Figure - available from: Journal of Ambient Intelligence and Humanized Computing
This content is subject to copyright. Terms and conditions apply.
Source publication
The security of transmitting information from one point to another through different media is an important issue in the modern digital society. Researchers have consistently developed and proposed modern data transfer systems in order to transfer data in a safe manner. DNA (deoxyribonucleic acid) based encryption technology is a new encryption mode...
Citations
... Figure 1 describes both the encryption process and the decryption process. Decryption is the process of changing messages in a coded language (ciphertext) into original messages (plaintext) [21], [22]. Decryption is the reverse process of encryption which returns the passwords or information that has been traced to the original file form by using a key or code. ...
... On the contrary, an unusual case identification system studies the network's activity and identifies patterns. Once the regular behavior has been profiled, it automatically creates a datadriven framework to identify differences in the presence of any abnormalities [20]. ...
Today’s world depends on the Internet to meet all its daily needs. The usage of the Internet is growing rapidly. The world is using the Internet more frequently than ever. The hazards of harmful attacks have also increased due to the growing reliance on the Internet. Hazards to cyber security are actions taken by someone with malicious intent to steal data, destroy computer systems, or disrupt them. Due to rising cyber security concerns, cyber security has emerged as the key component in the fight against all online threats, forgeries, and assaults. A device capable of identifying network irregularities and cyber-attacks is intrusion detection. Several techniques have been created for Intrusion Detection Systems (IDS). There are elements in their effectiveness. Nevertheless, that provides room for more study. Finding an automatic method for detecting cyber-attacks is one of the biggest problems in cyber security. The recent trend is that the Machine Learning (ML) method has been demonstrated to be superior to conventional methods for IDS. Utilizing machine learning approaches, an effective intrusion prevention system will be designed. This research assessed different intrusion detection classification systems with particular applications. Before using ML classifiers for the classification process, the matrix factorization step of the Particle Swarm Optimization (PSO) technique was carried out. The categorization methods used in this study to classify network abnormalities were taken into consideration. Particle Swarm Optimization and Support Vector Machine classifiers (PSO + SVM) will be utilized in the proposed approach. The KDD-CUP 99 dataset will be used to confirm the results of the recognition algorithms. Due to the implementation, several performance metrics will be evaluated for various cyber-attack types, including specificity, recall, F1-score, accuracy, precision, and reliability.
... In [32], a Telugu encryption method based on genetic DNA algorithm is proposed, which follows the genetic process to encrypt English text into Telugu characters, and has a good avalanche effect. In [33], the authors showed an asymmetric DNA encryption and decryption technique for the Arabic plaintext. The authors utilized a mixture of RSA, dynamic encoding and DNA computing techniques to encrypt messages with good randomness. ...
The unique infinite self-renewal ability and multidirectional differentiation potential of stem cells provide a strong support for the clinical treatment. In light of the growing demands for stem cell storage, how to ensure personal privacy security and comply with strict ethical supervision requirements is particularly important. In order to solve the problem of low security of traditional encryption algorithm, we proposed a double encryption protection (DEP) algorithm for stem cell bank privacy data based on improved AES and chaotic encryption technology. Firstly, we presented the hash value key decomposition algorithm, through the hash value dynamic coding, cyclic shift, conversion calculation to get the key of each subsystem in the built algorithm. Secondly, DEP algorithm for privacy data is realized with two level of encryption. The first level of encryption protection algorithm used AES as the main framework, adding dynamic coding and byte filling based on DNA coding, and carries out dynamic shift of rows and simplified mixing of columns. The second level of encryption protection algorithm conducted random encoding, operation, diffusion and decoding based on the results of our proposed sequence conversion algorithm. Finally, we raised two evaluation indexes, the number of characters change rate (NCCR) and the unified average change intensity of text (UACIT) to measure the sensitivity of encryption algorithms to changes in plain information. The experimental results of using DEP shown that the average values of histogram variance, information entropy, NCCR and UACIT are116.7883, 7.6688, 32.52% and 99.67%, respectively. DEP algorithm has a large key space, high key sensitivity, and enables dynamic encryption of private data in stem cell bank. The encryption scheme provided in this study ensures the security of the private information of stem cell bank in private cloud environment, and also provides a new method for the encryption of similar high confidentiality data.
... Here, [[.]] stands for the integer value operator, and n signifies the quantity of generated wind speed variables. R 2 statistics assess the accuracy of the fit, while RMSE evaluates the variance of the appropriate model from the real data distributions [23]. Unlike the R 2 test, which quantifies this variability, γ 2 statistics measures how well the fitted distribution tracks variability in the real data distributed. ...
