In a Mobile Ad hoc Network (MANET), mobile nodes play multiple roles as hosts and routers and are dynamically changing multi-hop structures. MANET consists only of wireless nodes with limit processing power, and routing and data transfer are performed through cooperation with each other. It is exposed to many attack threats due to the dynamic topology by the movement of the nodes and the characteristic of multi-hop communication. Therefore, in MANET, a technique that can detect effectively must be applied while detecting malicious nodes and reducing the impact of various attacks. In this paper, we propose an trust based authentication technique for nodes and a distributed monitoring technique to improve the detection performance of malicious nodes. The hierarchical cluster structure was used to improve authentication of nodes and detection performance and management efficiency of malicious nodes. A lightweight authentication technique of member nodes in the cluster was applied and the efficiency of node authentication was improved. It was used to determine whether it was an attack node by transmitting traffic monitoring information for neighbor nodes to CA and using. In addition, the efficient authentication technique using only key exchange without anyone's help was applied in order to provide integrity when exchanging information between cluster heads. Through this, it was possible to be free from trust information about nodes and forgery and falsification of information about attack nodes. The superiority of the technique proposed in this paper was confirmed through comparative experiments with the existing intrusion detection technique.

Static analysis and detection of malware is a crucial phase for handling security threats. Most researchers stated that the problem with the static analysis is an imbalance in the dataset, causing invalid result metrics. It requires more time for extracting features from the raw binaries, and methods like neural networks require more time for the training. Considering these problems, we proposed a model capable of building a feature set from the dataset and classifying static PE files efficiently.  The research work was conducted to emphasize the importance of feature extraction rather than focusing on model building. The well-extracted features help to provide better results when fed to neural networks with minimal numbers of layers. Using minimum layers will enhance the performance of the model and take fewer resources and time for the processing and evaluation. In this research work, EMBER datasets published by Endgame Inc. containing PE file information are used. Feature extraction, data standardization, and data cleaning techniques are performed to handle the imbalance and impurities from the dataset. Later the extracted features were scaled into a standard form to avoid the problems related to range variations. A total of 2381 features are extracted and pre-processed from both the 2017 and 2018 datasets, respectively. 

The pre-processed data is then given to a deep learning model for training. The deep learning model created using dense and dropout layers to minimize the resource strain on the model and deliver more accurate results in less amount of time. The results obtained during experimentation for EMBER v2017 and v2018 datasets are 97.53% and 94.09%, respectively. The model is trained for ten epochs with a learning rate of 0.01, and it took 4 minutes/epoch, which is one minute lesser than the Decision Tree model. In terms of precision metrics, our model achieved 98.85%, which is 1.85% more as compared to the existing models. 

The fifth generation (5G) technology standard, utilizing the Internet of Things, promises enhanced communication systems. However, the efficiency expected from such systems entails significant requirements, such as higher data rates and flexibility of the lowest 5G layer. Meeting these requirements in subsequent wireless communication systems is highly dependent on the use of waveforms capable of efficiently enabling multiple access. In other words, proper waveforms determine the effective handling of diverse traffic within a given band. In this study, four candidate multicarrier waveforms, namely filtered orthogonal frequency division multiplexing, filter bank multicarrier, universal filtered multicarrier, and orthogonal frequency division multiplexing, which is currently used in 4G systems, are compared based on multiple parameters. MATLAB simulation results indicate that the waveforms significantly improved spectrum localization and provided appropriate spectrum fragmentation. As these waveforms can mix diverse traffic specifications, they handle the problem of time-frequency synchronization effectively. Therefore, these new waveforms exhibit significant potential in terms of orthogonality and synchronicity and can support numerous users without dropping signals. In addition, they support all applications and scenarios related to multiple-input and multiple-output. The obtained simulation results confirm the suitability of such waveforms for 5G applications and systems.

