Decision Tree Classification of Spatial Data Streams Using Peano Trees of classification

Many organizations have large quantities of spatial data collected in various application areas, including remote sensing, geographical information systems (GIS), astronomy, computer cartography, environmental assessment and planning, etc.  These data collections are growing rapidly and can therefore be considered as spatial data streams.  For data stream classification, time is a major issue.  However, these spatial data sets are too large to be classified effectively in a reasonable amount of time using existing methods.  In this paper, we developed a new method for decision tree classification on spatial data streams using a data structure called Peano Count Tree (P-tree).  The Peano Count Tree is a spatial data organization that provides a lossless compressed representation of a spatial data set and facilitates efficient classification and other data mining techniques.  Using P-tree structure, fast calculation of measurements, such as information gain, can be achieved.  We compare P-tree based decision tree induction classification and a classical decision tree induction method with respect to the speed at which the classifier can be built (and rebuilt when substantial amounts of new data arrive).  Experimental results show that the P-tree method is significantly faster than existing classification methods, making it the preferred method for mining on spatial data streams

Ensuring the Data Integrity and Confidentiality in Cloud Storage Using Hash Function and TPA

Main call for Cloud computing is that users only utilize what they required and only pay for whatever they are using. Mobile Cloud Computing refers to an infrastructure where data processing and storage can happen away from mobile device. Research estimates that mobile subscribers worldwide will reach 15 billion by the end of 2014 and 18 billion by at the ending of 2016. Due to increasing use of mobile devices the requirement of cloud computing in mobile devices arise, which evolves Mobile Cloud Computing. Mobile devices require large storage capacity and maximum CPU speed. As we are storing data on cloud there is an issue of data security. As there is risk associated with data storage many IT professionals are not showing their interest towards Mobile Cloud Computing. To ensure the users' data correctness in the cloud, here we are proposing an effective mechanism with salient feature of data integrity and confidentiality. This paper proposed a solution which uses the RSA algorithm and mechanism of hash function along with various cryptography tools to provide better security to the data stored on the cloud. This model can not only solve the problem of storage of massive data, but also make sure that it will give data access control mechanisms and ensure sharing data files with confidentiality and integrity. DOI: 10.17762/ijritcc2321-8169.15055

Lightweight novel trust based framework for IoT enabled wireless network communications

For IoT enabled networks, the security and privacy is one of the important research challenge due to open nature of wireless communications, especially for the networks like Vehicular Ad hoc Networks (VANETs). The characteristics like heterogeneity, constrained resources, scalability requirements, uncontrolled environment etc. makes the problems of security and privacy even more challenging. Additionally, the high degree of availability needs of IoT networks may compromise the integrity and confidentially of communication data. The security threats mainly performed during the operations of data routing, hence designing the secure routing protocol main research challenge for IoT networks. In this paper, to design the lightweight security algorithm the use of Named Data Networking (NDN) which provides the benefits applicable for IoT applications like built-in data provenance assurance, stateful forwarding etc. Therefore the novel security framework NDN based Cross-layer Attack Resistant Protocol (NCARP) proposed in this paper. In NCARP, we designed the cross-layer security technique to identify the malicious attackers in network to overcome the problems like routing overhead of cryptography and trust based techniques. The parameters from the physical layer, Median Access Control (MAC) layer, and routing/network layer used to compute and averages the trust score of each highly mobility nodes while detecting the attackers and establishing the communication links. The simulation results of NCARP is measured and compared in terms of precision, recall, throughput, packets dropped, and overhead rate with state-of-art solutions