Recent Advances in Computer Science and Communications - Volume 16, Issue 5, 2023

The Internet Of Things (IoT) is a network of heterogeneous nodes that exchange data and critical information amongst themselves with minimum human intervention. The utility of this technology is large, thus it is used today in most modern applications. With billions of nodes connected across multiple applications, the area for potential security attacks is ever increasing. In addition to this, the nature of the data being transmitted also becomes more sensitive to the latest applications and this calls for effective security provisions. Due to its unique nature, traditional security provisions are not as successfully applicable in IoT networks. This leaves these networks vulnerable to malicious intruders. In such a scenario, Artificial Intelligence (AI) comes out as a powerful solution. This article serves to provide an overview of previously proposed AI-based solutions that can be applied to IoT networks to secure them. An industrywide overview of security provisions is provided by categorizing IoT applications into three broad sectors, namely, healthcare, smart grid, and smart city. The survey strives to give a clear industry-oriented vision of the available AI solutions and address the requirement of an application-ready security survey in the field.

Healthcare data is most unorganized and decentralized in many countries, including India. EHR (Electronic Health Record) has increased its acceptability and importance as it assists in medical research and helps backtrack the origin of disease. EHR may be generated at multiple sources like hospitals, clinics, path labs, or pharmacies. This paper is mainly targeted towards understanding the feasibility and different processes that could be adopted for medical data security; for this purpose, we have performed a systematic literature review. This research explores existing EHR frameworks, their benefits and challenges, as well as a brief comparison and applicability of various blockchain procedures.As part of the review, we have discussed prior works, the background of blockchain in the healthcare domain, and bibliometric analysis on ample research papers from 2011 to 2021.

Aims: To solve the problem of difficult acquisition of airport runway destruction image data. Objectives: This paper introduces SinGAN, a single-sample generative adversarial network algorithm. Methods: To address the shortcomings of SinGAN in image realism and diversity generation, an improved algorithm based on the combination of Gaussian error linear unit GELU and efficient channel attention mechanism ECANet is proposed. Results: Experiments show that its generated image results are subjectively better than SinGAN and its lightweight algorithm ConSinGAN, and the model can obtain an effective balance in both quality and diversity of image generation. Conclusion: The algorithm effect is also verified using three objective evaluation metrics, and the results show that the method in this paper effectively improves the generation effect compared with SinGAN, in which the SIFID metric is reduced by 46.67%.

Background: A mobile communication network (MCN) consists of mobile and selfsupporting nodes in a network that communicates over wireless links in the network. These mobile nodes within communication range will communicate directly between them, while other nodes in the network need to support neighboring nodes through a routing protocol (EEZRP). Methods: The Routing Protocol (EEZRP) is used to reduce network topology routing. However, it leads to higher energy consumption. Since mobile nodes and self-supporting nodes have different communication ranges, there is no centralized system to manage the energy usage of mobile networks, and this high energy consumption in EEZR limits the successful data transfer rate of mobile networks over wireless links. Thus, this proposed work is a game-theoretic approach based on the zone-based routing protocol. Results: GTZRP for parallel conflict-driven broadcasts takes into account both energy consumption and a second channel for flow control and complex congestion that improve data transfer. Conclusion: Finally, simulation results show that the proposed GTZRP outperforms other routing protocols.

Objective: Privacy in VANETs commands short-lived pseudonyms since the nodes are very mobile. It results in big computational overheads during secure communication between vehicles, which could place the safety of people and vehicles at risk. To overcome such a limitation, we are using the validation approach of pseudonyms based on Bloom Filter, which provides less computational overheads than other authentication procedures. A complete end-toend system is developed in three phases: authorization, clustering, encryption, and decryption phase, to establish secure transmission of data in VANETs. Methods: The authorization phase uses Bloom’s Filter authentication based on the pseudonym scheme. Based on the distributed parameters related to vehicles, clusters of vehicles are created to save power and bandwidth in communications. These clusters are chained to the next cluster with the help of the cluster head to share the information. First, only authenticated vehicles that are regular travelers on the road segment are allowed to become part of the cluster. SMBF ensures that any vehicle that is not a frequent traveler on the given road segment is not taking part in the communication process. Clustering is used to ensure the speed of communication. Results: The proposed scheme results are compared with other state-of-art techniques in VANETs. The analysis indicates that the storage and computational processing requirements are reduced by 28%, resulting in decreased communication costs. Conclusion: The proposed authentication process stores the pseudonyms in the certificate authority database. Hence, more memory requirements are required to maintain this technique. Overall, the performance of SMBF needs to be tested under different types of attacks.

