Image encryption is an efficient and vital way to protect classified and secret images. With the advancement of the processing power of the computer, AES, DES or chaotic series type just alike image encryption schemes are not as secure as before. So in this paper, we present a new hybrid image encryption method for protecting secret and imperative images by employing Logistic Sine System (LSS) together with two-dimensional Cellular Automata and FSM based DNA rule generator. Secure hash (SHA-256) algorithm is used to generate a secret key and to compute initial values for the LSS. In our proposed method there are three stages and each stage has its own rule. After the scrambling process, the first stage is the Feistel structure based bit inversion (FSBI) to change the pixels value. The second stage is 2D-CA with Moore neighborhood structure based local rules. The third is DNA conversion based on Finite State Machine (FSM-DNA) rule generator. The proposed encryption scheme is robust against well-known attacks such as statistical attacks, brute force attacks, differential attacks, and pixel correlation attacks and also possess strong key sensitivity. Results show that our three-layer hybrid image encryption technique is robust against many well-known attacks and can be applied directly to all type of classified gray-scale images to make them more secure from such cryptography attacks.

Alzheimer’s is a chronic neurodegenerative disease that frequently occurs in many people today. It has a major effect on the routine activities of affected people. Previous advancement in smartphone sensors technology enables us to help people suffering from Alzheimer’s. For people in the Muslim community, where it is mandatory to offer prayers five times a day, it may mean that they are struggling in their daily life prayers due to Alzheimer’s or lack of concentration. To deal with such a problem, automated mobile sensor-based activity recognition applications can be supportive to design accurate and precise solutions with an objective to direct the Namazi (worshipper). In this paper, a Salah activities recognition model (SARM) using a mobile sensor is proposed with the aim to recognize specific activities, such as Al-Qayam (standing), Ruku (standing to bowing), and Sujud (standing to prostration). This model entails the collection of data, selection and placement of sensor, data preprocessing, segmentation, feature extraction, and classification. The proposed model will provide a stepping edge to develop an application for observing prayer. For these activities’ recognition, data sets were collected from ten subjects, and six different features sets were used to get improved results. Extensive experiments were performed to test and validate the model features to train random forest (RF), K-nearest neighbor (KNN), naive Bayes (NB), and decision tree (DT). The predicted average accuracy of RF, KNN, NB, and DT was 97%, 94%, 71.6%, and 95% respectively.

Binary Code Similarity Detection (BCSD) is a fundamental binary analysis technique in the area of software security. Recently, advanced deep learning algorithms are integrated into BCSD platforms to achieve superior performance on well-known benchmarks. However, real-world large programs embed more complex diversities due to different compilers, various optimization levels, multiple architectures and even obfuscations. Existing BCSD solutions suffer from low accuracy issues in such complicated real-world application scenarios. In this paper, we propose BinCola, a novel Transformer-based dual diversity-sensitive contrastive learning framework that comprehensively considers the diversity of compiler options and candidate functions in the real-world application scenarios and employs the attention mechanism to fuse multi-granularity function features for enhancing generality and scalability. BinCola simultaneously compares multiple candidate functions across various compilation option scenarios to learn the differences caused by distinct compiler options and different candidate functions. We evaluate BinCola’s performance in a variety of ways, including binary similarity detection and real-world vulnerability search in multiple application scenarios. The results demonstrate that BinCola achieves superior performance compared to state-of-the-art (SOTA) methods, with improvements of 2.80%, 33.62%, 22.41%, and 34.25% in cross-architecture, cross-optimization level, cross-compiler, and cross-obfuscation scenarios, respectively.

Due to the lack of effective attack detection measures, cyberattacks may cause strong damage to industrial cyber–physical systems (CPSs). The embedding of attack categories learned by the existing attack detection methods is highly coupled to each other with fuzzy boundaries and overlapped neighborhood, leading to weak robustness and high false positive rates. To address these issues, in this article, we propose a few-shot attack detection method based on decoupled prototype learning (DPL-FSAD), aiming to enhance the detection accuracy and generalization capabilities for malicious attacks in CPS. Specifically, we first introduce feature contrastive learning to extract differentiated features from highly similar samples, achieving compact intraclass and sparse interclass feature embedding space. To solve the problem of fuzzy boundaries of different attack categories, prototype contrastive learning is then employed to reduce the coupling degree among prototypes and enhance their discriminability. A regularization term is exploited to mitigate the overfitting problem by reducing the gap between the feature embedding and prototypes. Furthermore, an orthogonal constraint is employed to separate prototypes of different attack types, generating a decoupled prototype embedding space. The experimental results on three public cyberattack datasets show that, compared with the suboptimal model a few-shot learning model with Siamese convolutional neural network (FSL-SCNN), the proposed DPL-FSAD can improve the precision by 5.53%, F 1-score by 3.3%, and reduce the false positive rate by 2.37% in average, which proves that the space decoupled prototype learning is effective for improving the generalization and robustness of industrial CPS attack detection in few-shot scenario.