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Kaur, R., Singh, A., Singh, S., Sharma, S..  2016.  Security of software defined networks: Taxonomic modeling, key components and open research area. 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). :2832–2839.

Software defined networking promises network operators to dramatically simplify network management. It provides flexibility and innovation through network programmability. With SDN, network management moves from codifying functionality in terms of low-level device configuration to building software that facilitates network management and debugging[1]. SDN provides new techniques to solve long-standing problems in networking like routing by separating the complexity of state distribution from network specification. Despite all the hype surrounding SDNs, exploiting its full potential is demanding. Security is still the major issue and a striking challenge that reduces the growth of SDNs. Moreover the introduction of various architectural components and up cycling of novel entities of SDN poses new security issues and threats. SDN is considered as major target for digital threats and cyber-attacks[2] and have more devastating effects than simple networks. Initial SDN design doesn't considered security as its part; therefore, it must be raised on the agenda. This article discusses the security solutions proposed to secure SDNs. We categorize the security solutions in the article by presenting a thematic taxonomy based on SDN architectural layers/interfaces[3], security measures and goals, simulation framework. Moreover, the literature also points out the possible attacks[2] targeting different layers/interfaces of SDNs. For securing SDNs, the potential requirements and their key enablers are also identified and presented. Also, the articles sketch the design of secure and dependable SDNs. At last, we discuss open issues and challenges of SDN security that may be rated appropriate to be handled by professionals and researchers in the future.

Kaur, R., Singh, S..  2015.  Detecting anomalies in Online Social Networks using graph metrics. 2015 Annual IEEE India Conference (INDICON). :1–6.

Online Social Networks have emerged as an interesting area for analysis where each user having a personalized user profile interact and share information with each other. Apart from analyzing the structural characteristics, detection of abnormal and anomalous activities in social networks has become need of the hour. These anomalous activities represent the rare and mischievous activities that take place in the network. Graphical structure of social networks has encouraged the researchers to use various graph metrics to detect the anomalous activities. One such measure that seemed to be highly beneficial to detect the anomalies was brokerage value which helped to detect the anomalies with high accuracy. Also, further application of the measure to different datasets verified the fact that the anomalous behavior detected by the proposed measure was efficient as compared to the already proposed measures in Oddball Algorithm.

Kaur, R., Singh, M..  2014.  A Survey on Zero-Day Polymorphic Worm Detection Techniques. Communications Surveys Tutorials, IEEE. 16:1520-1549.

Zero-day polymorphic worms pose a serious threat to the Internet security. With their ability to rapidly propagate, these worms increasingly threaten the Internet hosts and services. Not only can they exploit unknown vulnerabilities but can also change their own representations on each new infection or can encrypt their payloads using a different key per infection. They have many variations in the signatures of the same worm thus, making their fingerprinting very difficult. Therefore, signature-based defenses and traditional security layers miss these stealthy and persistent threats. This paper provides a detailed survey to outline the research efforts in relation to detection of modern zero-day malware in form of zero-day polymorphic worms.