A dynamic data encryption method based on addressing the data importance on the internet of things

The rapid growth of internet of things (IoT) in multiple areas brings research challenges closely linked to the nature of IoT technology. Therefore, there has been a need to secure the collected data from IoT sensors in an efficient and dynamic way taking into consideration the nature of collected data due to its importance. So, in this paper, a dynamic algorithm has been developed to distinguish the importance of data collected and apply the suitable security approach for each type of data collected. This was done by using hybrid system that combines block cipher and stream cipher systems. After data classification using machine learning classifiers the less important data are encrypted using stream cipher (SC) that use rivest cipher 4 algorithm, and more important data encrypted using block cipher (BC) that use advanced encryption standard algorithm. By applying a performance evaluation using simulation, the proposed method guarantees that it encrypts the data with less central processing unit (CPU) time with improvement in the security over the data by using the proposed hybrid system.

Multi-round encryption for COVID-19 data using the DNA key

The need for a reliable and fast encryption algorithm to encrypt medical data for patients is an extremely important topic to be considered especially during pandemic times such as the pandemic COVID-19. This pandemic forced governments and healthcare institutions to monitor COVID-19 patients. All the patient's data or records are also shared among healthcare researchers to be used to help them find vaccines or cures for this pandemic. Therefore, protecting such data (images, text) or records face an everincreasing number of risks. In this paper, a novel multi-round encryption algorithm based on deoxyribonucleic acid (DNA) is proposed. The significance of the proposed algorithm comes from using a different random key to perform simple and fast encryption operations on multiple rounds to achieve a high level of confusion and diffusion effects in encrypted data. Experiments were conducted using a set of datasets of various types such as Excel sheets, images, and database tables. The experiments were conducted to test the performance and security level of the proposed encryption algorithm against well-known algorithms such as data encryption standard (DES) and advanced encryption standard (AES). The experiments show an outstanding performance regarding the encryption time, key size, information entropy, and the avalanche effects.

LT Codes with Double Encoding Matrix Reorder Physical Layer Secure Transmission

In traditional wireless sensor networks, information transmission usually uses data encryption methods to prevent information from being stolen illegally. However, once the encryption methods are leaked, eavesdropping nodes can easily obtain information. LT codes are rateless codes; if it is attacked by random channel noise, the decoding process will change and the decoding overhead will also randomly change. When it is used for physical layer communication of wireless sensor networks, it ensures that the destination node recovers all the information without adding the key, while the eavesdropping node can only obtain part of the information to achieve wireless information security transmission. To reduce the intercept efficiency of eavesdropping nodes, a physical layer security (PLS) method of LT codes with double encoding matrix reorder (DEMR-LT codes) is proposed. This method performs two consecutive LT code concatenated encoding on the source symbol, and part of the encoding matrix is reordered according to the degree value of each column from large to small, which reduces the probability of eavesdropping nodes recovering the source information. Experimental results show that compared with other LT code PLS schemes, DEMR-LT codes only increase the decoding overhead by a small amount. However, it can effectively reduce the intercept efficiency of eavesdropping nodes and improve information transmission security.

Zika Virus Prediction Using AI-Driven Technology and Hybrid Optimization Algorithm in Healthcare

The Zika virus presents an extraordinary public health hazard after spreading from Brazil to the Americas. In the absence of credible forecasts of the outbreak's geographic scope and infection frequency, international public health agencies were unable to plan and allocate surveillance resources efficiently. An RNA test will be done on the subjects if they are found to be infected with Zika virus. By training the specified characteristics, the suggested Hybrid Optimization Algorithm such as multilayer perceptron with probabilistic optimization strategy gives forth a greater accuracy rate. The MATLAB program incorporates numerous machine learning algorithms and artificial intelligence methodologies. It reduces forecast time while retaining excellent accuracy. The projected classes are encrypted and sent to patients. The Advanced Encryption Standard (AES) and TRIPLE Data Encryption Standard (TEDS) are combined to make this possible (DES). The experimental outcomes improve the accuracy of patient results communication. Cryptosystem processing acquires minimal timing of 0.15 s with 91.25 percent accuracy.

Revisiting silk: a lens-free optical physical unclonable function

For modern security, devices, individuals, and communications require unprecedentedly unique identifiers and cryptographic keys. One emerging method for guaranteeing digital security is to take advantage of a physical unclonable function. Surprisingly, native silk, which has been commonly utilized in everyday life as textiles, can be applied as a unique tag material, thereby removing the necessary apparatus for optical physical unclonable functions, such as an objective lens or a coherent light source. Randomly distributed fibers in silk generate spatially chaotic diffractions, forming self-focused spots on the millimeter scale. The silk-based physical unclonable function has a self-focusing, low-cost, and eco-friendly feature without relying on pre-/post-process for security tag creation. Using these properties, we implement a lens-free, optical, and portable physical unclonable function with silk identification cards and study its characteristics and reliability in a systemic manner. We further demonstrate the feasibility of the physical unclonable functions in two modes: authentication and data encryption.

