Transformers were initially introduced for natural language processing, leveraging the self-attention mechanism. They require minimal inductive biases in their design and can function effectively as set-based architectures. Additionally, transformers excel at capturing long-range dependencies and enabling parallel processing, which allows them to outperform traditional models, such as long short-term memory (LSTM) networks, on sequence-based tasks. In recent years, transformers have been widely adopted in computer vision, driving remarkable advancements in the field. Previous surveys have provided overviews of transformer applications across various computer vision tasks, such as object detection, activity recognition, and image enhancement. In this survey, we focus specifically on image classification. We begin with an introduction to the fundamental concepts of transformers and highlight the first successful Vision Transformer (ViT). Building on the ViT, we review subsequent improvements and optimizations introduced for image classification tasks. We then compare the strengths and limitations of these transformer-based models against classic convolutional neural networks (CNNs) through experiments. Finally, we explore key challenges and potential future directions for image classification transformers.

An insider threat scenario refers to the outcome of a set of malicious activities caused by intentional or unintentional misuse of the organization's systems, networks, data, and resources. Prevention of insider threat is difficult, since trusted partners of the organization are involved in it, who have authorized access to these confidential/sensitive resources. The state-of-the-art research on insider threat detection mostly focuses on developing unsupervised behavioral anomaly detection techniques with the objective of finding out anomalousness or abnormal changes in user behavior over time. However, an anomalous activity is not necessarily malicious that can lead to an insider threat scenario. As an improvement to the existing approaches, we propose a technique for insider threat detection from time-series classification of user activities. Initially, a set of single-day features is computed from the user activity logs. A time-series feature vector is next constructed from the statistics of each single-day feature over a period of time. The label of each time-series feature vector (whether malicious or nonmalicious) is extracted from the ground truth. To classify the imbalanced ground-truth insider threat data consisting of only a small number of malicious instances, we employ a cost-sensitive data adjustment technique that undersamples the nonmalicious class instances randomly. As a classifier, we employ a two-layered deep autoencoder neural network and compare its performance with other popularly used classifiers: random forest and multilayer perceptron. Encouraging results are obtained by evaluating our approach using the CMU Insider Threat Data, which is the only publicly available insider threat data set consisting of about 14-GB web-browsing logs, along with logon, device connection, file transfer, and e-mail log files. We observe that both deep autoencoder and random forest classifiers classify the dataadjusted time-series feature set with high precision, recall, and f-score. Although multilayer perceptron has a high recall, it suffers from a lower precision and f-score compared to the other two classifiers.