## 2 Literature Review  
Anomaly detection in data streams has been an active research area due to the increasing volume and complexity of data generated by IoT devices and smart environments [2]. Traditional anomaly detection methods, such as statistical techniques [5], clustering [16], and classification [8], have been applied to data streams [6], with varying degrees of success. However, they often struggle to adapt to the dynamic nature of data streams, which may have changing distributions and evolving patterns [5]. For example, during a timed event,the traffic pattern can change dramatically, potentially causing statistical methods that rely on historical data to label the surge in traffic as an anomaly due to the shift in statistical properties like mean and variance [17]. In addition, the traditional clustering methods might not recognize the sudden appearance of a new cluster as an anomaly, leading to delayed detection, or traditional classifiers might struggle to identify novel patterns that were not present in the training data [6]. In summary, traditional anomaly detection methods have limitations that become more pronounced in dynamic data streams with changing distributions and evolving patterns. The technical challenges of concept drift [17], high-dimensional data [7], computational efficiency [18], and feature engineering [19] contribute to their struggles in adapting to these scenarios. This has prompted the exploration of more advanced techniques, including deep learning-based approaches, which have shown better adaptability and scalability in handling the dynamic nature of data streams. Recently, deep learning-based techniques [2] have been proposed for data stream anomaly detection, including autoencoders [20], recurrent neural networks (RNNs) [21], and convolutional neural networks (CNNs) [22]. These methods have demonstrated better adaptability and scalability compared to traditional methods, but they still face challenges in dealing with heterogeneous data types and efficiently focusing on relevant features. Specifically, deep learning techniques face significant challenges in dealing with heterogeneous data types and efficiently focusing on relevant features [2]. These challenges include handling diverse data types, ensuring feature relevance and selection, addressing data imbalance, and interpreting deep models [23]. Heterogeneous data types, such as numerical, categorical, text, image, and time series data, can be challenging to integrate and process effectively [7; 24]. Researchers are exploring techniques to handle multiple data types [25], such as specialized network architectures [26] or converting different data types into a common feature space [27]. Feature engineering and selection techniques aim to identify the most informative features, while data imbalance can lead to models favoring the majority class and performing poorly in anomaly detection [28]. Interpretable models are crucial to understanding the underlying patterns learned by deep learning models, such as in manufacturing processes where engineers need to know which factors contributed to anomaly detection [29]. Researchers are developing techniques to explain deep model decisions, such as attention mechanisms, feature attribution methods, and gradient-based visualizations, to provide insights into which features were influential in making anomaly predictions [30]. Cross-modal learning [31] refers to the process of learning shared representations from multiple data modalities, such as images, text, and audio. It has shown great potential in various applications, including multimedia retrieval [32], recommendation systems [33], and multimodal sentiment analysis [25]. Several methods have been proposed for cross-modal learning, including deep neural networks [34], matrix factorization [35], and probabilistic graphical models [36]. Recently, cross-modal learning has been integrated with attention mechanisms to improve the interpretability and performance of the learned representations [37; 38; 39].