Abstract. Nowadays, malware is growing rapidly through the last few years and becomes more and more sophisticated as well as dangerous. A striking malware is obfuscation malware that is very difficult to detect. This kind of malware can create new variants that are similar to original malware feature but different about code. In order to deal with such types of malware, many approaches have been proposed, however, some of these approaches are ineffective due to their limited detection range, huge overheads or manual stages. Malware detection based on signature, for example, cannot overcome the obfuscation techniques of malware. Likewise, the behavior-based methods have the natural problems of a monitoring system such as recovery costs and long-lasting detection time. In this paper, we propose a new method (semantic set method) to detect metamorphic malware effectively by using semantic set (a set of changed values of registers or variables allocated in memory when a program is executed). For more details, this semantic set is analyzed by n-gram separator and Naïve Bayes classifier to increase detection accuracy and reduce detection time. This system has been already experimented on different datasets and got the accuracy up to 98% and detection rate almost 100%.

Malware is a program used to disrupt computer operation or to gather the sensitive information or to gain access to private computer system. Malware detection methods can only work well on some specific types of malware. For example, API/function based methods can detect malware quickly, but are unable to identify advanced transformable malwares or unknown malwares. To deal with these malwares, researchers proposed data mining methods which can recognize various types of malware. However, these method not only requires more overhead for training and detecting process but also is still ineffective to identify metamorphic malwares. A semantic set, a set of changed values of registers and variables allocated in memory when a program is executed, supports detecting most of malware variants even when they use complicated transformation techniques such as metamorphic malwares. Nevertheless, this approach requires that malware files must be disassembled. Based on analyzed results of these methods, we concluded that these methods can be combined together to create a powerful malware detection system because each method's advantages can cover the others' disadvantages. Namely, each of method is able to perform effectively in the specific range of malwares, so this combined system can detect all types of malware while separately each method could not. In this paper, we proposed an SSSM system (semantic set and string matching detection) which combined three methods: API/function signature based method, data mining method and semantic set method. SSSM system has been experimented on different datasets and achieved the accuracy up to 99.07% and detection rate nearly 100%.

Membrane distillation (MD), a process based on the thermal principle, is a combination of distillation and membrane separation in the same unit. There are many factors that can affect the MD performance, but the membrane characteristics are the most important in this process. The changes in the membrane properties affect the process efficiency, the permeate flux as well as the membrane lifetime. Some of the membrane properties mentioned in this paper include liquid entry pressure (LEP), contact angle, pore size, porosity, thickness, thermal conductivity, support layer, tortuosity, etc. This review paper aims to evaluate the membrane properties in order to reduce membrane wetting and to improve desalination efficiency. From this review, it can be seen that the LEP and contact angle are the important factors which directly affect the hydrophobicity of the membrane. When LEP and the contact angle increase, the hydrophobicity of the membrane increases. Thus, the membrane is durable and the MD system works efficiently. The remaining factors indirectly affect the operation of the MD system through LEP and contact angle (hydrophobicity).