The latest power sources trend is renewable energy. Numerous researchers, innovators, and technicians work diligently to exploit renewable power. The effectiveness of wind speed data is crucial when estimating wind energy production from a wind turbine. For evaluating wind possibilities, there are several studies on the four generally utilized wind speed distribution models. However, there isn't enough research to determine how sensitive these models are to the observed wind speed data quality. The current research aims to demonstrate wind speed data distribution using Weibull distribution. Researchers discover that the statistical data of the changes among consecutive points of the anticipated wind speed information may drastically deviate from the characteristics of the observed wind. Firstly, the variables of Weibull distribution are estimated using modified maximum likelihood (MML) methods, Energy Pattern Factor and method of moment (MOM). The suggested approach is based on an algorithm that compares the statistical characteristics of the produced and actual wind speeds to get a more precise estimation. Therefore, the findings show that using Weibull distribution to represent wind speed modelling is feasible, precise, and efficient.
... The LCA model has been created to depict the kinetics of response conflict in a neutrally realistic fashion. The frequency of concentration for the cth storage tank is denoted by q c , the lateral inhibitory activity variable is denoted by b, the discharge variable is denoted by k, and the degree of sound in the condensation process is denoted by n t Ɲ 0; g ð , which would be attracted from a normally distributed with such a mean of zero and sample variance of t when modeled [26]. To put it another way, at each time interval t in the gathering evidence procedure. ...
Due to advancements in Bayesian modeling, the likelihood-free posterior estimate is now feasible. These estimation methods are crucial for a deeper understanding of simulation-based systems since it might be difficult, if not impossible, to estimate probability values. This compares the effectiveness of such updated estimate approaches to earlier maximum likelihood forecasting models and offers some adjustments to maximum likelihood estimation for estimating the parameters of the Bayesian analysis in this work. Recent improvements in Bayesian modeling have made it feasible to obtain the likelihood-free posterior estimate. These estimate methods are essential for evaluating simulation-based theories since it might be difficult, if not impossible, to determine probability values. Simulation-based concepts such as the Leaky Competing Accumulator (LCA) theory and Feed-Forward Inhibition (FFI) theory have not yet benefited from Bayesian techniques. As assessment criteria, total relative deviation (TRD), total mean square error (TMSE), and Stein Loss Function (SLF) are used. Maximum likelihood (ML) estimates are modified because the original statistic's Cumulative Distribution Function (CDF) is better than traditional ML estimations and other modified estimators emphasize average and coefficient variability. Prior estimator performance was unaffected by response rate or real parameter settings. These systems have not been formally evaluated by the Bayesian factor. Ó 2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/ licenses/by-nc-nd/4.0/).
... With the advancing trends, expansion, and applications of the data communication system, there is a great demand for research to increase the security system and devices. One of the main technique to protect the user information sent through the transmission channel is the data encryption Tsai et al. [25] In general, symmetric or asymmetric cryptosystems were utilized extensively for securing the information, in which symmetric method uses a key that is similar to the transmitter and receiver and asymmetric key make use of variable key generation for data encryption and data decryption Alruily et al. [4]. Our study employed AES algorithm and Simulation Box (S-Box) with variable key pattern generation for securing the huge data against multi-level attacks. ...
In recent times, data transmission in electronic medium is found to be more susceptible to several attacks. The study aims to control the multi-level attacks in encryption and decryption process by using Advanced Encryption Standard (AES) algorithm based S box operations. In AES based variable key generation pattern, every round generates the new key. The generation of multiple keys strengthen the operation of AES-dynamic S box. The AES algorithm performs operation on a 128 bit plain text and utilizes identical key for decryption and encryption process. The proposed algorithm shows significant improvements in the quality of encryption and decryption. The performance of the proposed system has been analysed in accordance with delay, power consumption and number of slices. Further the efficiency of the proposed system has been compared with other existing methods such as Positive Polarity Reed Muller (PPRM), Modified Positive Polarity Reed Muller (MPPRM) Twisted Binary Decision Diagram (TBDD) and Composite Field (CF) architecture. The results exposed that the proposed system outperforms with superior performance.
... Arabic NLP (ANLP) is attracting the attention of researchers because there is a great need for tools for analyzing and understanding text automatically in various application domains, such as machine translation, sentiment analysis, and question answering. There has been a long line of research on ANLP in various fields, ranging from traditional text classification to encryption and decryption techniques [43]. In this section, literature related to ARD is covered. ...
With the increased popularity of social media platforms, people are increasingly depending on them for news and updates. Even official media channels post news on social media platforms such as Twitter and Facebook. However, with the vast amount of user-generated content, the credibility of shared information must be verified, and this process should be performed automatically and efficiently to accommodate the huge rate of generated posts. Current technology provides powerful methods and tools to solve the issue of rumor spreading on social networks. In this study, the aim is to investigate the use of state-of-the-art machine learning and deep learning models to detect rumors in a collection of Arabic tweets using the ArCOV19-Rumors dataset. A comprehensive comparison of the performance of the models was conducted. In deep learning experiments, the performances of seven optimizers were compared. The results demonstrated that using over-sampled data did not enhance classical and deep learning models. By contrast, using stacking classifiers increased the predictive model’s performance. As a result, the model became more logical and realistic in predicting rumors, non-rumors, and other classes than using classical machine learning without the stacking technique. Additionally, both long short-term memory (LSTM) and bidirectional-LSTM (Bi-LSTM) with the Root mean square propagation (RMSprop) optimizer obtained the best results. Finally, the results were analyzed to explain and interpret the low performance.
