Abstract:The Internet of Things (IoT) is the interconnection of things around us to make our daily process more efficient by providing more comfort and productivity. However, these connections also reveal a lot of sensitive data. Therefore, thinking about the methods of information security and coding are important as the security approaches that rely heavily on coding are not a strong match for these restricted devices. Consequently, this research aims to contribute to filling this gap, which adopts machine learning t… Show more
“…3 , OI is the input image, and EI is the encrypted image, EA: represents the encryption algorithm, and DA: represents the decryption algorithm. In the process of encryption, as discussed above, the RGB colours of the original image were randomly arranged [ 2 , 7 ]. Fig.…”
Section: Literature Review and Background Discussionmentioning
During medical picture transmission, the most pressing concern is security. Medical images must be encrypted since they are extremely sensitive. Watermarking, digital fingerprinting/signature, and encoding are some of the available image security techniques. Images and movies, for example, must be highly encrypted and decoded without losing any content information. Medical photos, for example, require extra protection, and protecting medical images is a critical issue when medical images and related patient information are transferred over public networks. This research work proposes a visual encryption strategy to secure medical pictures before being transmitted or stored in the cloud. This technique makes such pictures of unauthorized people unavailable and also maintains confidentiality, a prime safety requirement. The process made use of a pixel shuffling-based encryption technique and a secret key created from the image. In this research, we encrypted the medical image using modified Arnold Map Encryption and generated secret key values. Therefore, the image is encrypted, and henceforth it is decrypted as well. So this work gave us the encrypted image and decrypted image/original image as well. The modified Arnold Map Encryption tries to add more randomness, thus increasing the entropy of the image and thus makes it harder to decrypt. The modified Arnold Map Encryption is also compared to other algorithms such as Hyper Chaotic, Secure Hash Algorithm-13 (SHA-13), Ten Logistic Maps, Bakers Map, HenonMap, Cross Chaos Map, and 2D Logistic Map and shows better results in terms of encryption speed and Number of Pixel Change Rate (NPCR) value.
“…3 , OI is the input image, and EI is the encrypted image, EA: represents the encryption algorithm, and DA: represents the decryption algorithm. In the process of encryption, as discussed above, the RGB colours of the original image were randomly arranged [ 2 , 7 ]. Fig.…”
Section: Literature Review and Background Discussionmentioning
During medical picture transmission, the most pressing concern is security. Medical images must be encrypted since they are extremely sensitive. Watermarking, digital fingerprinting/signature, and encoding are some of the available image security techniques. Images and movies, for example, must be highly encrypted and decoded without losing any content information. Medical photos, for example, require extra protection, and protecting medical images is a critical issue when medical images and related patient information are transferred over public networks. This research work proposes a visual encryption strategy to secure medical pictures before being transmitted or stored in the cloud. This technique makes such pictures of unauthorized people unavailable and also maintains confidentiality, a prime safety requirement. The process made use of a pixel shuffling-based encryption technique and a secret key created from the image. In this research, we encrypted the medical image using modified Arnold Map Encryption and generated secret key values. Therefore, the image is encrypted, and henceforth it is decrypted as well. So this work gave us the encrypted image and decrypted image/original image as well. The modified Arnold Map Encryption tries to add more randomness, thus increasing the entropy of the image and thus makes it harder to decrypt. The modified Arnold Map Encryption is also compared to other algorithms such as Hyper Chaotic, Secure Hash Algorithm-13 (SHA-13), Ten Logistic Maps, Bakers Map, HenonMap, Cross Chaos Map, and 2D Logistic Map and shows better results in terms of encryption speed and Number of Pixel Change Rate (NPCR) value.
“…The need for network information security has increased recently due to the advancements and popularization of information and network technologies [1]- [12]. Human-based IDSs can either warn or intercept network intrusion, but this is not the case for the traditional network defense mechanisms.…”
Intrusion detection is mainly achieved by using optimization algorithms. The need for optimization algorithms for intrusion detection is necessitated by the increasing number of features in audit data, as well as the performance failure of the human-based smart intrusion detection system (IDS) in terms of their prolonged training time and classification accuracy. This article presents an improved intrusion detection technique for binary classification. The proposal is a combination of different optimizers, including Rao optimization algorithm, extreme learning machine (ELM), support vector machine (SVM), and logistic regression (LR) (for feature selection & weighting), as well as a hybrid Rao-SVM algorithm with supervised machine learning (ML) techniques for feature subset selection (FSS). The process of selecting the least number of features without sacrificing the FSS accuracy was considered a multi-objective optimization problem. The algorithm-specific, parameter-less concept of the proposed Rao-SVM was also explored in this study. The KDDCup 99 and CICIDS 2017 were used as the intrusion dataset for the experiments, where significant improvements were noted with the new Rao-SVM compared to the other algorithms. Rao-SVM presented better results than many existing works by reaching 100% accuracy for KDDCup 99 dataset and 97% for CICIDS dataset.
“…A lot of research papers [13]- [35] have used IoT efficiently in smart grid applications, where they used it to establish secure and efficient communication platform to serve different parts and processes in smart grid. So many other applications in different fields such as health care, weather monitoring, irrigation and farmer management, oil industry, smart home and other and others [36]- [62].…”
Many modern monitoring and controlling projects such as systems in factories, home, and other used the internet of things (IoT). These devices perform self-functions without requiring manual intervention in order to improve convenience and safety. Electrical networks are one of the most important areas in which IoT systems can control, monitor, detect, and alarm for faultier, because detecting faults, monitoring network data, and finding the best solutions in a smaller duration of time to improve the efficiency and reliability of electrical networks. This paper proposes a system on the basis of a wireless sensor network (WSN). This system monitors and controls a variety of electrical and environmental variables, including power consumption, weather temperature, humidity, flame, lighting, and detection cut in the cable in electrical poles. Each sensor is a node and is connected to a microcontroller board separately. The data collected by these sensors is display and monitored on a web page and saved in a local server's database, this site was created with a variety of web programming languages. The system was developed using a free global domain. The website having a database for storing real-time sensor information.
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