Video generation is a core task in computer vision, its key challenge is the effective modeling of the temporal and spatial dependencies between video frames. This paper proposes a diffusion model-based video generation framework, GHS-VDG, which introduces the Video Frame Temporal Graph Aggregator (VFTGA) and Hybrid Spatio-Temporal Attention (HSTA) modules to capture complex spatiotemporal dependencies between video frames, thereby enhancing the quality and accuracy of predicted frames. Specifically, in the VFTGA module, we innovatively represent video frames as graph nodes and construct edge connections according to their temporal order, which effectively enhances the capability of capturing temporal information. The HSTA module combines temporal and spatial attention mechanisms to optimize the learning of spatiotemporal features. Experimental results demonstrate that GHS-VDG performs excellently on datasets such as SMMNIST, BAIR, KTH, and UCF101, validating its effectiveness in video generation tasks.

Rescue UAVs can support important tasks such as rapid response and manoeuvrability, search and rescue, monitoring and assessment, and delivery of relief materials and communication. However, in natural disasters and accidents scenarios, there are difficulties in the autonomous selection of UAV landing sites. This study proposes a landing point detection method that considers UAV attitude and fuses visual depth estimation with matrix computation. Considering image local visual features and global features, the highlight is to propose a multi-layer feature fusion framework, including visual depth estimation module based on Depth-Anything-V2, planar matrix estimation based on Sobel operator, and optimal landing point detection considering UAV attitude. At present, the initial validation has been completed at UseGeo, and the landing point selection is as expected.

Data centers have been growing rapidly in recent years to meet the surging demand of cloud services. However, the expanding scale and powerful servers generate a great amount of heat, resulting in significant cooling costs. A trend in modern data centers is to raise the temperature and maintain all servers in a relatively hot environment. While this can save on cooling costs given benign workloads running in servers, the hot environment increases the risk of a cooling failure. In this article, we unveil a new vulnerability of existing data centers with aggressive cooling energy saving policies. Such a vulnerability might be exploited to launch thermal attacks that could severely worsen the thermal conditions in a data center. Specifically, we conduct thermal measurements and uncover effective thermal attack vectors at the server, rack, and data center levels. We also present damage assessments of thermal attacks. Our results demonstrate that thermal attacks can (1) largely increase the temperature of victim servers degrading their performance and reliability, (2) negatively impact on thermal conditions of neighboring servers causing local hotspots, (3) raise the cooling cost, and (4) even lead to cooling failures. Finally, we propose and evaluate effective server and data center level defenses to enhance thermal stabilities.

Intrusive multi-step attacks, such as Advanced Persistent Threat (APT) attacks, have plagued enterprises with significant financial losses and are the top reason for enterprises to increase their security budgets. Since these attacks are sophisticated and stealthy, they can remain undetected for years if individual steps are buried in background "noise." Thus, enterprises are seeking solutions to "connect the suspicious dots" across multiple activities. This requires ubiquitous system auditing for long periods of time, which in turn causes overwhelmingly large amount of system audit events. Given a limited system budget, how to efficiently handle ever-increasing system audit logs is a great challenge. This paper proposes a new approach that exploits the dependency among system events to reduce the number of log entries while still supporting high-quality forensic analysis. In particular, we first propose an aggregation algorithm that preserves the dependency of events during data reduction to ensure the high quality of forensic analysis. Then we propose an aggressive reduction algorithm and exploit domain knowledge for further data reduction. To validate the efficacy of our proposed approach, we conduct a comprehensive evaluation on real-world auditing systems using log traces of more than one month. Our evaluation results demonstrate that our approach can significantly reduce the size of system logs and improve the efficiency of forensic analysis without losing accuracy.

Privacy and information security in general are major concerns that impede enterprise adaptation of shared or public cloud computing. Specifically, the concern of virtual machine (VM) physical co-residency stems from the threat that hostile tenants can leverage various forms of side channels (such as cache covert channels) to exfiltrate sensitive information of victims on the same physical system. However, on virtualized x86 systems, covert channel attacks have not yet proven to be practical, and thus the threat is widely considered a “potential risk.” In this paper, we present a novel covert channel attack that is capable of high-bandwidth and reliable data transmission in the cloud. We first study the application of existing cache channel techniques in a virtualized environment and uncover their major insufficiency and difficulties. We then overcome these obstacles by: (1) redesigning a pure timing-based data transmission scheme, and (2) exploiting the memory bus as a high-bandwidth covert channel medium. We further design and implement a robust communication protocol and demonstrate realistic covert channel attacks on various virtualized x86 systems. Our experimental results show that covert channels do pose serious threats to information security in the cloud. Finally, we discuss our insights on covert channel mitigation in virtualized environments.

This paper proposes a simple but very effective approach called Gemini to prevent victim users from exposing sensitive credentials to a phishing site. As an emergency line of defense, Gemini assumes that a victim user is already deceived into a phishing site and starts the user authentication procedure. Gemini springs into action once the username field is filled in, and tackles the phishing problem from a new perspective. In particular, by exploiting username input, Gemini is able to provide more accurate detection of a phishing site and much stronger protection for a password, the most confidential and crucial information for user authentication. To validate the efficacy of Gemini, we implement different prototypes of Gemini as a browser extension for IE, Firefox, and Chrome, respectively, and conduct extensive live experiments over various legitimate and phishing websites for more than one month. Our experimental results show that Gemini can achieve zero false negative rate and less than 1% false positive rate, and Gemini can effectively block the access to a phishing site before a victim user begins to enter in a password. Moreover, Gemini is complementary to existing anti-phishing tools. The performance overhead induced by Gemini is minor and has a negligible effect upon users' browsing activities.

Memory deduplication has been widely used in various commodity hypervisors. By merging identical memory contents, it allows more virtual machines to run concurrently on top of a hypervisor. However, while this technique improves memory efficiency, it has a large impact on system security. In particular, memory deduplication is usually implemented using a variant of copy-on-write techniques, for which, writing to a shared page would incur a longer access time than those non-shared. In this paper, we investigate the security implication of memory deduplication from the perspectives of both attackers and defenders. On one hand, using the artifact above, we demonstrate two new attacks to create a covert channel and detect virtualization, respectively. On the other hand, we also show that memory deduplication can be leveraged to safeguard Linux kernel integrity.

Memory deduplication has been widely used in various commodity hypervisors. However, while this technique improves memory efficiency, it has an impact on system security. In particular, memory deduplication is usually implemented using a variant of copy-on-write techniques, for which, writing to a shared page would incur a longer access time than those non-shared. By exploiting this artifact, we demonstrate a new covert channel can be built in a virtualized environment.