Malware, a lethal weapon of cyber attackers, is becoming increasingly sophisticated, with rapid deployment and self-propagation. In addition, modern malware is one of the most devastating forms of cybercrime, as it can avoid detection, make digital forensics investigation in near real-time impossible, and the impact of advanced evasion strategies can be severe and far-reaching. This makes it necessary to detect it in a timely and autonomous manner for effective analysis. This work proposes a new systematic approach to identifying modern malware using dynamic deep learning-based methods combined with heuristic approaches to classify and detect five modern malware families: adware, Radware, rootkit, SMS malware, and ransomware. Our symmetry investigation in artificial intelligence and cybersecurity analytics will enhance malware detection, analysis, and mitigation abilities to provide resilient cyber systems against cyber threats. We validated our approach using a dataset that specifically contains recent malicious software to demonstrate that the model achieves its goals and responds to real-world requirements in terms of effectiveness and efficiency. The experimental results indicate that the combination of behavior-based deep learning and heuristic-based approaches for malware detection and classification outperforms the use of static deep learning methods.

Land cover and land use classification have been important areas of research in the remote sensing (RS) field. It can give data assistance for many real-world applications, including land planning and use. Convolutional neural networks (CNNs), in particular, demonstrate the remarkable power of feature learning in remote sensing for land use and cover classification, as demonstrated by recent deep learning techniques driven by vast amounts of data. Although the pre-trained models obtained good classification results, it is difficult for them to classify RS images into a correct class with a higher precision rate. In this work, we proposed a new network-level fusion deep architecture based on 16-tiny Vision Transformer and SIBNet. In the initial phase, data augmentation has been performed to resolve the problem of data imbalances. For this purpose, traditional mathematical operations have been performed. In the next step, we proposed a self-attention bottleneck-based inception CNN network named SIBNet. In this network, two architectures are followed. The blocks are designed using inception architecture, and each inception module is created with bottleneck blocks. The 16-tiny vision transformer architecture has been implemented for RS images and fused using a network-level fusion with SIBNet for the first time. Hyperparameters of the proposed model have been initialized using Bayesian Optimization for better training on the RS images. After the fusion, the model was on RS image datasets and extracted deep features from the self-attention layer. The extracted features are classified using a neural network classifier with multiple hidden layers. The experimental process of the proposed architecture has been performed on two publically available datasets, such as EuroSAT and NWPU, and obtained an accuracy of 97.8 and 98.9%, respectively. A detailed ablation study has been performed to test the proposed models and shows that the fusion model achieved improved accuracy. In addition, a comparison is conducted with recent techniques and proposed methods, showing improved precision, recall, and accuracy.

In retail management, the continuous monitoring of shelves to keep track of the availability of the products and following proper layout are the two important factors that boost the sales and improve customer’s level of satisfaction. The studies conducted earlier were either performing shelf monitoring or verifying planogram compliance. As both the activities are important, to tackle this problem, we presented a deep learning and computer vision-based hybrid approach called Hyb-SMPC that deals with both activities. The Hyb-SMPC approach consists of two modules: The first module detects fine-grained retail products using one-stage deep learning detector. For the detection part, the comparison of three deep learning-based detectors, You Only Look Once (YOLO V4), YOLO V5, and You Only Learn One Representation (YOLOR), is provided and the one giving the best result will be selected. The selected detector will perform detection of different categories of SKUs and racks. The second module performs planogram compliance; for this purpose, the company-provided layout is first converted to JavaScript Object Notation (JSON) and then the matching is performed with the postprocessed retail images. The compliance reports will be generated at the end for indicating the level of compliance. The approach is tested in both quantitative and qualitative manners. The quantitative analysis demonstrates that the proposed approach achieved an accuracy up to 99% on the provided dataset of retail, whereas the qualitative evaluation indicates increase in sales and customers’ satisfaction level.

The Internet of Things is emerging as a crucial technology in aiding humans and making their lives easier. Among the human population, a large percentage of people suffer from disabilities resulting in challenges in everyday life particularly people with visual disabilities. While several inventions exist to aid people with blindness in their everyday lives, the tools are not adequate in terms of accessibility and efficiency. Smart cane is a useful technology that is being researched and developed to enhance navigation of the visually impaired through smart obstacle detection. Moreover, artificial intelligence is being incorporated in these devices to provide users with a sense of vision and allow them greater independence. However, these devices are expensive and often do not contain the desired functionality in a compact single device. The research by I‐CANe aims to create a low‐cost, single device system that is equipped with obstacle detection and identification features to enhance navigation for users without using multiple detection devices. The cane will also incorporate wireless communications for safety features such as Global Positioning System tracking and Global System for Mobile communication to allow maximum independence of the user.