We present a construction for searchable symmetric encryption (SSE). We consider a wide range of attacks and hardness assumptions and fulfill the strongest security requirements.
The "standard" privacy requirement against searchable encryption is message indistinguishability under an adaptively chosen keyword attack (IND-CKA2). We consider to protect the data and the keyword(s) together, i.e. privacy of the data is not considered as a separate problem (as the latter is typical in research papers). Beside the CKA model, we consider also the adaptively chosen trapdoor attack (CTA). Against active attacks (such as swapping attack) we add integrity protection for the (data, keyword) pair. By guaranteeing existential unforgeability (EU) for trapdoor keys we give protection against Keyword Guessing Attack (KGA). Attacks via searching for patterns in the database is prevented by randomized keyword encryption and trapdoor generation. Our construction is secure in the standard model of computation assuming bilinear groups with the widely used Symmetric eXternal Diffie Hellmann (SXDH) assumption.

Peer to peer networks have become one of the most popular networking methods because of their flexibility and many use cases such as file sharing and distributed computations. Unstructured overlay peer to peer networks are one of key components of peer to peer systems that are considerable because of their low cost in network construction and maintenance. One of the main challenges in unstructured peer to peer overlay networks is the topology mismatch between overlay network and the underlying physical infrastructure. The root of this challenge is lack of awareness about peers in the network infrastructure during connection to and disconnection from overlay network, in addition to the neighbor selection mechanism in the overlay network. Different types of awareness of network infrastructure includes awareness of the location of internet service providers. Also awareness of proximity, geographical location and resources of peers. In this article we present a middleware which configures overlay network by using public measurements and the estimated delay among peers in order to have the most conformity with the topology of physical infrastructure. To evaluate the performance, our middleware is implemented on the top of Gnutella which is an unstructured overlay peer-to-peer network. Our simulations show that our middleware enhances the conformity of overlay network to the topology of physical network infrastructure. In addition, it improved the average throughput and the average delay.

Sensing element Networks area unit gaining a lot of attention as a result of applications like sensible cities(traffic congestion, sensible parking, sensible lighting), sensible setting (forest hearth detection, air pollution) security and emergencies (Radiation levels, Explosive and dangerous Gases, Military applications) to call a couple of. The important facet of those observation and chase applications area unit security and sensing element location. The Wireless sensing element Networks may be thought to be associate degree freelance theme for accomplishing data-intensive chores like atmosphere (habitat) perceptive, data congregation, earthquake perceptive, parcel intelligence operation, etc. and any communication to the appliance. Wormhole attack could be a severe threat to the safety of the network. Because it could be a passive attack, it's terribly difficult to notice Wormhole attack. The most stress of this analysis work is to mitigate the wormhole attack. During this paper, we have a tendency to address the wormhole attack by proposing a trust-based wormhole attack mitigation technique. Our projected system is easy with no further hardware demand and no tight clock synchronization.

Passive image forgery detection has attracted many researchers in the recent years. Image manipulation becomes easier than before because of the fast development of digital image editing software. Image splicing is one of the most widespread methods for tampering images. Research on detection of image splicing still carries great challenges. In this paper, an algorithm based on deep learning approach and wavelet transform is proposed to detect the spliced image. In the deep learning approach, Convolution Neural Network (CNN) is employed to automatically extract features from the spliced image. CNN is applied and then Haar Wavelet Transform (HWT) is used. Support Vector Machine (SVM) is used later for classification. Additional experiments are performed. That is, Discrete Cosine Transform (DCT) replaces HWT and then Principle Component Analysis (PCA) is applied. The proposed algorithm is evaluated on a publicly available image splicing datasets (CASIA v1.0 and CASIA v2.0). It achieves high accuracy while using a relatively low dimension feature vector. Our results demonstrate that the proposed algorithm is effective and accomplishes better performance for detecting the spliced image.

In this paper, we propose to optimize energy and overheads of a network by reducing the copies of messages in the network. The key idea behind the proposed scheme is to select the distance of encountered node from the destination to decide the relay nodes. This limits the number of relay nodes and thus reduces the communication energy and message overheads by producing lesser number of copies of the messages in the network. Further to maintain delivery of messages, the proposed protocol evaluates delivery probability of relay nodes. The measures of probability are inter-contact delay and variance in delay between the nodes. This probability is used to decide how many copies of a message is transferred to the encountered node. This further reduces the communication energy as well as message overheads. The simulation results show that our proposed strategy reduces message overheads and energy consumption as compared to the previous existing strategy while maintaining comparable delivery probability.

Extending the battery lifetime and reducing the power consumption using ultra low power sensor nodes and energy harvesting systems is essential to realize 50 billion IOT devices. Development of efficient routing algorithm is a critical aspect for reducing energy consumption and enhancing network lifetime. Clustering is a key technique used to enhance network lifetime. The lifetime of a wireless sensor network for Daylight Artificial Light Integrated Scheme is enhanced using Self Organizing Map (SOM) based clustering algorithm. A simulation of cluster based routing protocols like LEACH (Low Energy Adaptive Clustering Hierarchy), Fuzzy based LEACH and ANFIS based LEACH is also carried out using MATLAB software.