Different organizations in today’s scenario are fully dependent on information technology for their survival, suffer from various security challenges like unauthorized access, physical damages etc. To avoid various security breaches and concerns, robust mechanism for user access need to be adopted that not only secure of valuable data but can also be utilized for developing various other security applications. “Biometric” secured technology is gaining attention for over traditional security mechanism like password, smart card etc. because information related to biometric are difficult to steal as compared to other mechanisms. In this research analysis “strong biometric approach” is proposed to overcome security apprehensions of various organizations& society through iris recognition system. Iris recognition system is a mechanism to identify a person through analyzing his or her iris pattern. This recognition system includes iris image acquisition, segmentation, normalization, encoding, matching and finally validation of iris templates. The iris recognition system developed and simulated in this research study has taken IIT database iris images as inputs and utilized hamming distance as the matching parameter. The simulated results depict an efficient and novel secured approach that will overcome various unauthorized accesses across the internet. The most novel approach of this iris based recognition system as compared to other traditional systems is that, if selected images are matched with trained iris images present in database then the resultant hamming distance as most of the iris recognition systems directly accept or reject images and causes huge congestion and execution prorogations.

Distributed Denial of Service (DDOS) attacks have become one of the main threats in cloud environment. A DDOS attack can make large scale of damages to resources and access of the resources to genuine cloud users. Old-established defending system cannot be easily applied in cloud computing due to their relatively low competence and wide storage. In this paper we offered a data mining and neural network technique, trained to detect and filter DDOS attacks. For the simulation experiments we used KDD Cup dataset and our lab datasets. Our proposed model requires small storage and ability of fast detection. The obtained results indicate that our model has the ability to detect and filter most type of TCP attacks. Detection accuracy was the metric used to evaluate the performance of our proposed model. From the simulation results, it is visible that our algorithms achieve high detection accuracy (97%) with fewer false alarms.