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.

Power analysis attack, which was introduced by Kocher et al. in 1999, was known as the most threatening physical attack against low power device such as smart-card. The essential reason that allows an attacker to implement a power analysis attack on a cryptosystem is leakage information, which is leaked during the operation of the cryptosystem's encryption/decryption process and related to internal secret information. The general and efficient power analysis attack method proposed in this paper is based on an internally divided operation unit. As such, the proposed power analysis attack is implemented to expose the weakness of the operation of a symmetric key encryption algorithm in a smart-card