Although the vast majority of malware used to be x86 architecture-based, the rapid rise of Internet of Things (IoT) malware in recent years has been forcing malware analysts to deal with binaries written for a wide range of architectures with little tooling support.

Data flow analysis is an essential technique for understanding the complicated behavior of malicious scripts. For tracking the data flow in scripts, dynamic taint analysis has been widely adopted by existing studies. However, the existing taint analysis techniques have a problem that each script engine needs to be separately designed and implemented. Given the diversity of script languages that attackers can choose for their malicious scripts, it is unrealistic to prepare taint analysis tools for the various script languages and engines.

Stateful packrat parsing is an algorithm for parsing syntaxes that have context-sensitive features. It is a well-known knowledge among researchers that the running time of stateful packrat parsing is linear for real-world grammars, as demonstrated in existing studies. However, we have found the cases in real-world grammars and tools that lead its running time to become exponential.

A malware signature including behavioral artifacts, namely Indicator of Compromise (IOC) plays an important role in security operations, such as endpoint detection and incident response. While building IOC enables us to detect malware efficiently and perform the incident analysis in a timely manner, it has not been fully-automated yet. To address this issue, there are two lines of promising approaches: regular expression-based signature generation and machine learning. However, each approach has a limitation in accuracy or interpretability, respectively.

Malicious scripts have been crucial attack vectors in recent attacks such as malware spam (malspam) and fileless malware. Since malicious scripts are generally obfuscated, statically analyzing them is difficult due to reflections. Therefore, dynamic analysis, which is not affected by obfuscation, is used for malicious script analysis. However, despite its wide adoption, some problems remain unsolved. Current designs of script analysis tools do not fulfill the following three requirements important for malicious script analysis. (1) Universally applicable to various script languages, (2) capable of outputting analysis logs that can precisely recover the behavior of malicious scripts, and (3) applicable to proprietary script engines.

Code-reuse attacks by corrupting memory address pointers have been a major threat of software for many years. There have been numerous defenses proposed for countering this threat, but majority of them impose strict restrictions on software deployment such as requiring recompilation with a custom compiler, or causing integrity problems due to program modification. One notable exception is ASLR(address space layout randomization) which is a widespread defense free of such burdens, but is also known to be penetrated by a class of attacks that takes advantage of its coarse randomization granularity. Focusing on minimizing randomization granularity while also possessing these advantages of ASLR to the greatest extent, we propose a novel defensive approach called code shredding: a defensive scheme based on the idea of embedding the checksum value of a memory address as a part of itself. This simple yet effective approach hinders designation of specific address used in code-reuse attacks, by giving attackers an illusion of program code shredded into pieces at byte granularity and dispersed randomly over memory space. We show our design and implementation of a proof-of-concept prototype system for the Windows platform and the results from several experiments conducted to confirm its feasibility and performance overheads.