... To save data from robberies, secret writing methods were used, and the most popular ones are cryptography and steganography. DNA cryptography can encrypt or encode the data using DNA computing techniques due to the DNA properties like parallel molecular computing, storing, transmitting the data, and computing capabilities [11][12][13][14]. DNA is also used for other purposes like Cryptography, Intrusion detection systems, and Steganography. ...
... In [18,12], the authors developed a modern data security method by considering the advantage of DNA-based Advanced Encryption Standard (AES) cryptography and DNA steganography. This technique will furnish multilayer security to the secret message. ...
— In this study, a new method has been eliciting for encoding 2D and 3D color images. The DNA strand construction was used as the basis for structuring the method. This method consisted of two main stages, the encryption and decryption stages. As each stage includes several operations to reach the desired goal. In the coding stage, a special table was prepared to show the mechanism of work. It starts with encoding the DNA bases into two binary orders, then two zeros are added to the string to finally consist of four binary bits whose size is parallel to the representation of a set of hexadecimal numbers represented in binary, where the XOR operation is then done between the two values to be the result is completely different from the original code. Then the binary values we obtained are converted to decimal values that are placed in an array with the same size as the image to be encoded. Finally, this last array was processed with the exponential function factor, so the final result is a 100% encoded image. In the decoding stage, another algorithm was built that reflects the work of what preceded it in the encryption stage, where the result was an exact copy of the original image. It is worth noting that standard images of different sizes were used as testing images. The performance evaluation of the method was calculated based on several factors: MSE, peak PSNR, and the time required to perform the encoding and decoding process. The method achieved good results when compared with the results of other methods in terms of quality and time.
... 102 and private key encryption to encrypt a binary document [3,9]. Many approaches extended RSA algorithm to encrypt text documents through representing text data in a binary manner [10]. ...
... Another related approach was Playfair Key Matrix which is a three-dimensional representation of the Caesar' method [14,15] leading to more complex decoding to achieve better confidentiality. Other techniques use the genetic algorithm for novel text encryption and decryption [16,17,9] using Residue Number System (RNS) by benefiting from inherent properties. Finally, a hybrid technique is proposed by Ramadan et al. [18] that uses Volvox representation algorithm, which produces a positive integer number that helps in encryption/decryption of an English text. ...
This paper proposes a new algorithm for text encryption utilizing English words as a unit of encoding. The algorithm vanishes any feature that could be used to reveal the encrypted text through adopting variable code lengths for the English words, utilizing a variable-length encryption key, applying two-dimensional binary shuffling techniques at the bit level, and utilizing four binary logical operations with randomized shuffling inputs. English words that alphabetically sorted are divided into four lookup tables where each word has assigned an index. The strength of the proposed algorithm concluded from having two major components. Firstly, each lookup table utilizes different index sizes, and all index sizes are not multiples of bytes. Secondly, the shuffling operations are conducted on a two-dimensional binary matrix with variable length. Lastly, the parameters of the shuffling operation are randomized based on a randomly selected encryption key with varying size. Thus, the shuffling operations move adjacent bits away in a randomized fashion. Definitively, the proposed algorithm vanishes any signature or any statistical features of the original message. Moreover, the proposed algorithm reduces the size of the encrypted message as an additive advantage which is achieved through utilizing the smallest possible index size for each lookup table. Keywords: Encryption algorithm Word-base English text encoding Two-dimensional shuffling operations Encryption/Decryption Algorithm This is an open access article under the CC BY-SA license.
... A long line of research on ANLP was found in the literature in different fields, ranging from text classification to encryption and decryption methods [40]. Several ML approaches were applied to tasks on social media platforms. ...
In today’s world, news outlets have changed dramatically; newspapers are obsolete, and radio is no longer in the picture. People look for news online and on social media, such as Twitter and Facebook. Social media contributors share information and trending stories before verifying their truthfulness, thus, spreading rumors. Early identification of rumors from social media has attracted many researchers. However, a relatively smaller number of studies focused on other languages, such as Arabic. In this study, an Arabic rumor detection model is proposed. The model was built using transformer-based deep learning architecture. According to the literature, transformers are neural networks with outstanding performance in natural language processing tasks. Two transformers-based models, AraBERT and MARBERT, were employed, tested, and evaluated using three recently developed Arabic datasets. These models are extensions to the BERT, Bidirectional Encoder Representations from Transformers, a deep learning model that uses transformer architecture to learn the text representations and leverages the attention mechanism. We have also mitigated the challenges introduced by the imbalanced training datasets by employing two sampling techniques. The experimental results of our proposed approaches achieved 0.97 accuracy. This result demonstrated the effectiveness of the proposed method and outperformed other existing Arabic rumor detection methods.