Digital Immunity Module: Preventing Unwanted Encryption using Source Coding

Unwanted data encryption, such as ransomware attacks, continues to be a significant cybersecurity threat. Ransomware is a preferred weapon of cybercriminals who target small to large organizations' computer systems and data centres. It is malicious software that infects a victim's computer system and encrypts all its valuable data files. The victim needs to pay a ransom, often in cryptocurrency, in return for a decryption key. Many solutions use methods, including the inspection of file signatures, runtime process behaviors, API calls, and network traffic, to detect ransomware code. However, unwanted data encryption is still a top threat. This paper presents the first immunity solution, called the digital immunity module (DIM). DIM focuses on protecting valuable business-related data files from unwanted encryption rather than detecting malicious codes or processes. We show that methods such as file entropy and fuzzy hashing can be effectively used to sense unwanted encryption on a protected file, triggering our novel source coding method to paralyze the malicious manipulation of data such as ransomware encryption. Specifically, maliciously encrypted data blocks consume exponentially larger space and longer writing time on the DIM-protected file system. As a result, DIM creates enough time for system/human intervention and forensics analysis. Unlike the existing solutions, DIM protects the data regardless of ransomware families and variants. Additionally, DIM can defend against simultaneously active multiple ransomware, including the most recent hard to detect and stop fileless ones. We tested our solution on 39 ransomware families, including the most recent ransomware attacks. DIM successfully defended our sample file dataset (1335 pdf, jpg, and tiff files) against those ransomware attacks with zero file loss.

Digital Immunity Module: Preventing Unwanted Encryption using Source Coding

Unwanted data encryption, such as ransomware attacks, continues to be a significant cybersecurity threat. Ransomware is a preferred weapon of cybercriminals who target small to large organizations' computer systems and data centres. It is malicious software that infects a victim's computer system and encrypts all its valuable data files. The victim needs to pay a ransom, often in cryptocurrency, in return for a decryption key. Many solutions use methods, including the inspection of file signatures, runtime process behaviors, API calls, and network traffic, to detect ransomware code. However, unwanted data encryption is still a top threat. This paper presents the first immunity solution, called the digital immunity module (DIM). DIM focuses on protecting valuable business-related data files from unwanted encryption rather than detecting malicious codes or processes. We show that methods such as file entropy and fuzzy hashing can be effectively used to sense unwanted encryption on a protected file, triggering our novel source coding method to paralyze the malicious manipulation of data such as ransomware encryption. Specifically, maliciously encrypted data blocks consume exponentially larger space and longer writing time on the DIM-protected file system. As a result, DIM creates enough time for system/human intervention and forensics analysis. Unlike the existing solutions, DIM protects the data regardless of ransomware families and variants. Additionally, DIM can defend against simultaneously active multiple ransomware, including the most recent hard to detect and stop fileless ones. We tested our solution on 39 ransomware families, including the most recent ransomware attacks. DIM successfully defended our sample file dataset (1335 pdf, jpg, and tiff files) against those ransomware attacks with zero file loss.

Automatic Control System of Vacuum Nano Coating Based on AVR Single Chip Microcomputer

Aiming at the problems of long response time and poor anti-interference ability of traditional vacuum nano-coating automatic control system, a design of vacuum nano-coating automatic control system based on AVR single-chip microcomputer is proposed. The use of ATmega128L microcontroller and Harvard architecture improves the parallel processing efficiency of the microcontroller. Select the transient voltage suppression diode to protect the power supply of the single-chip microcomputer, optimize the communication circuit, connect an external encryptor to realize the data encryption function, increase the filtering program and optimize the binary code processing function. Using PID (Packet Identifier) control algorithm, the design of nano-coating vacuum automatic control system based on AVR single-chip microcomputer is realized. Compared with the traditional system, when the simulation model and system parameters of the control system constructed in the environment change, the proposed system can be stabilized within 15 seconds, and can be stabilized for 13 seconds after the interference signal is added. The response time of the system is longer. Shorter, stronger anti-interference ability, more suitable for automatic control of vacuum nano-coating.

Unbalanced Big Data-Compatible Cloud Storage Method Based on Redundancy Elimination Technology

In order to meet the requirements of users in terms of speed, capacity, storage efficiency, and security, with the goal of improving data redundancy and reducing data storage space, an unbalanced big data compatible cloud storage method based on redundancy elimination technology is proposed. A new big data acquisition platform is designed based on Hadoop and NoSQL technologies. Through this platform, efficient unbalanced data acquisition is realized. The collected data are classified and processed by classifier. The classified unbalanced big data are compressed by Huffman algorithm, and the data security is improved by data encryption. Based on the data processing results, the big data redundancy processing is carried out by using the data deduplication algorithm. The cloud platform is designed to store redundant data in the cloud. The results show that the method in this paper has high data deduplication rate and data deduplication speed rate and low data storage space and effectively reduces the burden of data storage.