Networks play important roles in modern life, and cyber security has become a vital research area. An intrusion detection system (IDS) which is an important cyber security technique, monitors the state of software and hardware running in the network. Despite decades of development, existing IDSs still face challenges in improving the detection accuracy, reducing the false alarm rate and detecting unknown attacks. To solve the above problems, many researchers have focused on developing IDSs that capitalize on machine learning methods. Machine learning methods can automatically discover the essential differences between normal data and abnormal data with high accuracy. In addition, machine learning methods have strong generalizability, so they are also able to detect unknown attacks. Deep learning is a branch of machine learning, whose performance is remarkable and has become a research hotspot. This survey proposes a taxonomy of IDS that takes data objects as the main dimension to classify and summarize machine learning-based and deep learning-based IDS literature. We believe that this type of taxonomy framework is fit for cyber security researchers. The survey first clarifies the concept and taxonomy of IDSs. Then, the machine learning algorithms frequently used in IDSs, metrics, and benchmark datasets are introduced. Next, combined with the representative literature, we take the proposed taxonomic system as a baseline and explain how to solve key IDS issues with machine learning and deep learning techniques. Finally, challenges and future developments are discussed by reviewing recent representative studies.

In recent years, the design of models for performing the trajectory prediction task, one of the critical tasks in autonomous driving, has received great attention from researchers. However, accurately predicting future locations is challenging due to the difficulty of learning accurate intentions and modeling multimodality. Historical paths at a certain location can help predict the future trajectory of an agent currently located in that position and address these limitations. In this work, we propose a goal-based local behavior guided model, Goal-LBP, using such information (referred to as local behavior data) to generate potential goals and guide the prediction of trajectories conditioned on such goals. Goal-LBP uses Transformer encoders to extract homogeneous features and attention mechanism to represent the heterogeneous interactions and subsequently uses an encoder-decoder Gated Recurrent Unit (GRU) model to generate predictions. We evaluate our Goal-LBP using two large-scale real-world autonomous driving datasets, namely nuScenes and Argoverse. Our results show that compared to several SOTA models, Goal-LBP achieves the best ADE/FDE performance and it ranked #2 on the leaderboard of the nuScenes trajectory benchmark in June 2023. In addition, we also demonstrate that our local behavior estimator block can be easily added to two existing SOTA methods, namely AgentFormer and LaPred. Adding this LBE block improves the original AgentFormer and LaPred performance by at least 10%.

For autonomous vehicles (AVs), the ability for effective end-to-end perception and future trajectory prediction is critical in planning a safe automatic maneuver. In the current AVs systems, perception and prediction are two separate modules. The prediction module receives only a restricted amount of information from the perception module. Furthermore, perception errors will propagate into the prediction module, ultimately having a negative impact on the accuracy of the prediction results. In this paper, we present a novel framework termed BEV-TP, a visual context-guided center-based transformer network for joint 3D perception and trajectory prediction. BEV-TP exploits visual information from consecutive multi-view images and context information from HD semantic maps, to predict better objects’ centers whose locations are then used to query visual features and context features via the attention mechanism. Generated agent queries and map queries facilitate learning of the transformer module for further feature aggregation. Finally, multiple regression heads are used to perform 3D bounding box detection and future velocity prediction. This center-based approach achieves a differentiable, simple, and efficient E2E trajectory prediction framework. Extensive experiments conducted on the nuScenes dataset demonstrate the effectiveness of BEV-TP over traditional pipelines with sequential paradigms.