Enhancement to the patient's health care image encryption system, using several layers of DNA computing and AES (MLAESDNA)

Keeping patient health data private has been a big issue for decades, and this issue will not go away anytime soon. As an integral part of many developing technologies, cryptographic Internet communications ICs (e.g. fog computing and cloud computing) are a main focus of IoT research. Just keep trying all the potential keys until you find the correct one. New and future technologies must have a model of DNA cryptography in order to assure the efficient flow of these technologies. Public-key cryptography is also required to make DNA sequence testing devices for the Internet of Things interoperable. This method employs DNA layers and AES in such a way that it may be easier to design a trustworthy hybrid encryption algorithm that uses DNA layers and AES. In order to guard against brute-force decryption attacks, DNA sequences are encrypted using three keys: (I) the main key, which is the key to the AES encryption algorithm; (II) the rule 1 key, which is the base DNA structure; and (III) the rule 2 key, which is the DNA helical structure binding probability. This key was created with increased security in mind. multi-layered AES encryption and DNA computing were applied to "Covid 19" images in this research (MLAESDNA). With cloud computing, the MLAESDNA team was able to show that IoT signals could be enhanced with encrypted data

True Random Number Generation Based on DNA molecule Genetic Information (DNA-TRNG)

In digital world cryptographic algorithms protect sensitive information from intruder during communication. True random number generation is used for Cryptography algorithms as key value encryption and decryption process. To develop unbreakable algorithms key as one important parameter for Cryptography .We proposed DNA based True random number generation.DNA is deoxyribonucleic acid chemical molecule present in all living cells. DNA molecule consists of 4 nucleotides A-adenine,T-Thymine,G-Guanine and CCytosine. DNA molecules have uniqueness properties like Each person in the world distinguish based on DNA sequences and genes. The proposed algorithm pass NIST SP 800-22 test suite for DNA based true random number generation with highest Entropy,FFT,Block Frequency and Linear Complexity

The need for polymorphic encryption algorithms: A review paper

Current symmetric ciphers including the Advanced Encryption Standard (AES) are deterministic and open. Using standard ciphers is necessary for interoperability. However, it gives the potential opponent significant leverage, as it facilitates all the knowledge and time he needs to design effective attacks. In this review paper, we highlight prominent contributions in the field of symmetric encryption. Furthermore, we shed light on some contributions that aim at mitigating potential threats when using standard symmetric ciphers. Furthermore, we highlight the need for more practical contributions in the direction of polymorphic or multishape ciphers

Advanced approach for encryption using advanced encryption standard with chaotic map

At present, security is significant for individuals and organizations. All information need security to prevent theft, leakage, alteration. Security must be guaranteed by applying some or combining cryptography algorithms to the information. Encipherment is the method that changes plaintext to a secure form called cipherment. Encipherment includes diverse types, such as symmetric and asymmetric encipherment. This study proposes an improved version of the advanced encryption standard (AES) algorithm called optimized advanced encryption standard (OAES). The OAES algorithm utilizes sine map and random number to generate a new key to enhance the complexity of the generated key. Thereafter, multiplication operation was performed on the original text, thereby creating a random matrix (4×4) before the five stages of the coding cycles. A random substitution-box (S-Box) was utilized instead of a fixed S-Box. Finally, we utilized the eXclusive OR (XOR) operation with digit 255, also with the key that was generated last. This research compared the features of the AES and OAES algorithms, particularly the extent of complexity, key size, and number of rounds. The OAES algorithm can enhance complexity of encryption and decryption by using random values, random S-Box, and chaotic maps, thereby resulting in difficulty guessing the original text

