A modular reuse-based design methodology has been one of the most important factors in improving hardware design productivity. Traditionally, reuse involves manually searching through repositories for existing components. This search can be tedious and often unfruitful. In order to enhance design reuse, an automated discovery technique is proposed: a reference circuit is compared with an archive of existing designs such that similar circuits are suggested throughout the design phase. To achieve this goal, methods for assessing the similarity of two designs are necessary. Different techniques for comparing the similarity of circuits are explored utilizing concepts from different domains. A new similarity measure was developed using birthmarks that allows for fast and efficient comparison of large and complex designs. Applications where circuit similarity matching can be utilized are examined such as IP theft detection and reverse engineering. Productivity experiments show that automatically suggesting reusable designs to the user could potentially increase productivity by more than 34% on average. / Ph. D.

The rapid increase in mobile malware and deployment of over-privileged applications over the years has been of great concern to the security community. Encroaching on user’s privacy, mobile applications (apps) increasingly exploit various sensitive data on mobile devices. The information gathered by these applications is sufficient to uniquely and accurately profile users and can cause tremendous personal and financial damage. On Android specifically, the security and privacy holes in the operating system and framework code has created a whole new dynamic for malware and privacy exploitation. This research work seeks to develop novel analysis techniques that monitor Android applications for possible unwanted behaviors and then suggest various ways to deal with the privacy leaks associated with them. Current state-of-the-art static malware analysis techniques on Android-focused mainly on detecting known variants without factoring any kind of software obfuscation. The dynamic analysis systems, on the other hand, are heavily dependent on extending the Android OS and/or runtime virtual machine. These methodologies often tied the system to a single Android version and/or kernel making it very difficult to port to a new device. In privacy, accesses to the database system’s objects are not controlled by any security check beyond overly-broad read/write permissions. This flawed model exposes the database contents to abuse by privacy-agnostic apps and malware. This research addresses the problems above in three ways. First, we developed a novel static analysis technique that fingerprints known malware based on three-level similarity matching. It scores similarity as a function of normalized opcode sequences found in sensitive functional modules and application permission requests. Our system has an improved detection ratio over current research tools and top COTS anti-virus products while maintaining a high level of resiliency to both simple and complex obfuscation. Next, we augment the signature-related weaknesses of our static classifier with a hybrid analysis system which incorporates bytecode instrumentation and dynamic runtime monitoring to examine unknown malware samples. Using the concept of Aspect-oriented programming, this technique involves recompiling security checking code into an unknown binary for data flow analysis, resource abuse tracing, and analytics of other suspicious behaviors. Our system logs all the intercepted activities dynamically at runtime without the need for building custom kernels. Finally, we designed a user-level privacy policy enforcement system that gives users more control over their personal data saved in the SQLite database. Using bytecode weaving for query re-writing and enforcing access control, our system forces new policies at the schema, column, and entity levels of databases without rooting or voiding device warranty.

Product matching in e-commerce is an area that faces more and more challenges with growth in the e-commerce marketplace as well as variation in the quality of data available online for each product. Product matching for e-commerce provides competitive possibilities for vendors and flexibility for customers by identifying identical products from different sources. Traditional methods in product matching are often conducted through rule-based methods and methods tackling the issue through machine learning usually do so through unimodal systems. Moreover, existing methods would tackle the issue through product identifiers which are not always unified for each product. This thesis provides multimodal approaches through product name, description, and image to the problem area of product matching that outperforms unimodal approaches. Three multimodal approaches were taken, one unsupervised and two supervised. The unsupervised approach uses straight-forward embedding space to nearest neighbor search that provides better results than unimodal approaches. One of the supervised multimodal approaches uses Siamese network on the embedding space which outperforms the unsupervised multi- modal approach. Finally, the last supervised approach instead tackles the issue by exploiting distance differences in each modality through logistic regression and a decision system that provided the best results. / Produktmatchning inom e-handel är ett område som möter fler och fler utmaningar med hänsyn till den tillväxt som e-handelsmarknaden undergått och fortfarande undergår samt variation i kvaliteten på den data som finns tillgänglig online för varje produkt. Produktmatchning inom e-handel är ett område som ger konkurrenskraftiga möjligheter för leverantörer och flexibilitet för kunder genom att identifiera identiska produkter från olika källor. Traditionella metoder för produktmatchning genomfördes oftast genom regelbaserade metoder och metoder som utnyttjar maskininlärning gör det vanligtvis genom unimodala system. Dessutom utnyttjar mestadels av befintliga metoder produktidentifierare som inte alltid är enhetliga för varje produkt mellan olika källor. Denna studie ger istället förslag till multimodala tillvägagångssätt som istället använder sig av produktnamn, produktbeskrivning och produktbild för produktmatchnings-problem vilket ger bättre resultat än unimodala metoder. Tre multimodala tillvägagångssätt togs, en unsupervised och två supervised. Den unsupervised metoden använder embeddings vektorerna rakt av för att göra en nearest neighborsökning vilket gav bättre resultat än unimodala tillvägagångssätt. Ena supervised multimodal tillvägagångssätten använder siamesiska nätverk på embedding utrymmet vilket gav resultat som överträffade den unsupervised multimodala tillvägagångssättet. Slutligen tar den sista supervised metoden istället avståndsskillnader i varje modalitet genom logistisk regression och ett beslutssystem som gav bästa resultaten.

This work focuses on a possibility of using current computer vision alghoritms and methods for automatic similarity matching of so called graffiti tags. Those are such graffiti, that are used as a fast and simple signature of their authors. The process of development and implementation of CBIR system, which is created for this task, is described. For the purposes of finding images similarity, local features are used, most notably self-similarity features.