Visual grounding is the task of locating the relevant region in an image based on a textual description. Most existing methods rely on pre-trained visual and text encoder to extract features from images and text, which are fed into a fusion module to obtain fused features. To obtain high-quality fused features, researchers often design various complex multi-modal fusion modules. These fused features are processed using an encoder-decoder architecture to produce the final output. However, in the training process, the optimization objective is typically centered around the final predicted outputs, such as bounding boxes or instance segmentation masks, while the quality of the fused features often receives less direct attention. Therefore, the gradient usually needs to traverse a relatively long path when being backpropagated to the multi-modal fusion module, leading to diminished optimization effectiveness. In this paper, we propose ALVG, a simple and efficient visual grounding framework. We design a novel loss function to directly supervise the attention mechanism of multi-modal fusion modules, along with a simple but effective text-guided image enhancement module to complement it. The enhanced features are directly used for instance segmentation tasks, as well as object detection tasks. Experiments on six widely used Visual Grounding datasets, including RefCOCO/+/g, ReferIt, Flickr30K, and GRefCOCO, demonstrate the superiority of ALVG. Our method not only improves efficiency and convergence speed but also achieves state-of-the-art performance on these benchmarks.

The classification of encrypted network traffic (ENTC) is vital for ensuring network security, effective administration, and maintaining service quality. To accurately detect malicious encrypted traffic in communications and overcome the challenges posed by traditional detection methods, including the lack of labeled training data, difficulty in feature identification, and reliance on single-method approaches, we propose a novel framework based on Masked Autoencoders (MAE) and multidimensional feature fusion. Using a formatted traffic representation matrix that incorporates hierarchical flow information, we extract raw traffic features, plaintext packet features, and traditional statistical features in image format. Multidimensional feature fusion is achieved through RGB multi-channel integration. Our approach utilizes the MAE paradigm, which pre-trains a classifier on extensive unlabeled data and fine-tunes it with minimal labeled data for traffic classification. Experimental results demonstrate that our method achieves detection accuracy exceeding 98% on three public traffic datasets: USTC-TFC2016, ISCX-VPN2016, and CICIoT2022, significantly outperforming other deep learning methods.

JointCloud computing is a new cloud computing paradigm that enables interconnection between clouds. Compared with other network environments, large-scale diverse data interactions and data collaboration have become the norm in the JointCloud environment based on JointCloud computing. The complex behaviors between entities in the JointCloud environment are realized through the interaction of network traffic. Attackers often mix malicious traffic with benign network traffic to break the JointCloud ecosystem. Among them, encrypted malicious traffic poses a huge security risk to JointCloud environments due to its strong invisibility and fast propagation speeds. Currently, there is a gap in researches on malicious traffic detection in the JointCloud environment, especially encrypted malicious traffic detection. To the best of our knowledge, this paper is the first research work to conduct encrypted malicious traffic detection for JointCloud environment. Specifically, to cope with the complex network traffic data in the JointCloud environment, this paper proposes a spatio-temporal feature extraction method for encrypted traffic to enrich the features of the original traffic. Subsequently, we propose a method of mining JointCloud entity knowledge based on the characteristics of traffic distribution in the JointCloud environment, which can improve detection performance. Finally, we construct an encrypted malicious traffic detection model fusing spatio-temporal features and JointCloud entity knowledge for JointCloud environment (ESTK-JC). In experiments in a simulated JointCloud environment, ESTK-JC exhibits detection performance superior to current state-of-the-art models.

In cloud storage, symmetric encryption is a common method to protect the confidentiality of volume data. One critical issue in symmetric encryption is the management of volume symmetric keys such as key generation, update and distribution. Many schemes have adopted hierarchical structures based on key derivation to generate and organize the keys. However, the efficient update of these derived and associated keys and the distribution of multiple derived keys have not been well studied. This paper mainly studies in-situ key update and traffic cost of key distribution. First, we redesign the key node structure of our binary key-derivation tree to provide the basis of the in-situ key update. Then, secure in-situ key update algorithms are proposed, in which forward secrecy and backward secrecy are guaranteed. Finally, we propose a minimal key set generation algorithm, which can effectively reduce the communication cost of key distribution. We also describe the key distribution and derivation process. Security analysis and extensive experimental evaluations show the proposed algorithms are secure, efficient and practical.