Wireless mesh network (WMN) with wireless backhaul technology provides last-mile Internet connectivity to the end-users. In multi-radio multi-channel WMN (MRMC-WMN), routers provide multiple concurrent transmissions among end-users. The existence of interference among concurrent transmissions severely degrades the network performance. A well-organized channel assignment (CA) scheme significantly alleviates the interference effect. But in trying to minimize interference, the CA scheme may affect the network connectivity. So, the CA scheme has to consider both these two conflicting issues. In this paper, as part of the initial configuration of WMNs, we propose a game theory-based load-unaware CA scheme to minimize the co-channel interference and to maximize the network connectivity. To adapt to the varying network traffic, we propose a dynamic channel assignment scheme. This scheme measures the traffic-load condition of the working channels of each node. Whenever a node finds an overloaded channel, it initiates a channel switch. Channel switching based on the fixed threshold may result in a channel over/underutilization. For optimal channel utilization, we propose a fuzzy logic-based approach to compute the channel switch threshold. The contending nodes and their densities and loads dominantly affect the network capacity and hence the performance. In the context of network capacity enhancement, we have addressed these factors and focused on increasing the network capacity. The simulation results indicate that our proposed load-unaware and load-aware CA schemes outperform the other related load-unaware and load-aware CA approaches.

Ad-hoc networks in which nodes are mobile as well as communicate via wireless links fall under the category of mobile ad-hoc networks (MANETs). Evasive mobility and the limited battery life of MANET nodes make routing a difficult problem. Most of the conventional routing protocols recommend the shortest path without considering route stability into account. A Cross-Layer Design and Fuzzy Logic based Stability Oriented (CLDFL-SO) routing protocol is proposed in this research, which offers a  solution for stable route formation by eliminating unstable links and low-quality nodes. Cross-layer interaction parameter based link residual lifetime calculation is used to assess the link's stability. The fuzzy logic is being used to evaluate the node quality by providing node metrics like node speed, residual energy and node degree. The simulations illustrate the efficacy of the suggested protocol in comparison to the popular Ad-hoc On-Demand Distance Vector (AODV) protocol. 

With the integration of cloud computing approaches in the healthcare systems, medical images are now processed and stored remotely on third-party servers. For such digital medical image data, privacy, protection, and security must be maintained by using image encryption methods. The aim of this paper is to design and apply a robust medical encryption framework to enhance the protection of medical image transformation and the patient information confidentiality. The proposed Framework encrypt the digital medical images using DNA computation and hyperchaotic RKF-45 random sequence approach. For which, the DNA computation is enhanced by applying hyperchaoticRKF-45 random key to the different Framework phases. The simulation results on different medical images were measured with various security analyses to prove the proposed framework randomness and coherent. Simulation results showed the ability of the hyperchaotic DNA encryption framework to withstand multiple electronic attacks with high performance compared to its counterparts of encryption algorithms. Finally, simulation and comparative studies have shown that, the proposed cryptography framework reported UACI and NPCR values 33.327 and 99.603 respectively, which are near to the theoretical optimal value.

Maintenance accounts for approximately 20% of the operational cost of aircraft; a margin higher than cost associated with fuel, crew, navigation, and landing fees. A significant percentage of maintenance cost is attributed to failures of aircraft components and systems. These failures are random and provide a database which can further be analyzed to aid decision-making for maintenance optimization. In this paper, stochastic mathematical models which can potentially be used to optimize maintenance task intervals of aircraft systems are developed. The initial data for this research are diagnostic variables and reliability parameters which formed the basis for selecting the probability density function for time between failures according to the exponential and Erlang models. Based on the probability density functions, the efficiency of the maintenance processes was calculated using average operational cost per unit time. The results of the analysis were further tested using the Monte Carlo simulation method and the findings are highlighted in this paper. The simulation results compared favorably with analytical results obtained using already existing Monte Carlo techniques to about 82% accuracy. The proposed mathematical optimization models determine the optimal aircraft maintenance task interval which is cost effective while considering safety and reliability requirements; our results can also be applied during the development, design, and operation phases of aircraft systems.