The Android operating system has gained popularity and evolved rapidly since the previous decade. Traditional approaches such as static and dynamic malware identification techniques require a lot of human intervention and resources to design the malware classification model. The real challenge lies with the fact that inspecting all files of the application structure leads to high processing time, more storage, and manual effort. To solve these problems, optimization algorithms and deep learning has been recently tested for mitigating malware attacks. This manuscript proposes Summing of neurAl aRchitecture and VisualizatiOn Technology for Android Malware identification (SARVOTAM). The system converts the malware non-intuitive features into fingerprint images to extract the quality information. A fine-tuned Convolutional Neural Network (CNN) is used to automatically extract rich features from visualized malware thus eliminating the feature engineering and domain expert cost. The experiments were done using the DREBIN dataset. A total of fifteen different combinations of the Android malware image sections were used to identify and classify Android malware. The softmax layer of CNN was substituted with machine learning algorithms like K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Random Forest (RF) to analyze the grayscale malware images. It is observed that CNN-SVM model outperformed original CNN as well as CNN-KNN, and CNN-RF. The classification results showed that our method is able to achieve an accuracy of 92.59% using Android certificates and manifest malware images. This paper reveals the lightweight solution and much precise option for malware identification.

Due to the increasing need for accurate location-based services, indoor positioning systems (IPSs) have evolved rapidly. This study reviews the literature published from 2010 to 2024, employing the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology for the identification, screening, validation, and inclusion of research literature. By exploring the complexities of IPS methodologies, algorithms, technologies, and challenges, this study offers a comprehensive introduction for researchers, scholars, and specialists. Additionally, the scope of this study expands its focus to encompass mapping techniques, such as crowdsourcing, geographic information systems (GISs), remote sensing, LiDAR, cartography, and augmented reality (AR). The findings of this study will contribute to the increased applicability of these methods and techniques in the field of urban planning and environmental management. Contributing to the expanding pool of knowledge on indoor positioning, this work is a valuable resource for those exploring the ever-changing field of IPS. This work not only contributes to the academic but also bridges the gap between scientific discoveries and practical, real-world applications.

The super packed functionalities and artificial intelligence (AI)-powered applications have made the Android operating system a big player in the market. Android smartphones have become an integral part of life and users are reliant on their smart devices for making calls, sending text messages, navigation, games, and financial transactions to name a few. This evolution of the smartphone community has opened new horizons for malware developers. As malware variants are growing at a tremendous rate every year, there is an urgent need to combat against stealth malware techniques. This paper proposes a visualization and machine learning-based framework for classifying Android malware. Android malware applications from the DREBIN dataset were converted into grayscale images. In the first phase of the experiment, the proposed framework transforms Android malware into fifteen different image sections and identifies malware files by exploiting handcrafted features associated with Android malware images. The algorithms such as Gray Level Co-occurrence Matrix-based (GLCM), Global Image deScripTors (GIST), and Local Binary Pattern (LBP) are used to extract the handcrafted features from the image sections. The extracted features were further classified using machine learning algorithms like K-Nearest Neighbors, Support Vector Machines, and Random Forests. In the second phase of the experiment, handcrafted features were fused with CNN features to form the feature fusion strategy. The classification performance was evaluated against every malware image file section. The results obtained using the Feature Fusion strategy are compared with handcrafted features results. The experiment results conclude to the fact that Feature Fusion-SVM model is most suited for the identification and classification of Android malware using the certificate and Android Manifest (CR + AM) malware images. It attained an high accuracy of 93.24%.

The Metaverse envisions a future where immersive online interactions become the norm. However, this vision can only be safely realized if user safety and privacy are prioritized. This review critically evaluates existing user safety and security measures within the Metaverse. We have highlighted the limited ability of existing solutions to protect against harassment, identity theft, and the misuse of personal data collected in this environment. This research comprehensively analyzes user safety and security dimensions within virtual worlds and presents a taxonomy to classify the unique threats and vulnerabilities Metaverse users may encounter. This review reveals critical gaps, including the lack of comprehensive security frameworks and balanced privacy-preserving models in virtual immersive environments. In response, we propose a novel Metaverse Security Architecture designed with Zero-Trust principles. This architecture prioritizes user control over data, identity, and experiences. It also emphasizes proactive security measures to mitigate diverse potential harms in virtual worlds. Our research highlights the critical importance of such robust, user-centric frameworks in enabling a Metaverse that is immersive, safe, and secure for all its participants.

Skin disorders encompass a wide range of conditions that affect the skin, a vital organ of the human body. Early detection of skin diseases is difficult due to a lack of awareness, subtle symptoms, similarities, inter-individual heterogeneity in symptoms, limited access to dermatologists, and difficulties in imaging techniques. In this article, we propose a clinical decision support model for detection and classification of skin diseases (DermCDSM) through productive improvement of division capabilities and a cross-breed profound learning procedure. The division cycle is expected to be further developed using an improved chameleon swarm optimization (ICSO) method that takes into account a more accurate and proficient identification of the main cause of the disease. By employing the ICSO algorithm, we aim to enhance the overall accuracy and reliability of disease detection methods. Multi-strategy seeking optimization (MSSO), which is used to optimize feature selection by identifying the most significant features for the task at hand, has been introduced to handle the tests connected to data dimensionality. Convolutional deep spiking neural networks (CD-SNN), a deep learning method, have been implemented to improve the precision of skin cancer diagnosis and multi-class classification. The benchmark ISIC 2017 dataset is utilized to validate the efficacy of our proposed framework, DermCDSM, and its superiority over existing approaches in terms of accuracy, dependability, and efficiency is demonstrated.

Ransomware is a special malware designed to extort money in return for unlocking the device and personal data files. Smartphone users store their personal as well as official data on these devices. Ransomware attackers found it bewitching for their financial benefits. The financial losses due to ransomware attacks are increasing rapidly. Recent studies witness that out of 87% reported cyber-attacks, 41% are due to ransomware attacks. The inability of application-signature-based solutions to detect unknown malware has inspired many researchers to build automated classification models using machine learning algorithms. Advanced malware is capable of delaying malicious actions on sensing the emulated environment and hence posing a challenge to dynamic monitoring of applications also. Existing hybrid approaches utilize a variety of features combination for detection and analysis. The rapidly changing nature and distribution strategies are possible reasons behind the deteriorated performance of primitive ransomware detection techniques. The limitations of existing studies include ambiguity in selecting the features set. Increasing the feature set may lead to freedom of adept attackers against learning algorithms. In this work, we intend to propose a hybrid approach to identify and mitigate Android ransomware. This study employs a novel dominant feature selection algorithm to extract the dominant feature set. The experimental results show that our proposed model can differentiate between clean and ransomware with improved precision. Our proposed hybrid solution confirms an accuracy of 99.85% with zero false positives while considering 60 prominent features. Further, it also justifies the feature selection algorithm used. The comparison of the proposed method with the existing frameworks indicates its better performance.