Augmented Reality (AR) and Multimodal Large Language Models (LLMs) are rapidly evolving, providing unprecedented capabilities for human-computer interaction. However, their integration introduces a new attack surface for Social Engineering (SE). In this paper, we systematically investigate the feasibility of orchestrating AR-driven Social Engineering attacks using Multimodal LLM for the first time, via our proposed SEAR framework, which operates through three key phases: (1) AR-based social context synthesis, which fuses Multimodal inputs (visual, auditory and environmental cues); (2) role-based Multimodal RAG (Retrieval-Augmented Generation), which dynamically retrieves and integrates social context; and (3) ReInteract social engineering agents, which execute adaptive multiphase attack strategies through inference interaction loops. To verify SEAR, we conducted an IRB-approved study with 60 participants and build a novel dataset of 180 annotated conversations in different social scenarios (e.g., coffee shops, networking events). Our results show that SEAR is highly effective at eliciting high-risk behaviors (e.g., 93.3% of participants susceptible to email phishing). The framework was particularly effective in building trust, with 85% of targets willing to accept an attacker's call after an interaction. Also, we identified notable limitations such as authenticity gaps. This work provides proof-of-concept for AR-LLM driven social engineering attacks and insights for developing defenses against next-generation AR/LLM-based SE threats.

The improvement of transferability of adversarial examples is the key property in practical black-box scenarios. Recent research has identified that transferable adversarial examples for video models can be effectively crafted with image models. However, existing studies primarily target single-layer features, overlooking the influence of diverse feature layers. Moreover, they neglect transitions between video frames and fail to fully capture temporal context. In this paper, we introduce an efficient and stable cross-modal attack method termed Feature and Temporal Disruption Attack (FTDA). Our approach caters to both feature space diversity and temporal cues by introducing two innovative modules, i.e., Depth-Aware Feature Fusion Attack (DF2A) and Clip-Based Temporal Fusion Attack (CTFA). Extensive experiments demonstrate that our approach achieves SOTA. Our code is available at https://github.com/xiaopengge2000/FTDA.

In the growing trend of remote working, millions of edge networks (e.g., homes or branch offices) are increasingly threatened by Advanced Persistent Threats (APTs), because of the weakened segmentation between business and non-business devices in remote working environment. Despite the fact that numerous APT detection mechanisms have been proposed, all of them are struggling to handle thecomplex structure, themassive scaleand thediverse topologyof edge networks.Can recent machine-learning advances tackle these APT detection pain points in edge networks?The GNNs (Graph Neural Networks) seems to be suited to capture thecomplex structure, but its adjacency matrix fails to capture key network flow context. Additionally, GNNs require extensive manual labeling, which is not scalable. LLMs (Large Language Models) have the potential to provide automatic labeling for the GNNs, but they lack the supplementary security context needed for effective labeling. To address these gaps, we presentLLMGraph, which incorporates extended GCNs (Graph Convolutional Networks) and domain-specific RAG (Retrieval-Augmented Generation) pipeline to achieve label-free detection against APTs in edge networks.LLMGraph's extended GCNs model can capture network flow context and direction.LLMGraph's domain-specific RAG pipeline can supplement key security contexts, including device vulnerability and network flow, for effective labeling. Additionally,LLMGraphprovides an LLM aggregator to augment and merge thediverse topologyof the edge networks. Compared to the state-of-the-art mechanisms,LLMGraphproveseffectiveandscalable, improving the F1-score by at least 46.9%, and the training time for 1 million edge networks is within 1000 s.

With the tremendous popularity gained by container technology, many applications are being containerized: splitting into numerous containers connected by networks. However, current container networking solutions have either bad performance or poor portability, which undermines the advantages of containerization. In this paper, we propose FreeFlow, a container networking solution which achieves both high performance and good portability. FreeFlow is designed according to the observation that strict isolations are unnecessary among containers trusting each other, and it can significantly boost the communication quality of containers by compromising isolation a little bit. Specifically, we enable containers on the same physical machine to communicate via shared-memory and the ones on different physical machines communicate via high performance networking options, e.g. RDMA and DPDK. Naively wrapping up all the solutions together will result in poor potability of containers and huge complexity in application development. Instead, FreeFlow leverages a network abstraction which supports all common network APIs and a centralized network orchestrator which decides how to deliver data transparently to applications in the containers.