The presence of haze negatively affects the visibility of captured images, posing challenges for general object detection models. We observe that current techniques exhibit three limitations: 1) they typically view image restoration and object detection as separate tasks; 2) they disregard potential details in degraded images that benefit detection; and 3) they lack sufficient recognition ability under haze interference. To this end, we propose a novel Diffusion Model (Diff-HOD) for Object Detection in Hazy weather conditions. Diff-HOD is a multi-task joint learning paradigm that integrates low-level image restoration and high-level object detection. Specifically, to bridge restoration and detection, we present a lightweight restoration module that mitigates the impact of weather-specific information, guiding the shared image encoder to provide high-quality features. We further leverage the excellent modeling ability of diffusion models to enhance the detection capability in hazy conditions. Moreover, we introduce an IoU-aware attention module that utilizes IoU as spatial priors to strengthen relevant features. Extensive experiments demonstrate that our Diff-HOD performs favorably against representative state-of-the-art approaches on both synthetic and natural datasets.

Real-world rain is a mixture of rain streaks and rainy haze. It poses a great challenge for current deraining techniques due to two reasons. First, existing methods consider rain streaks removal and rainy haze removal as separate processes. Second, they are limited in insufficient modeling ability to learn the mapping from the mixture of rain to clean images. To address these issues, we propose a novel Depth-guided Dynamic Message Passing Diffusion Model for the mixture of rain removal, called DG-RainDiff. DG-RainDiff is a joint learning paradigm that integrates depth estimation and image deraining in a diffusion framework. It takes full advantages of additional guidance from depth information and powerful generation ability of diffusion models to significantly improve the capacity in the mixture of rain removal. Furthermore, we also explore the importance of contextual information in image rain removal tasks and introduce a novel dynamic message passing module (DGMP) that contains convolution with offsets to enhance the ability of DG-RainDiff to obtain rich contextual information, thereby achieving a more vivid restoration of occluded pixels. Extensive experiments on both synthetic and real-world data show that DG-RainDiff quantitatively and qualitatively outperforms fifteen state-of-the-art methods.

Multivariate time series anomaly detection on key performance indicators helps mitigate the impact of large-scale IT system anomalies. Due to the large volume and the abstract nature of multivariate time series, previous works have tended to make overly strict or optimistic hypotheses on labeling costs and resulted in unsatisfactory results. Thus, it remains a challenge to make an appropriate trade-off between labeling costs and model performance. This research proposes AMAD, an active learning-based approach that works to address this problem. Its core idea is to provide the learner model high-value label queries via an ensemble query strategy, which dynamically adapts to the estimated anomaly ratio and the ever-changing model performance. Moreover, it is the first to discuss in detail the margin effect of query strategies on model performance, to our knowledge, this has not been investigated in previous works on time series anomaly detection. Extensive experiments on five public datasets demonstrate that AMAD works well and robustly on various real-world scenarios, which outperforms state-of-the-art baseline methods by 16% and 11% in terms of recall and F1, with only 3% of data being labeled.

We propose a novel model‐free unsupervised learning paradigm to tackle the unfavorable prevailing problem of real‐world image deraining, dubbed MUL‐Derain. Beyond existing unsupervised deraining efforts, MUL‐Derain leverages a model‐free Multiscale Attentive Filtering (MSAF) to handle multiscale rain streaks. Therefore, formulation of any rain imaging is not necessary, and it requires neither iterative optimization nor progressive refinement operations. Meanwhile, MUL‐Derain can efficiently compute spatial coherence and global interactions by modeling long‐range dependencies, allowing MSAF to learn useful knowledge from a larger or even global rain region. Furthermore, we formulate a novel multiloss function to constrain MUL‐Derain to preserve both color and structure information from the rainy images. Extensive experiments on both synthetic and real‐world datasets demonstrate that our MUL‐Derain obtains state‐of‐the‐art performance over un/semisupervised methods and exhibits competitive advantages over the fully‐supervised ones.

Object detection remains challenging in hazy weather conditions due to the poor visibility of captured images. There are currently two types of detectors capable of adapting to varying weather conditions: (i) low‐level adaptation methods that combine one detector with an additional dehazing network and (ii) high‐level adaptation methods that explore various kinds of domain adaptation knowledge. However, neither of these approaches can achieve desirable performance due to their inherent limitations. We raise an intriguing question—if combining both low‐level adaptation and high‐level adaptation, can improve the generalization ability of a detector in hazy weather conditions? To answer it, we propose a Joint High‐Low Adaptation Object Detection paradigm (HLA‐HOD) in hazy weather conditions. By combining both low‐level adaptation and high‐level adaptation, HLA‐HOD achieves superior performance on hazy images without requiring ground‐truth bounding boxes or clean images. Extensive experiments demonstrate that our method outperforms state‐of‐the‐art low‐level and high‐level adaptation methods by a large margin both quantitatively and qualitatively.

