To improve the efficiency of security of the information secure mechanism, an algorithm BMD-128 is proposed. This algorithm is built on the SDDO. Using this operator decreases significanthy the cost of hardware implementation. Besides, it also ensures both the high applicability in the transaction needing the change of session keys with high frequency and the ability against slide attack. Concurrently, this algorithm also eliminates the weak keys without the complex round key proceduce. The algorithm is evaluated regards to the standard NESSIE and the ability against the differential cryptanalysis. Concurrently, it is also compared the performance with the other famous ciphers when implementing on hardware FPGA.

Context-awareness has long been an important building block in designing systems that vary their operating behavior based on an analysis of rapidly changing operating conditions. There is the need however to define context more formally so that context data-sharing can take place between systems and more complex interactions between connected systems can be developed. The area of computer security is examined in particular as an area where the representation and sharing of context data can lead to more effective policy enforcement. A framework is proposed for sharing data between assessment sensors and enforcement mechanisms in order to facilitate more accurate policy enforcement. A detailed performance analysis of the proposed system is offered along with conclusions on the feasibility of such systems.

Evaluating the security of software systems is a complex problem for the research communities due to the multifaceted and complex operational environment of the system involved. Many efforts towards the secure system development methodologies like secSDLC by Microsoft have been made but the measurement scale on which the security can be measured got least success. As with a shift in the nature of software development from standalone applications to distributed environment where there are a number of potential adversaries and threats present, security has been outlined and incorporated at the architectural level of the system and so is the need to evaluate and measure the level of security achieved . In this paper we present a framework for security evaluation at the design and architectural phase of the system development. We have outlined the security objectives based on the security requirements of the system and analyzed the behavior of various software architectures styles. As the component-based development (CBD) is an important and widely used model to develop new large scale software due to various benefits like increased reuse, reduce time to market and cost. Our emphasis is on CBD and we have proposed a framework for the security evaluation of Component based software design and derived the security metrics for the main three pillars of security, confidentiality, integrity and availability based on the component composition, dependency and inter component data/information flow. The proposed framework and derived metrics are flexible enough, in way that the system developer can modify the metrics according to the situation and are applicable both at the development phases and as well as after development.

By ever increase in using computer network and internet, using Intrusion Detection Systems (IDS) has been more important. Main problems of IDS are the number of generated alerts, alert failure as well as identifying the attack type of alerts. In this paper a system is proposed that uses Adaptive Neuro-Fuzzy Inference System to classify IDS alerts reducing false positive alerts and also identifying attack types of true positive ones. By the experimental results on DARPA KDD cup 98, the system can classify alerts, leading a reduction of false positive alerts considerably and identifying attack types of alerts in low slice of time.

Wireless links are characterized by high error rates and intermittent connectivity. TCP congestion control has been developed on the assumption that network congestion is the only cause for packet loss. Upon detecting a packet loss, TCP drops its transmit window resulting in an unnecessary reduction of end-to-end throughput which results in suboptimal performance.The sender has to be made aware by some feedback mechanism that some of the losses reported are not due to congestion. The Active Queue Management algorithms (AQM) are used to reduce congestion, and in this paper, we have analysed four AQM algorithms, Random Early Deduction (RED), Wireless Explicit Congestion Notification (WECN), Queue Management Backward Congestion Control Algorithm (QMBCCA) and its enhanced version Extended Queue Management Backward Congestion Control Algorithm (EQMBCCA). WECN, QMBCCA & EQMBCCA algorithms make use of feedback mechanisms. WECN gives feedback using the CE bit. QMBCCA and EQMBCCA make use of ISQ notifications and also the CE bit whenever the average queue size crosses minimum threshold value. EQMBCCA reduces the reverse ISQ traffic by introducing a configurable intermediate threshold value IntThres. The comparison is made in terms of Delay, HTTP packet loss percentage and fairness for FTP flows in a wireless environment. It is found that the performance of EQMBCCA is almost equal to that of QMBCCA and better than RED and WECN.

Black hole attack in Vehicular Ad Hoc Network is major problem related with the field of computer networking. In this paper we present the performance analysis of the black hole attack in Vehicular Ad Hoc Network. We elaborate the different types of attacks and their depth in ad hoc network. The performance metric is taken for the evaluation of attack which depends on a packet end to end delay, network throughput and network load. The delay, throughput and load are simulated by the help of OPNET 14.5 modeler. The simulation setup comprises of 30 Vehicular nodes moving with constant speed of 10 meter per second. The data rate of Vehicular nodes is 11 Mbps with default transmitting power of 0.005 watts. With On Demand Distance Vector Routing and Optimized Link State Routing the malicious node buffer size is lowered to a level which increase packet drops.

The internet today is being used by millions of users for a large variety of commercial and non commercial purposes. It is controlled by different entities. It is mainly used as an efficient means for communication, entertainment and education. With the rapid growth of internet, there is a need for protecting confidential data. The Internet was however originally designed for research and educational purpose, not for commercial applications.So internet was not designed with security in mind. As the internet grows the existing security framework was not adequate for modern day applications. Cryptography play a vital role in providing security.Lot of research is going on block cipher algorithms. In this paper we present a new 512 bit block cipher named SF Block cipher. The proposed cipher is developed based on design principle known as Substitution permutation network (SP Network). The algorithm is implemented in .NET Framework and MATLAB. Cryptanalysis is carried out in the encrypted file. It was found that the encrypted file with this algorithm is difficult to break.Simulation results shows that the proposed Block cipher has better performance over other algorithms such as AES and Blowfish

Taxonomy of audio signals containing secret information or not is a security issue addressed in the context of steganalysis. A cover audio object can be converted into a stego-audio object via different steganographic methods. In this work the authors present a statistical method based audio steganalysis technique to detect the presence of hidden messages in audio signals. The conceptual idea lies in the difference of the distribution of various statistical distance measures between the cover audio signals and their denoised versions i.e. stego-audio signals. The design of audio steganalyzer relies on the choice of these audio quality measures and the construction of two-class classifier based on KNN (k nearest neighbor), SVM (support vector machine) and two layer Back Propagation Feed Forward Neural Network (BPN). Experimental results show that the proposed technique can be used to detect the small presence of hidden messages in digital audio data. Experimental results demonstrate the effectiveness and accuracy of the proposed technique.