In this paper, we propose a highly efficient scheme, SLICE (a scalable and low stretch routing scheme), enabling greedy routing for wireless sensor networks (WSNs) deployed in complex-connected 3-D settings, whose topologies are often theoretically modeled as high genus 3-D WSNs. Compared to previous 3-D greedy embedding techniques, SLICE improves both the robustness and applicability. 1) It achieves a smaller distance distortion and a lower routing stretch with guaranteed delivery. While it follows the basic idea to embed the surface network to a planar topology to enable greedy routing, the embedding method proposed in SLICE is novel. We first slice the surface network to a genus-0 open surface with exactly one boundary. Then, to achieve a lower distance distortion, we purposely propose a variation of the Ricci flow algorithm, by which this open surface is flattened not to a planar annulus, but to a planar convex polygon, resulting in a lower routing stretch. 2) This is the first work, to the best of our knowledge, that enables greedy routing in high genus 3-D WSNs with general topologies. SLICE not only works for high genus 3-D surface WSNs, but also can be easily adapted to more general cases: high genus 3-D surface networks with holes, and high genus 3-D volume networks. For a high genus 3-D surface network with holes, SLICE embeds it to a planar convex polygon with circular holes, where our proposed greedy routing variation can be applied. For a high genus 3-D volume network, SLICE embeds the inner nodes to a height structure attached to the convex polygon, and a variation of greedy routing scheme with guaranteed delivery is proposed in this structure. The effectiveness of SLICE is validated by extensive simulations.

The Internet-of-Things (IoT) has quickly moved from the realm of hype to reality with estimates of over 25 billion devices deployed by 2020. While IoT has huge potential for societal impact, it comes with a number of key security challenges---IoT devices can become the entry points into critical infrastructures and can be exploited to leak sensitive information. Traditional host-centric security solutions in today's IT ecosystems (e.g., antivirus, software patches) are fundamentally at odds with the realities of IoT (e.g., poor vendor security practices and constrained hardware). We argue that the network will have to play a critical role in securing IoT deployments. However, the scale, diversity, cyberphysical coupling, and cross-device use cases inherent to IoT require us to rethink network security along three key dimensions: (1) abstractions for security policies; (2) mechanisms to learn attack and normal profiles; and (3) dynamic and context-aware enforcement capabilities. Our goal in this paper is to highlight these challenges and sketch a roadmap to avoid this impending security disaster.

Efficient sensor network design requires a full understanding of the geometric environment in which sensor nodes are deployed. In practice, a large-scale sensor network often has a complex and irregular topology, possibly containing obstacles/holes. Convex network partitioning, also known as convex segmentation, is a technique to divide a network into convex regions in which traditional algorithms designed for a simple network geometry can be applied. Existing segmentation algorithms heavily depend on concave node detection, or sink extraction from the median axis/skeleton, resulting in sensitivity of performance to network boundary noise. Furthermore, since they rely on the network's 2-D geometric properties, they do not work for 3-D cases. This paper presents a novel segmentation approach based on Morse function, bringing together the notions of convex components and the Reeb graph of a network. The segmentation is realized by a distributed and scalable algorithm, named CONSEL, for CONnectivity-based SEgmentation in Large-scale 2-D/3-D sensor networks. In CONSEL, several boundary nodes first flood the network to construct the Reeb graph. The ordinary nodes then compute mutex pairs locally, generating a coarse segmentation. Next, neighboring regions that are not mutex pairs are merged together. Finally, by ignoring mutex pairs that lead to small concavity, we provide an approximate convex decomposition. CONSEL has a number of advantages over previous solutions: 1) it works for both 2-D and 3-D sensor networks; 2) it uses merely network connectivity information; 3) it guarantees a bound for the generated regions' deviation from convexity. We further propose to integrate network segmentation with existing applications that are oriented to simple network geometry. Extensive simulations show the efficacy of CONSEL in segmenting networks and in improving the performance of two applications: geographic routing and connectivity-based localization.

