@article {541, title = {A User-Interaction Bug Analyzer Based on Image Processing}, journal = {CLEI Electronic Journal}, volume = {19}, year = {2016}, month = {08/2016}, abstract = {

Context: Mobile applications support a set of user-interaction features that are inde- pendent of the application logic. Rotating the device, scrolling, or zooming are examples of such features. Some bugs in mobile applications can be attributed to user-interaction features. Objective: This paper proposes and evaluates a bug analyzer based on user- interaction features that uses digital image processing to find bugs. Method: Our bug analyzer detects bugs by comparing the similarity between images taken before and after a user-interaction. SURF, an interest point detector and descriptor, is used to compare the images. To evaluate the bug analyzer, we conducted a case study with 15 randomly selected mobile applications. First, we identified user-interaction bugs by manually testing the applications. Images were captured before and after applying each user-interaction feature. Then, image pairs were processed with SURF to obtain interest points, from which a similarity percentage was computed, to finally decide whether there was a bug. Results: We performed a total of 49 user-interaction feature tests. When manually testing the applications, 17 bugs were found, whereas when using image processing, 15 bugs were detected. Conclusions: 8 out of 15 mobile applications tested had bugs associated to user-interaction features. Our bug analyzer based on image processing was able to detect 88\% (15 out of 17) of the user-interaction bugs found with manual testing.

}, keywords = {Bug analyzer, Image processing, Interest points, Testing, User-interaction features}, url = {http://www.scielo.edu.uy/pdf/cleiej/v19n2/v19n2a04.pdf}, author = {Abel M{\'e}ndez-Porras and Jorge Alfaro-Vel{\'a}sco and Marcelo Jenkins and Alexandra Martinez} } @conference {706, title = {Known/chosen key attacks against software instruction set randomization}, booktitle = {Computer Security Applications Conference, 2006. ACSAC{\textquoteright}06. 22nd Annual}, year = {2006}, month = {12/2006}, publisher = {IEEE}, organization = {IEEE}, address = {Miami, FL, Estados Unidos}, abstract = {

Instruction set randomization (ISR) has been proposed as a form of defense against binary code injection into an executing program. One proof-of-concept implementation is randomized instruction set emulator (RISE), based on the open-source Valgrind IA-32 to IA-32 binary translator. Although RISE is effective against attacks that are not RISE-aware, it is vulnerable to pure data and hybrid data-code attacks that target its data, as well to some classes of brute-force guessing. In order to enable the design of a production version, we describe implementation-specific and generic vulnerabilities that can be used to overcome RISE in its current form. We present and discuss attacks and solutions in three categories: known-key attacks that rely on the key being leaked and then used to pre-scramble the attacking code; chosen-key attacks that use implementation weaknesses to allow the attacker to define its own key, or otherwise affect key generation; and key-guessing ("brute-force") attacks, about which we explore the design of mini-malistic loaders which can be used to minimize the number of mask bytes required for a successful key-guessing attack. All the described attacks were tested in real-world scenarios

}, keywords = {Binary codes, Computer aided instruction, Emulation, genetics, Hardware, Open source software, Production, Protection, Security, Testing}, isbn = {0-7695-2716-7}, doi = {10.1109/ACSAC.2006.33}, url = {http://ieeexplore.ieee.org/document/4041180/}, author = {Weiss, Yoav and Barrantes, Elena Gabriela} }