International Journal of Information and Network Security 

About: International Journal of Information and Network Security is an academic journal. The journal publishes majorly in the area(s): Encryption & Wireless network. It has an ISSN identifier of 2089-3299. It is also open access. Over the lifetime, 114 publications have been published receiving 621 citations.

A Comprehensive Study of Social Engineering Based Attacks in India to Develop a Conceptual Model

Abstract: The objective of this research is to present and demonstrate an analytical approach towards Social Engineering A questionnaire was created and a survey was conducted accordingly to determine the understanding of IT practitioners and social networking users based in India Based on the responses an advanced model of Social Engineering based attacks was developed This model can be used in development of Organization-wide Information Security policy and Information Security Awareness Program

Performance evaluation of data clustering techniques using KDD Cup-99 Intrusion detection data set

Abstract: Intrusion-detection systems aim at detecting attacks against computer systems and networks or, in general, against information systems A number of techniques are available for intrusion detection Data mining is the one of the efficient technique among them Intrusion detection and clustering have forever been hot topics in the area of machine learning Data clustering is a procedure of putting related data into groups Clustering procedure clusters the data into groups with the property of inter-group similarity and intra-group dissimilarity A clustering technique partitions a data-set into several groups such that the likeness within a group is larger than amongst groups Clustering as an intrusion detection technique has long before proved to be beneficial This paper evaluate four most representative off-line clustering techniques: k-means clustering, fuzzy c-means clustering, Mountain clustering, and Subtractive clustering These techniques are implemented and tested against KDD cup-99 data set, which is used as a standard benchmark data set for intrusion detection Performance and accuracy of the four techniques are presented and compared in this paper The experimental outcomes obtained by applying these algorithms on KDD cup-99 data set demonstrate that k-means and fuzzy c-means clustering algorithms perform well in terms of accuracy and computation time

Fast mapping method based on matrix approach for elliptic curve cryptography

Abstract: A new mapping method of the alphanumeric characters on to the x-y coordinate of the elliptic curve defined over a finite field Fp is provided. This paper attemps to utilize the properties of invertible matrices and elliptic curve to generate an algorithm that will guarantee the confidentiality of messages. The results points are encrypted by using elliptic curve encryption technique.The proposed method is efficient in its principale and has great potentiel to be applied to others situations where the exchange of messages is done confidentially. Finally, the use of matrices and their algebraic properties will provide better performance in this regard.

A survey on real world botnets and detection mechanisms

Abstract: Mitigating the destructive effect of botnets is a concern of security scholars. Though various mechanisms are proposed for botnets detection, real world botnets still survive and do their harmful operations. Botnets have developed new evasion techniques and covert communication channels. Knowing the characteristics of real world botnets helps security researchers in developing more robust detection methods. There are some surveys in the literature that study botnet detection methods; however they do not advert to real world botnets a lot. In this paper, we study various aspects of several real world botnets, i.e. Conficker, Kraken, Rustock, Storm, TDL4, Torpig, Waledac, Zeus and P2P Zeus. Architecture, protocol, type of infection, communication interval, attacks and evasion techniques of these botnets are probed in this paper. Moreover, studies on mitigation and detection of various aspects of botnets and new trends in botnet communication channels are reviewed.

Novel Shoulder-Surfing Resistant Authentication Schemes using Text-Graphical Passwords

Abstract: There are many applications which require the user to be authenticated before being permitted to perform certain tasks. Text password-based authentication is a popularly used authentication mechanism. Despite having greater security, text-passwords are characterized by selection of a weak and easy to remember passwords. Users also tend to write them down and share them with friends, family members and colleagues defeating the security provided by text-passwords. Graphical passwords offer an alternative to text passwords as the password space is typically higher, less prone to dictionary attacks and easier to remember visually. However, they suffer from shoulder-surfing attacks. In this paper, we propose two authentication schemes that support keyboard as well as graphical mouse-based input that map password characters to other regions of the password space. This shields the user’s password from being known to the adversary thus deflecting shoulder-surfing and spyware attacks. The schemes include both single and multi color input images consisting of printable characters. An analysis of security, usability, memorability and social engineering aspects of the proposed schemes is presented. Future research directions are also presented