In this paper, we put forward a novel scalable and distributed routing algorithm, called SINUS, for sensor networks deployed on the surface of complex-connected 3D settings such as tunnels, whose topologies are often theoretically modeled as high genus 3D surfaces. SINUS is carried out by first slicing the genus-n surface along a maximum cut set based on Morse theory and Reeb graph, in order to form a genus-0 surface with 2n boundaries. Then, it groups these 2n boundaries into two groups each of which is next connected together. By doing so, a genus-0 surface with exactly two boundaries emerges, which can be flattened into a strip, using the Ricci flow algorithm and next mapped to a planar annulus by Möbius Transform. By assigning nodes virtual coordinates on the planar annulus, SINUS finally realizes a variation of greedy routing to enable individual nodes to make local muting decisions. Our simulation results show that SINUS can achieve low-stretch routing with guaranteed delivery, as well as balanced traffic load.

A cardinal prerequisite for the system design of a sensor network, is to understand the geometric environment where sensor nodes are deployed. The global topology of a large-scale sensor network is often complex and irregular, possibly containing obstacles/holes. A convex network partition, so-called segmentation, is to divide a network into convex regions, such that traditional algorithms designed for a simple geometric region can be applied. Existing segmentation algorithms highly depend on concave node detection on the boundary or sink extraction on the medial axis, thus leading to quite sensitive performance to the boundary noise. More severely, since they exploit the network's 2D geometric properties, either explicitly or implicitly, so far there has been no general 3D segmentation solution. In this paper, we bring a new view to segmentation from a Morse function perspective, bridging the convex regions and the Reeb graph of a network. Accordingly, we propose a novel distributed and scalable algorithm, named CONSEL, for CONnectivity-based SEgmentation in Large-scale 2D/3D sensor networks. Specifically, several boundary nodes first perform flooding to construct the Reeb graph. The ordinary nodes then compute mutex pairs locally, thereby generating the coarse segmentation. Next the neighbor regions which are not mutex pair are merged together. Finally, by ignoring mutex pairs which leads to small concavity, we provide the constraints for approximately convex decomposition. CONSEL is more desirable compared with previous studies: (1) it works for both 2D and 3D sensor networks; (2) it only relies on network connectivity information; (3) it guarantees a bound for the regions' deviation from convexity. Extensive simulations show that CONSEL works well in the presence of holes and shape variation, always yielding appropriate segmentation results.

User-Generated Content (UGC) is overwhelming the Internet with its interactivity and various contents. However, traditional UGC still have constrains on videos' length and size, which block out a wide variety of potential popular contents. In this paper, we present the first experimental measurements and analysis of an Unconstrained User-Generated Content (UUGC) system - a test site (so-called "T" site in this paper) of a leading VOD service provider in China. This test site is a video-sharing portal just like traditional UGC, while its contents are not constrained by either duration or size. As an UUGC system, its most distinguishing characteristics are the various types of contents uploaded (movie, TV episode, TV show, music, documentary, sports, etc.) and the wide range of uploaders, which make it an interesting case study. By matching relative key words in video's index, we classify the contents into several basic types and analyze the statistics of three major types - movie, TV episode and TV show (labeled MVI, TV-E and TV-S). For further study of various contents, we demonstrate the patterns of flash crowd triggering of MVI, TV-E and TV-S with several typical cases. To find out the viewers' consumption pattern, we investigate daily & weekly cycles, as well as grouping the videos by age and exhibiting the popularity evolution. By means of curve fitting with multiple known distributions to video view traces, we show that power law with exponential cutoff best fits the videos' popularity distribution for this UUGC system.