Intrusion detection systems are crucial in fighting against various network attacks. By monitoring the network behavior in real time, possible attack attempts can be detected and acted upon. However, with the development of openness and flexibility of networks, artificial immunity‐based network anomaly detection methods lack continuous adaptability and hence have poor detection performance. Thus, a novel framework for network anomaly detection with adaptive regulation is built in this paper. First, a heuristic dimensionality reduction algorithm based on unsupervised clustering is proposed. This algorithm uses the correlation between features to select the best subset. Then, a hybrid partitioning strategy is introduced in the negative selection algorithm (NSA), which divides the feature space into a grid based on the sample distribution density and generates specific candidate detectors in the boundary grid to effectively mitigate the holes caused by boundary diversity. Finally, the NSA is improved by self‐set clustering and a novel gray wolf optimizer to achieve adaptive adjustment of the detector radius and position. The results show that the proposed NSA algorithm based on mixed hierarchical division and gray wolf optimization (MDGWO‐NSA) achieves a higher detection rate, lower false alarm rate, and better generation quality than other network anomaly detection algorithms.

LiDAR and camera, as two different sensors, supply geometric (point clouds) and semantic (RGB images) information of 3-D scenes. However, it is still challenging for existing methods to fuse data from the two cross sensors, making them complementary for quality 3-D object detection (3OD). We propose ImLiDAR, a new 3OD paradigm to narrow the cross-sensor discrepancies by progressively fusing the multiscale features of camera Images and LiDAR point clouds. ImLiDAR enables to provide the detection head with cross-sensor yet robustly fused features. To achieve this, two core designs exist in ImLiDAR. First, we propose a cross-sensor dynamic message propagation (CDMP) module to combine the best of the multiscale image and point features. Second, we raise a direct set prediction problem that allows designing an effective set-based detector (SD) to tackle the inconsistency of the classification and localization confidences, and the sensitivity of hand-tuned hyperparameters. Besides, the novel SD can be detachable and easily integrated into various detection networks. Comparisons on the KITTI, nuScenes, and SUN-RGBD datasets all show clear visual and numerical improvements of our ImLiDAR over 45 state-of-the-art 3OD methods.

Temporal knowledge graph (TKG) has received great attention in recent years. However, the TKG is not always complete due to the missing of important facts, which has seriously hindered its wide application. Inferring missing facts in TKG is a critical and challenging task due to its highly dynamic nature. Most of the existing methods mainly focus on modeling the structural features and temporal dependencies of TKG to solve the temporal knowledge graph completion problem (TKGC). However, those methods only operate at a single timescale without considering the latent time variability of TKG and thus limit the performance of TKGC solutions. Therefore, we propose a novel method named MtGCN (Multi-timescale history modeling framework based on Graph Convolutional Networks) for completing TKG by self-adaptively modeling the multi-timescale history of the incomplete TKG. Firstly, MtGCN uses a structural encoder with a graph convolutional network to mine the latent semantic information and structural features of the TKG. Secondly, MtGCN uses GRU-based temporal encoder to learn the historical information at various timescales of the TKG. Finally, it generates effective entity and relation representations to infer the missing facts for the originally incomplete TKG. By conducting comprehensive experiments on 5 public datasets, the experimental results show that our proposed method MtGCN significantly outperforms the baselines by achieving the highest MRR and HITS@1,3,10.

Cloud attack provenance is a well-established industrial practice for assuring transparency and accountability for a service provider to tenants. However, the multi-tenancy and self-service nature coupled with the sheer size of a cloud implies many unique challenges to cloud forensics. Although Virtual Machine Introspection (VMI) is a powerful tool for attack provenance due to the privilege isolation, the stealthiness of state-of-the-art attacks and the lack of precise information make existing attack provenance solutions difficult to fulfill real-time forensics when tracking enormous suspicious behaviors. To this end, we propose an instruction-level tracing framework for inspecting the presence of attacks by dynamically tracking shared processor hardware event patterns and analyzing the attack traces. To overcome the challenges of real-time detection and provenance, we advocate Last Branch Record (LBR) profiling, to extract the suspicious execution flows. With the hardware assistance and software-based virtualization introspection, we show that the framework can provide an effective response to threats in different cases, thereby enabling a quick attack provenance with high fidelity. The evaluation shows that our prototype introduces negligible performance penalties.

Fault localization is a critical step of software debugging. We present a statistical fault localization approach via semi-dynamic slicing in this paper. In our technique, we first conduct the execution flow graph based on both the coverage information and static control-flow-graph to model the executions approximately. Second, we use the backward slicing to analyze the dependence relationships between execution statements and execution results, obtain sliced statements and calculate the coverage statistics. At last, we calculate the fault suspiciousness according to Tarantula, a classic approach of statistical fault localization. Controlled experiments are setup on the Siemens subjects, and the results are promising.