Background: The enhancement of real-world speech signals is still a challenging task to eliminate noises, namely reverberation, background, street, and babble noises. Recently learned methods like dictionary learning have become increasingly popular and showed promising results in speech enhancement. The K-means Singular Value Decomposition (KSVD) algorithm is best suited for dictionary learning among many sparse representation algorithms. Moreover, the orthogonal matching pursuit (OMP) based algorithm used for signal recovery is given. The orthogonal matching pursuit (OMP) based algorithm for signal recovery gives the best enhancement results. On the other hand, FPGAs and ASICs are widely used to accelerate speech enhancement applications. FPGAs are commonly used in healthcare and consumer applications, where speech enhancement plays a crucial role. Methods: This paper proposes a modified KSVD algorithm that can easily be implemented onto hardware platforms like FPGAs and ASICS. Instead of using the double-precision arithmetic for the singular value decomposition part of the KSVD algorithm, we proposed to use CORDIC (Coordinate Rotation Digital Computer) based QR decomposition and QR-based singular value decomposition in dictionary learning. Results: The proposed KSVD algorithm is optimal with the CORDIC algorithm that can reduce by 7-8 times the processing time. Conclusion: The finding indicates that the proposed work is best suited to FPGA or ASIC platforms.

Background: Network security is getting more serious and has attracted much attention in recent years. Anomaly detection is an important technology to identify bad network flows and protect the network, which has been a hot topic in the network security field. However, in an anomaly detection system, the unknown network flows are always identified as some known flows in the existing solutions, which results in poorer identification performance. Objective: Aiming at detecting unknown flows and improving the detection performance, based on the KDD’99 dataset from a simulated real network environment, we analyzed the dataset and the main factors which affect the accuracy, and proposed a more robust and effective anomaly detection model (READM) to improve the accuracy of the detection. Methods: Based on unknown flows determination, the extra unknown type class is trained by neural network and identified by deep inspection method. Then, the identification result for unknown class will be updated to the detection system. Finally, the newly proposed robust and effective anomaly detection model (READM) is constructed and validated. Results: Through experiments comparison and analysis, the results indicate that READM achieves higher detection accuracy and less prediction time, which proves more efficient and shows better performance. Conclusion: Our study found that the existence of unknown flows always results in error detection and becomes the main factor influencing the detection performance. So, we propose a robust and effective anomaly detection model based on the construction and training of the extra unknown traffic class. Through the comparison of three experiments with different ways of thinking, it is proved that READM improves detection accuracy and reduces prediction time. Besides, after comparing with other solutions, it also shows better performance and has great application value in this field.

Background: Disease diagnosis is a useful phenomenon in healthcare. Machine learning classification methods would considerably improve the healthcare industry by providing a quick diagnosis of the disease. Thus, time could be saved for doctors. Nearly 17.9 million people expire due to heart disease every year. Objectives: World Health Organization (WHO) predicted that rate of death might increase by 24.5 million in 2030. Since heart illness was the major cause of death in comparison with other diseases today, it was the most challenging disease to diagnose. Methods: One of the reasons for death due to heart disease was due to the fact that risks were not identified in the earlier stage. Earlier diagnosis of disease was very much important. Machine Learning algorithms were used for predicting the prognosis of disease. Results: Here K-NN algorithm was used to predict the presence of heart disease in an individual. Thus, patients were classified as either positive or negative for heart disease and this model enhanced medical care and reduced the cost. This gave us significant knowledge that helps us to predict the patients with heart disease. Conclusion: The Python sci-kit library was used to implement this in Anaconda Navigator's Spyder Integrated Development Environment. Experiments revealed that technique worked well and was more accurate than before.

Background: The progress of the Cognitive Radio-Wireless Sensor Network is being influenced by advancements in wireless sensor networks (WSNs), which significantly have unique features of cognitive radio technology (CR-WSN). Enhancing the network lifespan of any network requires better utilization of the available spectrum as well as the selection of a good routing mechanism for transmitting informational data to the base station from the sensor node without data conflict. Aims: Cognitive radio methods play a significant part in achieving this, and when paired with WSNs, the above-mentioned objectives can be met to a large extent. Methods: A unique energy-saving Distance- Based Multi-hop Clustering and Routing (DBMCR) methodology in association with spectrum allocation is proposed as a heterogeneous CR-WSN model. The supplied heterogeneous CR-wireless sensor networks are separated into areas and assigned a different spectrum depending on the distance. Information is sent over a multi-hop connection after dynamic clustering using distance computation. Results: The findings show that the suggested method achieves higher stability and ensures the energy-optimizing CR-WSN. The enhanced scalability can be seen in the First Node Death (FND). Additionally, the improved throughput helps to preserve the residual energy of the network which helps to address the issue of load balancing across nodes. Conclusion: Thus, the result acquired from the above findings shows that the proposed heterogeneous model achieves the enhanced network lifetime and ensures the energy optimizing CR-WSN.