Biblio

Advanced Persistent Threat (APT) attacks became a major network threat in recent years. Among APT attack techniques, sending a phishing email with malicious documents attached is considered one of the most effective ones. Although many users have the impression that documents are harmless, a malicious document may in fact contain shellcode to attack victims. To cope with the problem, we design and implement a malicious document detector called Forensor to differentiate malicious documents. Forensor integrates several open-source tools and methods. It first introspects file format to retrieve objects inside the documents, and then automatically decrypts simple encryption methods, e.g., XOR, rot and shift, commonly used in malware to discover potential shellcode. The emulator is used to verify the presence of shellcode. If shellcode is discovered, the file is considered malicious. The experiment used 9,000 benign files and more than 10,000 malware samples from a well-known sample sharing website. The result shows no false negative and only 2 false positives.

Through-wall sensing of hidden objects is a topic that is receiving a wide interest in several application contexts, especially in the field of security. The success of the object retrieval relies on accurate scattering models as well as on reliable inversion algorithms. In this paper, a contribution to the modeling of direct scattering for Through-Wall Imaging applications is given. The approach deals with hidden scatterers that are circular cross-section metallic cylinders placed below a dielectric layer, and it is based on an analytical-numerical technique implementing Cylindrical Wave Approach. As the burial medium of the scatterers may be a dielectric of arbitrary permittivity, general problems of scattering by hidden objects may be considered.When the burial medium is filled with air, the technique can simulate objects concealed in a building interior. Otherwise, simulation of geophysical problems of targets buried in a layered soil can be performed. Numerical results of practical cases are reported in the paper, showing the potentialities of the technique for its use in inversion algorithms.
 

This paper presents a human model-based feature extraction method for a video surveillance retrieval system. The proposed method extracts, from a normalized scene, object features such as height, speed, and representative color using a simple human model based on multiple-ellipse. Experimental results show that the proposed system can effectively track moving routes of people such as a missing child, an absconder, and a suspect after events.