofadvertisement is implemented. The experimental result demonstrates that the proposed method can effectivelyidentify the user interest even though only one device is examined.
Abstract
In the Internet of Things (IoT) era, the types of devices used by one user are becoming more diverse and thenumber of devices is also increasing. However, a forensic investigator is restricted to exploit or collect all theuser’s devices; there are legal issues (e.g., privacy, jurisdiction) and technical issues (e.g., computing resources,the increase in storage capacity). Therefore, in the digital forensics field, it has been a challenge to acquireinformation that remains on the devices that could not be collected, by analyzing the seized devices. In thisstudy, we focus on the fact that multiple devices share data through account synchronization of the onlineplatform. We propose a novel way of identifying the user's interest through analyzing the remnants of targetedadvertising which is provided based on the visited websites or search terms of logged-in users. We introduce adetailed methodology to pick out the targeted advertising from cache data and infer the user’s interest usingdeep learning. In this process, an improved learning model considering the unique characteristics ofadvertisement is implemented. The experimental result demonstrates that the proposed method can effectivelyidentify the user interest even though only one device is examined.
참고문헌 (31)
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