Multi-shape symmetric encryption mechanism for nongeneric attacks mitigation

Static cyphers use static transformations for encryption and decryption. Therefore, the attacker will have some knowledge that can be exploited to construct assaults since the transformations are static. The class of attacks which target a specific cypher design are called Non-Generic Attacks. Whereby, dynamic cyphers can be utilised to mitigate non-generic attacks. Dynamic cyphers aim at mitigating non-generic attacks by changing how the cyphers work according to the value of the encryption key. However, existing dynamic cyphers either degrade the performance or decrease the cypher’s actual security. Hence, this thesis introduces a Multi-Shape Symmetric Encryption Mechanism (MSSEM) which is capable of mitigating non-generic attacks by eliminating the opponents’ leverage of accessing the exact operation details. The base cyphers that have been applied in the proposed MSSEM are the Advanced Encryption Standard (AES) competition finalists, namely Rijndael, Serpent, MARS, Twofish, and RC6. These cyphers satisfy three essential criteria, such as security, performance, and expert input. Moreover, the modes of operation used by the MSSEM are the secure modes suggested by the National Institute of Standards and Technology, namely, Cipher Block Chaining (CBC), Cipher Feedback Mode (CFB), Output Feedback Mode (OFB), and Counter (CTR). For the proposed MSSEM implementation, the sender initially generates a random key using a pseudorandom number generator such as Blum Blum Shub (BBS) or a Linear Congruential Generator (LCG). Subsequently, the sender securely shares the key with the legitimate receiver. Besides that, the proposed MSSEM has an entity called the operation table that includes sixty different cypher suites. Each cypher suite has a specific cypher and mode of operation. During the run-time, one cypher suite is randomly selected from the operation table, and a new key is extracted from the master key with the assistance of SHA-256. The suite, as well as the new key, is allowed to encrypt one message. While each of the messages produces a new key and cypher suite. Thus, no one except communicating parties can access the encryption keys or the cypher suites. Furthermore, the security of MSSEM has been evaluated and mathematically proven to resist known and unknown attacks. As a result, the proposed MSSEM successfully mitigates unknown non-generic attacks by a factor of 2−6. In addition, the proposed MSSEM performance is better than MODEM since MODEM generates 4650 milliseconds to encrypt approximately 1000 bytes, whereas MSSEM needs only 0.14 milliseconds. Finally, a banking system simulation has been tested with the proposed MSSEM in order to secure inbound and outbound system traffic

Advances in SCA and RF-DNA Fingerprinting Through Enhanced Linear Regression Attacks and Application of Random Forest Classifiers

Radio Frequency (RF) emissions from electronic devices expose security vulnerabilities that can be used by an attacker to extract otherwise unobtainable information. Two realms of study were investigated here, including the exploitation of 1) unintentional RF emissions in the field of Side Channel Analysis (SCA), and 2) intentional RF emissions from physical devices in the field of RF-Distinct Native Attribute (RF-DNA) fingerprinting. Statistical analysis on the linear model fit to measured SCA data in Linear Regression Attacks (LRA) improved performance, achieving 98% success rate for AES key-byte identification from unintentional emissions. However, the presence of non-Gaussian noise required the use of a non-parametric classifier to further improve key guessing attacks. RndF based profiling attacks were successful in very high dimensional data sets, correctly guessing all 16 bytes of the AES key with a 50,000 variable dataset. With variable reduction, Random Forest still outperformed Template Attack for this data set, requiring fewer traces and achieving higher success rates with lower misclassification rate. Finally, the use of a RndF classifier is examined for intentional RF emissions from ZigBee devices to enhance security using RF-DNA fingerprinting. RndF outperformed parametric MDA/ML and non-parametric GRLVQI classifiers, providing up to GS =18.0 dB improvement (reduction in required SNR). Network penetration, measured using rogue ZigBee devices, show that the RndF method improved rogue rejection in noisier environments - gains of up to GS =18.0 dB are realized over previous methods

An Adaptive Image Encryption Scheme Guided by Fuzzy Models

A new image encryption scheme using the advanced encryption standard (AES), a chaotic map, a genetic operator, and a fuzzy inference system is proposed in this paper. In this work, plain images were used as input, and the required security level was achieved. Security criteria were computed after running a proposed encryption process. Then an adaptive fuzzy system decided whether to repeat the encryption process, terminate it, or run the next stage based on the achieved results and user demand. The SHA-512 hash function was employed to increase key sensitivity. Security analysis was conducted to evaluate the security of the proposed scheme, which showed it had high security and all the criteria necessary for a good and efficient encryption algorithm were met. Simulation results and the comparison of similar works showed the proposed encryptor had a pseudo-noise output and was strongly dependent upon the changing key and plain image.Comment: Iranian Journal of Fuzzy Systems (2023

Enhancing Electromagnetic Side-Channel Analysis in an Operational Environment

Side-channel attacks exploit the unintentional emissions from cryptographic devices to determine the secret encryption key. This research identifies methods to make attacks demonstrated in an academic environment more operationally relevant. Algebraic cryptanalysis is used to reconcile redundant information extracted from side-channel attacks on the AES key schedule. A novel thresholding technique is used to select key byte guesses for a satisfiability solver resulting in a 97.5% success rate despite failing for 100% of attacks using standard methods. Two techniques are developed to compensate for differences in emissions from training and test devices dramatically improving the effectiveness of cross device template attacks. Mean and variance normalization improves same part number attack success rates from 65.1% to 100%, and increases the number of locations an attack can be performed by 226%. When normalization is combined with a novel technique to identify and filter signals in collected traces not related to the encryption operation, the number of traces required to perform a successful attack is reduced by 85.8% on average. Finally, software-defined radios are shown to be an effective low-cost method for collecting side-channel emissions in real-time, eliminating the need to modify or profile the target encryption device to gain precise timing information