Esmaeil Kheirkhah ¹, Sayyed Mehdi Poustchi Amin ², Hediyeh AmirJahanshahi Sistani ², Haridas Acharya ³

Affiliations
1 Department of Computer Engineering, Mashhad Branch, Islamic Azad University, IR
2 Department of Computer Studies and Research, Symbiosis International University, Pune, IN
3 Allana Institute of Management Science, Pune University, Pune, IN

Abstract

We studied Brute-force SSH attacks carried out on six different universities campus networks by using Honeypot Techniques. Brute-force password guessing attacks against SSH, FTP and telnet servers are the most common form of attack to compromise servers facing the internet. A key factor to avoid disruption of these networks is to defend it against Brute-force attacks. We focused on the attempts to gain remote access to our SSH Honeypots Plus Tools and techniques employed. There are striking similarities in the methods used to attack these dissimilar systems. The evidence shows that, pre-compiled lists of usernames and passwords that are widely shared form the basis for brute-force attacks. When the passwords were analysed, it was found that in the event of actual malicious traffic what was commonly understood to be strong password did not protect the systems from being compromised. The data from the study were used to evaluate the efficacy of a variety of techniques designed to defend the systems against these attacks. Table 17 lists some commonly recommendation for the protection of SSH servers.

Keywords

Honeypot, Honeynet, Internet Attacks, Network Security, SSH, Brute-force, Malware

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Esmaeil Kheirkhah ¹, Zahra Sadri Tabatabaie ¹

Affiliations
1 Department of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad, IR

Abstract

Human face detection in colour images has been researched extensively over the past decades. Face detection has several applications in areas, such as security access control, visual surveillance, video conferencing, intelligent human-computer interfaces and content-based information retrieval. An ideal face detection system should detect faces from a given image/video regardless of their poses, illumination, scale, age, race, image quality, and image complexity with optimum speed and very low false-positive rate. In this paper a hybrid approach, based on the skin color information and Adaboost-based face detection, is proposed. The key points of the proposed framework are background elimination and down-sizing using the adaptive skin colour classification and segmentation, as well as sub-window size estimation. Therefore, the skin colour segments, as face candidates, were searched instead of the whole image. Meanwhile, the Viola-Jones Adaboost-based face detector was adopted in this research as the final face detector. In order to test the accuracy of the proposed algorithm, the proposed system was implemented and some experiments were also conducted on the standard image datasets such as Caltech (California Institute of Technology) standard image dataset. The proposed hybrid face detection system was compared with the Viola-Jones face detection system. The experiments showed that the proposed approach could efficiently improve the face detection system in both aspects, namely, accuracy (98.88%) and detection time (259.59 ms).

Keywords

Color-Based, Face Detection, Illumination, Skin Color Classification.

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