Generative models have demonstrated significant success in anomaly detection and segmentation over the past decade. Recently, diffusion models have emerged as a strong alternative, outperforming previous approaches such as GANs and VAEs. In typical diffusion-based anomaly detection, a model is trained on normal data, and during inference, anomalous images are perturbed to a predefined intermediate step in the forward diffusion process. The corresponding normal image is then reconstructed through iterative reverse sampling.However, reconstruction-based approaches present three major challenges: (1) the reconstruction process is computationally expensive due to multiple sampling steps, making real-time applications impractical; (2) for complex or subtle patterns, the reconstructed image may correspond to a different normal pattern rather than the original input; and (3) choosing an appropriate intermediate noise level is challenging because it is application-dependent and often assumes prior knowledge of anomalies, an assumption that does not hold in unsupervised settings.We introduce Reconstruction-free Anomaly Detection with Attention-based diffusion models in Real-time (RADAR), which overcomes the limitations of reconstruction-based anomaly detection. Unlike current state-of-the-art (SOTA) methods that reconstruct the input image, RADAR directly produces anomaly maps from the diffusion model, improving both detection accuracy and computational efficiency. We evaluate RADAR on real-world 3D-printed material and the MVTec-AD dataset. Our approach surpasses state-of-the-art diffusion-based and statistical machine learning models across all key metrics, including accuracy, precision, recall, and F1 score. Specifically, RADAR improves F1 score by 7% on MVTec-AD and 13% on the 3D-printed material dataset compared to the next best model.Code: https://github.com/mehrdadmoradi124/RADAR

Simulation-based analyses of Cyber-Physical Systems are fundamental in industrial design and testing approaches. The utility of analyses relies on the correct configuration of the simulation tools, which can be highly complicated. System engineers can normally judge the results, and either evaluate multiple simulation algorithms, or change the models. However, this is not possible in a co-simulation approach. Co-simulation is a technique to perform full-system simulation, by combining multiple black-box simulators, each responsible for a part of the system. In this paper, we demonstrate the difficulty of correctly configuring a co-simulation scenario using an industrial case study. We propose an approach to tackle this challenge by allowing multiple engineers, specialized in different domains, to encode some of their experience in the form of hints. These hints, together with state-of-the-art best practices, are then used to semi-automatically guide the configuration process of the co-simulation. We report the application of this approach to a use case proposed by our industrial partners, and discuss some of the lessons learned.

The advances in unmanned aerial vehicle (UAV) technology have empowered mobile operators to deploy LTE base stations (BSs) on UAVs, and provide on-demand, adaptive connectivity to hotspot venues as well as emergency scenarios. However, today's evolved packet core (EPC) that orchestrates the LTE RAN faces fundamental limitations in catering to such a challenging, wireless and mobile UAV environment, particularly in the presence of multiple BSs (UAVs). In this work, we argue for and propose an alternate, radical edge EPC design, called SkyCore that pushes the EPC functionality to the extreme edge of the core network - collapses the EPC into a single, light-weight, self-contained entity that is co-located with each of the UAV BS. SkyCore incorporates elements that are designed to address the unique challenges facing such a distributed design in the UAV environment, namely the resource-constraints of UAV platforms, and the distributed management of pronounced UAV and UE mobility. We build and deploy a fully functional version of SkyCore on a two-UAV LTE network and showcase its (i) ability to interoperate with commercial LTE BSs as well as smartphones, (ii) support for both hotspot and standalone multi-UAV deployments, and (iii) superior control and data plane performance compared to other EPC variants in this environment.

We propose a novel cellular core network architecture, SoftBox, combining software-defined networking and network function virtualization to achieve greater flexibility, efficiency, and scalability compared to today's cellular core. Aligned with 5G use cases, SoftBox enables the creation of customized, low latency, and signaling-efficient services on a per user equipment (UE) basis. SoftBox consolidates network policies needed for processing each UE's data and signaling traffic into a light-weight, in-network, and per-UE agent. We design a number of mobility-aware techniques to further optimize: 1) resource usage of agents; 2) forwarding rules and updates needed for steering a UE's traffic through its agent; 3) migration costs of agents needed to ensure their proximity to mobile UEs; and 4) complexity of distributing the LTE mobility function on agents. Extensive evaluations demonstrate the scalability, performance, and flexibility of the SoftBox design. For example, basic SoftBox has 86%, 51%, and 87% lower signaling overheads, data plane delay, and CPU core usage, respectively, than two open source EPC systems. Moreover, our optimizations efficiently cut different types of data and control plane loads in the basic SoftBox by 51%-98%.

To optimize network cost, performance, and reliability, SDN advocates for centralized traffic engineering (TE) that enables more efficient path and egress point selection as well as bandwidth allocation. In this paper, we argue SDN-based TE for ISP networks can be very challenging. First, ISP networks often are very large in size, imposing significant scalability challenges to the centralized TE. Second, ISP networks usually have diverse types of links, switches, and cost models, leading to a complex combination of optimizations. Third, ISP networks not only have many choices of internal paths but also include rich selections of egress points and interdomain routes, unlike cloud/enterprise networks. To overcome these challenges, we present a novel TE application framework, called Dragon, for existing SDN control planes. To address the scalability challenge, Dragon consists of hierarchical and recursive TE algorithms and mechanisms that divide flow optimization problems into subtasks and execute them in parallel. Further, Dragon allows ISPs to express diverse objectives for different parts of their network. Finally, we extend Dragon to jointly optimize the selection of intradomain and interdomain paths. Using extensive evaluation on real topologies and prototyping with SDN controller and switches, we demonstrate that Dragon outperforms existing TE methods both in speed and optimality.

In response to the critical challenges of the current Internet architecture and its protocols, a set of so-called clean slate designs has been proposed. Common among them is an addressing scheme that separates location and identity with self-certifying, flat and non-aggregatable address components. Each component is long, reaching a few kilobits, and would consume an amount of fast memory in data plane devices (e.g., routers) that is far beyond existing capacities. To address this challenge, we present Caesar, a high-speed and length-agnostic forwarding engine for future border routers, performing most of the lookups within three fast memory accesses. To compress forwarding states, Caesar constructs scalable and reliable Bloom filters in Ternary Content Addressable Memory (TCAM). To guarantee correctness, Caesar detects false positives at high speed and develops a blacklisting approach to handling them. In addition, we optimize our design by introducing a hashing scheme that reduces the number of hash computations from k to log(k) per lookup based on hash coding theory. We handle routing updates while keeping filters highly utilized in address removals. We perform extensive analysis and simulations using real traffic and routing traces to demonstrate the benefits of our design. Our evaluation shows that Caesar is more energy-efficient and less expensive (in terms of total cost) compared to optimized IPv6 TCAM-based solutions by up to 67% and 43% respectively. In addition, the total cost of our design is approximately the same for various address lengths.

The current LTE network architecture is organized into very large regions, each having a core network and a radio access network. The core network contains an Internet edge comprised of packet data network gateways (PGWs). The radio network consists of only base stations. There are minimal interactions among regions other than interference management at the edge. The current architecture has several problems. First, mobile application performance is seriously impacted by the lack of Internet egress points per region. Second, the continued exponential growth of mobile traffic puts tremendous pressure on the scalability of PGWs. Third, the fast growth of signaling traffic known as the signaling storm problem poses a major challenge to the scalability of the control plane. To address these problems, we present SoftMoW, a recursive and reconfigurable cellular WAN architecture that supports seamlessly inter-connected core networks, reconfigurable control plane, and global optimization.