A hybrid network, which consists of the sections of communication lines with the transmission of signals of different physical nature on different transmission media, has been considered. Communication lines respond differently to threats, which allows to choose the line with the best performance for the transmission of information. The causal diagram of events that determine the state of the information transmission network, such as changes in emergency/accident-free time intervals, has been presented. The application scheme of the protection measures against dangerous events has been shown. To verify the measures, a matrix of their compliance with typical natural disasters has been developed and relevant examples have been given. It is suggested to evaluate the flexibility of the telecommunication network by its connectivity, characterized by the numbers of vertex and edge connectivity, the probability of connectivity. The presented scheme of the device for multi-channel information transmission in a hybrid network allows the choice for the transmission of information to the channel with the best performance. Using this device is the essence of the suggestion about increasing the flexibility of the network.

Sensitive data identification is a vital strategy in any distributed system. However, in the case of non-appropriate utilization of the system, sensitive data security can be at risk. Therefore, sensitive data identification and its security validation are mandatory. The paper primarily focuses on novel sensitive data recognition methodologies. Further, the sensitivity score of the attributes distinguishes non-sensitive attributes, and domain expert plays an important role in this process. The designing of the security assurance Algo and their corresponding decision tables make the system more robust and reliable. The result section is validated with the help of graphical representation, which clearly makes the authenticity of the research work. In summary, the authors may say that the sensitive data identification and security assurance of the proposed system is automated and work optimally in a cloud-based system.

In recent years, the most exploited sources of information such as Facebook, Instagram, LinkedIn and Twitter have been considered to be the main sources of misinformation. The presence of false information in these social networks has a very negative impact on the opinions and the way of thinking of Internet users. To solve this problem of misinformation, several techniques have been used and the most popular is the sentiment analysis. This technique, which consists in exploring opinions on corpora of texts, has become an essential topic in this field. In this article, we propose a new approach, called Conversational Sentiment Analysis Model (CSAM), allowing, from a text written on a subject through messages exchanged between different users, called a conversation, to find the passages describing feelings, emotions, opinions and attitudes. This approach is based on: (i) the conditional probability in order to analyse sentiments of different conversation items in Twitter microblog, which are characterized by small sizes, the presence of emoticons and emojis, (ii) the aggregation of conversation items using the uncertainty theory to evaluate the general sentiment of conversation. We conducted a series of experiments based on the standard Semeval2019 datasets, using three standard and different packages, namely a library for sentiment analysis TextBlob, a dictionary, a sentiment reasoner Flair and an integration-based framework for the Vader NLP task. We evaluated our model with two dataset SemEval 2019 and ScenarioSA, the analysis of the results, which we obtained at the end of this experimental study, confirms the feasibility of our model as well as its performance in terms of precision, recall and F-measurement.

Big data applications running on a big data cluster, creates a set of process on different nodes and exchange data via regular network protocols. The nodes of the cluster may receive some new type of attack or unpredictable internal attack from those applications submitted by client. As the applications are allowed to run on the cluster, it may acquire multiple node resources so that the whole cluster becomes slow or unavailable to other clients. Detection of these new types of attacks is not possible using traditional methods. The cumulative network traffic of the nodes must be analyzed to detect such attacks. This work presents an efficient testbed for internal attack generation, data set creation, and attack detection in the cluster. This work also finds the nodes under attack. A new insider attack named BUSY YARN Attack has been identified and analyzed in this work. The framework can be used to recognize similar insider attacks of type DOS where target node(s) in the cluster is unpredictable.

Authenticating the content of the digital image has profound influence in legal matters and in court rooms. Image forensics plays an important role towards it. Proposed approach helps to authenticate the original image by generating a content based image signature that is a unique fingerprint for the image. Our novel approach establishes spatio triad relationship among features and finds the centre of gravity or centroid of the same after indexing. Topology of the triad relationship for the content based low level feature descriptors is preserved through aggregation until single key feature is deduced which is a 128 bit signature hash value and represented in decimal form. Density of feature keypoints influences the centre of gravity which acts as a unique signature for the given image. Manipulated image cannot contribute to restore / regenerate the same signature. We have verified our authentication approach for standard benchmark image dataset like MICC-F220, Columbia Image Splicing Evaluation dataset and Image manipulation dataset from Friedrich Alexander University and have found satisfactory results for the same. Content based image signature obtained is used to verify authenticity of image and for retrieval of video from database. Content based image fingerprint generated can also be considered for embedding as a watermark.

In the domain of communication technologies, the Device-to-Device (D2D) communication becomes a predominant technology for the implementation of 5G communication system and Internet of Things (IoT) applications. In D2D communication Network, resource allocation and power management are the key areas of interest with ensuring the Quality of Services (QoS). Firstly, we propose the power control problem which is a non-convex problem. By using the log transformation approach, the non-convex problem converts into the convex optimization problem. Robust distributed power control method is further utilized for the power optimization at both ends (base station and D2D user) for underlay Inband D2D communication, where the cellular user and D2D user both use the cellular spectrum. After the power control, resource allocation is done to maximize the energy efficiency by 66.67% for the D2D system. Our proposed work provides new insight to power control techniques in D2D communication. Numerical analysis of the proposed algorithm reflects the impact of robust distributed power control for maintaining the quality of services and enhancing the energy efficiency of the system.

Due to the applications of wireless sensor networks (WSNs) in distinct fields, like civilian and military, environmental monitoring, and target tracking, etc., it became the important research area for researchers. Therefore, an efficient organization of network topology is needed for multi-hop data aggregation based on the energy constrained WSNs with large scale deployment for making the routing task simpler, balancing the load efficiently, and enhancing the network lifetime. The low-energy adaptive clustering hierarchy (LEACH) is an architecture of application-specific protocol, which is used for WSNs. However, this LEACH protocol suffered from more energy consumption in network without considering the cluster heads (CHs) distribution on a rotation basis. In addition, the complex security mechanisms are not sufficient due to limited bandwidth and other restrictions that the sensor nodes have. Thus, it is essential to improve the energy efficiency, CH stability and secure data transmission in WSN’s, which is the main objective of this work. Motivated from these three challenges, this article proposes a stable and secure LEACH (SS-LEACH), in which a new and improved protocol of LEACH is proposed for enhancing the stability of CH and energy efficiency as it considers the nodes’ consumed energy ratio (CER) for CH selection and random number generation, here after the proposed LEACH is named as CER-LEACH. In addition, it aims to prevent the previously elected CH node and it will get another chance in the current round. This method correlates the threshold used in traditional LEACH with the energy consumption ratio of each node. Further, it also introduces a hybrid reputation-based data transmission (HRDT) scheme for secure data transmission. With the proposed CER-LEACH protocol, WSNs achieves better performance in terms of secured communication, network lifetime, and energy consumption based on the analysis of simulation results as compared to state-of-art approaches.