The advancement of modern day computing has led to an increase of threats and intrusions. As a result, advanced security measurements and threat analysis models are necessary to detect these threats and identify protective measures needed to secure a system. Attack graphs and attack trees are the most popular form of attack modeling today. While both of these approaches represent the possible attack steps followed by an attacker, attack trees are architecturally more rigorous than attack graphs and provide more insights regarding attack scenarios. The goal of this research is to identify the possible direction to construct attack trees from attack graphs analyzing a large volume of data, alerts or logs generated through different intrusion detection systems or network configurations. This literature summarizes the different approaches through an extensive survey of the relevant papers and identifies the current challenges, requirements and limitations of an efficient attack modeling approach with attack graphs and attack trees. A discussion of the current state of the art is presented in the later part of the paper, followed by the future direction of research.

Data losses in wireless sensor network (WSN) commonly occur due to diverse transmission errors such as hardware or software limitations, channel congestion, network coverage constraint and transmission delay. Another important cause for data loss is distinct security attacks caused by illegal interferences of illicit third parties. Apart from that data loss may occur due to some unforeseen causes too. A number of efforts have been made in WSN to control such types of data loss during the transmission process individually or along with various combinations. However, none of them are capable of addressing each of the mentioned cause of data loss in WSN environment. Henceforth, we have proposed an error resistant technique for WSN to address all of the mentioned causes for data loss. The proposed technique also offers a backup system for the accidental data losses. The experimental results shows that the proposed technique offers minimum data loss during the communication process by offering higher Signal to Noise Ratio (SNR) and low Information Loss compared to the other existing error control techniques. The time efficiency can also be justified by its high Throughput and complexity can be verified by measuring Cyclomatic Complexity.

The Internet of Things (IoT) is advancing to prevail the application of the Internet, with the vision to connect everything around us. The deployment of IoT is advancing at a very fast pace, and relying on modified versions of the TCP/IP protocol suits. This rapid growth of the field is leaving a number of critical issues unresolved. Among the most critical issues are the quality of service and security of the delivered data. This research is set to tackle these issues through proposing a data delivery scheme that improves the quality of service (QoS) of classified data. The proposed solution relies on differentiating the priority of the delivered data, and to give preferences to secured and user-defined high priority traffic. The proposed solution denoted as Secured Traffic Priority Differentiation (STPD), is made to support any application, and is implemented at the Medium Access Control (MAC) sub layer. The proposed solution was tested in a virtual environment that simulates real scenarios using the Contiki operating system, using the Cooja simulator. The simulation results demonstrated a significant improvement of the proposed solution over the Carrier Sense Multiple Access Collision Avoidance, (CSMA/CA), by at 20%. The proposed solution worked to improve the channel utilization, data reliability, decreased latency of high priority traffic, and low priority traffic as well.

Classification is the technique of identifying and assigning individual quantities to a group or a set. In pattern recognition, K-Nearest Neighbors algorithm is a non-parametric method for classification and regression. The K-Nearest Neighbor (kNN) technique has been widely used in data mining and machine learning because it is simple yet very useful with distinguished performance. Classification is used to predict the labels of test data points after training sample data. Over the past few decades, researchers have proposed many classification methods, but still, KNN (K-Nearest Neighbor) is one of the most popular methods to classify the data set. The input consists of k closest examples in each space, the neighbors are picked up from a set of objects or objects having same properties or value, this can be considered as a training dataset. In this paper, we have used two normalization techniques to classify the IRIS Dataset and measure the accuracy of classification using Cross-Validation method using R-Programming. The two approaches considered in this paper are - Data with Z-Score Normalization and Data with Min-Max Normalization.

This paper presents a new SRNC relocation approach based on BOFC functions. The new approach handles all possible combinations of the user equipment movements, particularly, when it moves across overlapped regions with different GGSN branches. Additionally, it integrates both RNC and BS levels in order to reduce the number of packets loss during the hard handover process. The experimental results showed that the new approach reduces the packet-loss ratio in comparison to both SRNC and BOFC approaches. Besides, the experimental results showed that the average execution time of the handover procedure in each network component is closed to the average execution time of the BOFC approach.

A variety of technologies in recent years have been developed in designing on-chip networks with the multicore system. In this endeavor, network interfaces mainly differ in the way a network physically connects to a multicore system along with the data path. Semantic substances of communication for a multicore system are transmitted as data packets. Thus, whenever a communication is made from a network, it is first segmented into sub-packets and then into fixed-length bits for flow control digits. To measure required space, energy & latency overheads for the implementation of various interconnection topologies we will be using multi2sim simulator tool that will act as research bed to experiment various tradeoffs between performance and power, and between performance and area requires analysis for further possible optimizations.

This paper approaches security application for digital image and video processing. The techniques involve H.264 Video Compression, Elliptical Curve Cryptography Encryption followed by Image Interleaving and last by Pixel Integration to generate integrated multi-video. The user can choose any of the videos among the several integrated videos displayed with a unique security key for each video. With the secure key assigned for each video input, the original video is displayed by decrypting it from multiple videos.