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Diagnostic data such as logs and memory dumps from production systems are often shared with development teams to do root cause analysis of system crashes. Invariably such diagnostic data contains sensitive information and sharing it can lead to data leaks. To handle this problem we present Knowledge and Learning-based Adaptable System for Sensitive InFormation Identification and Handling (KLASSIFI) which is an end to end system capable of identifying and redacting sensitive information present in diagnostic data. KLASSIFI is highly customizable, allowing it to be used for various different business use cases by simply changing the configuration. KLASSIFI ensures that the output file is useful by retaining the metadata which is used by various debugging tools. Various optimizations have been done to improve the performance of KLASSIFI. Empirical evaluation of KLASSIFI shows that it is able to process large files (128 GB) in 84 minutes and its performance scales linearly with varying factors. This points to practicability of KLASSIFI
With the increasing usage of open-source software (OSS) components, vulnerabilities embedded within them are propagated to a huge number of underlying applications. In practice, the timely application of security patches in downstream software is cha
Function entry detection is critical for security of binary code. Conventional methods heavily rely on patterns, inevitably missing true functions and introducing errors. Recently, call frames have been used in exception-handling for function start d
Humans possess a large amount of, and almost limitless, visual memory, that assists them to remember pictures far better than words. This phenomenon has recently motivated the computer security researchers in academia and industry to design and devel
Currently, Android malware detection is mostly performed on server side against the increasing number of malware. Powerful computing resource provides more exhaustive protection for app markets than maintaining detection by a single user. However, ap
The Controller Area Network (CAN) bus works as an important protocol in the real-time In-Vehicle Network (IVN) systems for its simple, suitable, and robust architecture. The risk of IVN devices has still been insecure and vulnerable due to the comple