Distributed systems use General Packet Radio Service (GPRS) to exchange information between different members of the system. The members of the system depend critically upon their ability to access internet connection in order to exchange data via GPRS and the system will shut down in case of unavailability of Internet connection. There is a strong need for developing another backup communication media. In this paper a data transaction method based on encoded Short Message Service (SMS) over Global System for Mobile Communication (GSM) is proposed. This new method guarantees the functionality of the system in case of inaccessibility to GPRS which may be not always available due to measures such as attacks that affect its availability. The proposed method is based on third party agent who can keep the address secrecy of both communicators besides keeping confidentiality, integrity and availability.

Wireless Sensor Networks are emerging technologies that are diverse on energy consumption from different aspects. In the task mode, energy consumption of sensor nodes is categorized in, data packet transmitting, data processing and idle mode. Fundamentally, higher power is required and utilized at the time of data trans-receive as comparing to idle mode. However, power consumption of sensor in idle mode is necessarily important. To conserve energy, the network must provide quality of service sleep schedule, and use a mechanism to turn off the radio receiver periodically in coordinating method. Moreover, through dynamically round task management of sensors, significant energy saving can be achieved. Based on tasks and sleep schedules, nodes can form their clusters. It is necessary for real-time wireless applications to cogitate data transmit at the actual and response time based on the queries or tasks. This paper proposes Dynamic Immediate Data Report (DIDR) for real-time communication to schedule sleep mode of sensors in the network. Furthermore, to minimize the network energy consumption, Dynamic Schedule Data Report (DSDR) method is proposed. This method shows its efficiency by reducing the active time of nodes in the network. The performance analysis of the proposed works, illustrate effectively more energy efficiency as compared to existing methods.

In this work, we propose an image search method by visual content (CBIR), which is based on the color descriptor. The proposed method take account the spatial distribution of colors and make the signature partially invariant under rotation. The basic idea of our method is to use circular shift (clockwise or anti-clockwise direction) and mirror (horizontal direction and vertical direction respectively) matching scheme to measure the distance between signatures. Through some experiments, we show that this approach leads to a significant improvement in the quality of results.

Wireless Sensor Network (WSN) is a collection of sensory nodes forming a provisional network without the assistance of any recognized infrastructure. Due to the minimal range of each node’s transmissions, it may be essential for one sensory node to request for the aid of other sensory node in transferring a packet between the source and sink. The important issue while designing WSN is the routing protocol that makes the best use of the severely minimal resource, especially the energy consumption. We propose a Energy Conscious multipath Routing Algorithm designed to improve the latency, resiliency and efficiency through discovering multiple routes between the source and sink. A Sink Originated Route Discovery (SORD) process provides the locality information of the source to the sink. One dominant and multiple alternate routes are generated at the end of the route discovering process. Apart from the dominant route, all the other nodes are put to sleep mode in order to conserve energy and create a concussion free route. Data is transmitted between the source and sink through the dominant route and if there is a disturbance in the existing route, the next preferred alternate route is used. If the route doesn’t exist between the source and sink, the process starts from the beginning. Further, we analyze how the proposed protocol overcomes the drawback of the existing protocols. This system is implemented by using NS-2.34. By altering route update guidelines of existing on-demand routing schemes the Performance gains in the order of 10-15 % could be achieved. The simulation results show that the proposed protocol has less control packet overhead, less average energy consumption and the algorithm is faster.

Recently, considerable attention has been given to data mining techniques to improve the performance of intrusion detection systems (IDS). This has led to the application of various classification and clustering techniques for the purpose of intrusion detection. Most of them assume that behaviors, both normal and intrusions, are represented implicitly by connected classes. We state that such assumption isn't evident and is a source of the low detection rate and false alarm. This paper proposes a suitable method able to reach high detection rate and overcomes the disadvantages of conventional approaches which consider that behaviors must be closed to connected representation only. The main strategy of the proposed method is to segment sufficiently each behavior representation by connected subsets called natural classes which are used, with a suitable metric, as tools to build the expected classifier.
The results show that the proposed model has many qualities compared to conventional models; especially regarding those have used DARPA data set for testing the effectiveness of their methods. The proposed model provides decreased rates both for false negative rates and for false positives.

Cloud is the recent emerging technology in all aspects. The basic concern with the usage of this Cloud Technology is security. Security poses a major drawback with data storage, resource utilization, virtualization, etc. In the highly competitive environment the assurances are insufficient for the customers to identify the trust worthy cloud service providers. As a result all the entities in cloud and cloud computing environment should be trusted by each other and the entities that have communication should have valid trust on each other. Trust being the profound component in any network has attracted many researchers for research in various ways. The models developed so far are platform dependent and are not valid for heterogeneous platforms. An efficient model which can be ported on any platform is the current research trend in the research world. Our model is platform independent and also helps in calculating trust while migrating to another platform. The result shows that the proposed model is much more efficient in terms of computation time.

The ever changing network traffic reveals new attack types, which represent a security threat that poses a serious risk for enterprise resources. Therefore, the security administrators are in a real need to employ efficient Intrusion Detection and Prevention Systems, IDPS. Such systems might be capable to learn from the network behavior. In this paper, we present an incremental Learnable Model for Anomaly Detection and Prevention of Zero-day attacks, LMAD/PZ. To facilitate the ability of learning from observations that can provide a reliable model for automatic prevention, a comparison has been carried out between supervised and unsupervised learning techniques.
Thus, in LMAD/PZ, the intrusion detection step is integrated with an intrusion prevention plan. To ensure that the prevention plan is dependable and automatic, it must be backed and sustained with robust and accurate detection process. Therefore, two incremental data mining techniques are deeply investigated and implemented on NSL-KDD’99 intrusion dataset. The first technique is the Algorithm Quasi-optimal (AQ), which is a supervised Attributional Rules Learner, ARL, while the second is the Cobweb; an unsupervised hierarchical conceptual clustering algorithm. These algorithms categorize the network connections as either normal or anomalous. The performance of AQ is compared to Cobweb, and the best performance result is integrated with the prevention plan, to afford a fully automated system. The experimental results showed that, the model automatically adapts its knowledge base from continuous network streams, in addition to offering the advantage of detecting novel and zero day attacks. Many experiments have verified that AQ performance outperforms the Cobweb clustering, in terms of accuracy, detection rate and false alarm rate.

Linear-bounded automata (LBA) accept context-sensitive languages (CSLs) and CSLs are generated by context-sensitive grammars (CSGs). So, for every CSG/CSL there is a LBA. A CSG is converted into normal form like Kuroda normal form (KNF) and then corresponding LBA is designed. There is no algorithm or theorem for designing a linear-bounded automaton (LBA) for a context-sensitive grammar without converting the grammar into some kind of normal form like Kuroda normal form (KNF). I have proposed an algorithm for this purpose which does not require any modification or normalization of a CSG.