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Data-driven public security networking and computer systems are always under threat from malicious codes known as malware; therefore, a large amount of research and development is taking place to find effective countermeasures. These countermeasures are mainly based on dynamic and statistical analysis. Because of the obfuscation techniques used by the malware authors, security researchers and the anti-virus industry are facing a colossal issue regarding the extraction of hidden payloads within packed executable extraction. Based on this understanding, we first propose a method to de-obfuscate and unpack the malware samples. Additional, cross-method-based big data analysis to dynamically and statistically extract features from malware has been proposed. The Application Programming Interface (API) call sequences that reflect the malware behavior of its code have been used to detect behavior such as network traffic, modifying a file, writing to stderr or stdout, modifying a registry value, creating a process. Furthermore, we include a similarity analysis and machine learning algorithms to profile and classify malware behaviors. The experimental results of the proposed method show that malware detection accuracy is very useful to discover potential threats and can help the decision-maker to deploy appropriate countermeasures.

Side channel attacks are attacks that are based on "Side Channel Information". Side channel information is information that can be retrieved from the encryption device that is neither the plaintext to be encrypted nor the cipher text resulting from the encryption process. Side-channel attacks are easy-to-implement whilst powerful attacks against cryptographic implementations and their targets range from primitives, protocols, modules, and devices to even systems. These attacks pose a serious threat to the security of cryptographic modules. In consequence, cryptographic implementations have to be evaluated for their resistivity against such attacks and the incorporation of different countermeasures has to be considered. In this paper, we explain about the correlation power analysis attack, which is the most dangerous type of side channel attack. Also, we implemented and experiment this attack using ATmega cryptographic module for configuration and the oscilloscope to obtain the experimental result, and MATLAB program for the verification process and design technology to analyze countermeasures.

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Abstract. The importance of this emerging information security and u-Korea or ubiquitous IT era, and the information security is more important. Especially, the small core device password encryption algorithm is an important part of the secure side channel attack cryptographic algorithms. However, it can provide high level of security, an adversary can attack small core device through implementation of cryptographic algorithms. In this paper, we explain about the correlation power analysis attack, which is the most dangerous type of side channel attack. Also, we implemented and experiment this attack. In our experiment, we used ATmega cryptographic module to configure and the oscilloscope to obtain the experimental result, and MATLAB program for the verification process.