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Malware is one of the most serious network security threats. To detect unknown variants of malware, many researches have proposed various methods of malware detection based on machine learning in recent years. However, modern malware is often protected by software packers, obfuscation, and other technologies, which bring challenges to malware analysis and detection. In this paper, we propose a system call based malware detection technology. By comparing malware and benign software in a sandbox environment, a sensitive system call context is extracted based on information gain, which reduces obfuscation caused by a normal system call. By using the deep belief network, we train a malware detection model with sensitive system call context to improve the detection accuracy.

Direct sequence spread spectrum (DSSS) signal uses phase coherent signals where the information symbols are multiplied with a code sequence, commonly known as chips. The signals are processed at the receiver using the code sequence as a matched filter to extract the information symbols. Taking advantage of the spread processing gain derived from the matched filter, communications can be carried out at low signal levels, which is the preferred method for high quality underwater acoustic (UWA) communication and remote UWA communication. However, in practical applications, DSSS UWA communication will face lots of interferences including multipath interference, Doppler compression interference and phase fluctuation interference, which will seriously affect the performance of DSSS. This paper first analyzes the interferences that DSSS faces in practical applications based on multiple actual received data collected in different water areas. By calculating the gain of the matched filter loss, one can find that influence of phase fluctuation interference has the most serious impact on DSSS. Secondly, the corresponding receiver processing algorithms are given for these interferences, including the differential energy detector and Doppler estimation method. The performance of differential energy detector under the above interferences is analyzed, which shows that the differential energy detector is insensitive to slow phase fluctuation interference and multipath interference and the differential energy detector combined with Doppler compensation can work well based on moving at-sea data. Finally, the parameter design of DSSS system in practical application is discussed and parameter design of DSSS UWA communication system should comprehensively consider the Doppler compression interference and phase fluctuation interference.