Behavior-based detection methods have the ability to detect unknown malicious software (malware). The success of behavior-based detection methods must depend on sufficient number of abnormal behavior models. Insufficient number of abnormal behavior models can lead to high false positive and/or false negative rates. The majority of abnormal behavior models can only be derived by observing application behavior at lower level. However the traditional approaches are not very efficient in this type of analysis. In this paper, we present Holography,a virtual hardware-level tool to capture actions of malware programs. Holography does not rely on any driver that is installed on an operating system to log the execution profile of malware programs. Instead, Holography relies on only hardware level information to capture actions of malware programs. As a result, Holography is invisible to malware programs and therefore cannot be disabled or bypassed by malware programs.

The problem of placing wireless transmitters to meet particular objectives, such as coverage and cost, has proven to be NP-hard. Furthermore, the heterogeneity of wireless networks makes the problem more intractable to deal with. This paper presents a novel multiobjective variable-length genetic algorithm to solve this problem. One does not need to determine the number of transmitters beforehand; the proposed algorithm simultaneously searches for the optimal number, types, and positions of heterogeneous transmitters by considering coverage, cost, capacity, and overlap. The proposed algorithm can achieve the optimal number of transmitters with coverage exceeding 98% on average for six benchmarks. These preferable experimental results demonstrate the high capability of the proposed algorithm for the wireless heterogeneous transmitter placement problem.

The multiplex PCR experiment is to amplify multiple regions of a DNA sequence at the same time by using different primer pairs. Designing feasible primer pairs for multiplex PCR is a tedious task since there are too many constraints to be satisfied. In this paper, a new method for multiplex PCR primer design strategy using genetic algorithm is proposed. The proposed algorithm is able to find a set of suitable primer pairs more efficient and uses a MAP model to speed up the examination of the specificity constraint that is important for gene family sequences. The dry-dock experiment shows that the proposed algorithm finds several sets of primer pairs of gene family sequences for multiplex PCR that not only obey the design properties, but also have specificity.

MOTIVATION: Before performing a polymerase chain reaction experiment, a pair of primers to clip the target DNA subsequence is required. However, this is a tedious task as too many constraints need to be satisfied. Various kinds of approaches for designing a primer have been proposed in the last few decades, but most of them do not have restriction sites on the designed primers and do not satisfy the specificity constraint. RESULTS: The proposed algorithm imitates nature's process of evolution and genetic operations on chromosomes in order to achieve optimal solutions, and is a best fit for DNA behavior. Experimental results indicate that the proposed algorithm can find a pair of primers that not only obeys the design properties but also has a specific restriction site and specificity. Gel electrophoresis verifies that the proposed method really can clip out the target sequence. AVAILABILITY: A public version of the software is available on request from the authors.