WSN in three dimensional space is common in different application areas such as space monitoring, cave monitoring under water eco system and so on. Intrusion is a common type of attack in such types of networks. In this paper, we analyze the intrusion detection probability which helps in deploying the sensors in efficient manner. Even though the sensors are throw away in nature, still the cost matters. And the intelligent deployment helps in reducing the redundancy in communication. Therefore this model can be beneficial in case of three dimensional WSN.Here we deal with heterogeneous WSN as such types of WSN are common in different applications. For the case of simplicity, in our analysis, we consider only two types of sensors named as Type 1 and Type 2. This model can be extended to any number of types. This paper is an extension of our previous work where intrusion detection on homogeneous networks was discussed. General TermsWireless sensor networks, security, internal and external intrusion detection. KeywordsIntrusion detection, node density, sensing range, Wireless Sensor Network (WSN). INTRODUCTIONA wireless sensor network (WSN) is a type of wireless network consist of small nodes with capabilities of sensing physical or environmental conditions, processing related data and send information wirelessly.WSN is a wireless network consisting of spatially distributed autonomous devices using sensors to cooperatively monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, motion or pollutants, at different locations. The development of wireless sensor networks was originally motivated by military applications such as battlefield surveillance [1]. However, wireless sensor networks are now used in many industrial and civilian application areas, including industrial process monitoring and control, machine health monitoring, environment and habitat monitoring, healthcare applications, home automation and traffic control.The sensor nodes in WSNs are usually static after deployment, and communicate mainly through broadcast instead of point-topoint communication. Sensors are deployed in a variety of domains and some application should be secure from all types of attacks. A lot of security protocols or mechanisms have been designed for sensor networks. For example, SPINS (Sensor Protocol for Information via Negotiation), a set of protocols, provides secure data confidentiality, two-party data authentication, and data freshness and authenticated broadcast for sensor network [2]. LEAP (Localized Encryption and Authentication Protocol), is designed to support in-network processing bases on the different security requirements for different types of messages exchange [3]. INSENS is an intrusion tolerant routing protocol for wireless sensor networks [4]. In general, security solutions in the network can be divided into two categories: prevention and detection. Prevention techniques, such as encryption, authentication, firewalls, in WSNs, physical links may always be invaded, th...

Big dataextremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interaction and the data mining used for dig deep into analyzing the patterns and relationships of data. Frequent item set mining is a data mining method that was developed for market basket analysis.In the project proposed to anefficient data processing using Lshfp growth algorithm and grouping similar objects as the clusters with group id. The traditional datamining is based on the fp growth algorithm focused on the load balancing, and distributed among the nodes of the clusters.The process is mainly based on mapreduce which highly supported by Hadoop.Hadoop is a efficient popular frame work which supports mapreduce and itemset mining .Map reduce is that which contains map phase and reduce phase.Map phase which results the pair of key values and reduce phase which results the reduced results. It aims to decrease network overhead and efficient processing.