The rise of IoT devices brings a lot of security risks. To mitigate them, researchers have introduced various promising network-based anomaly detection algorithms, which oftentimes leverage machine learning. Unfortunately, though, their deployment and further improvement by network operators and the research community are hampered. We believe this is due to three key reasons. First, known ML-based anomaly detection algorithms are evaluated -in the best case- on a couple of publicly available datasets, making it hard to compare across algorithms. Second, each ML-based IoT anomaly-detection algorithm makes assumptions about attacker practices/classification granularity, which reduce their applicability. Finally, the implementation of those algorithms is often monolithic, prohibiting code reuse. To ease deployment and promote research in this area, we present Lumen. Lumen is a modular framework paired with a benchmarking suite that allows users to efficiently develop, evaluate, and compare IoT ML-based anomaly detection algorithms. We demonstrate the utility of Lumen by implementing state-of-the-art anomaly detection algorithms and faithfully evaluating them on various datasets. Among other interesting insights that could inform real-world deployments and future research, using Lumen, we were able to identify what algorithms are most suitable to detect particular types of attacks. Lumen can also be used to construct new algorithms with better performance by combining the building blocks of competing efforts and improving the training setup.

One of the major challenges faced by Augmented Reality (AR) systems is linking virtual content accurately on physical objects and locations. This problem is amplified for applications like mobile payment, device control or secure pairing that requires authentication. In this paper, we present an active LED tag system called GLITTER that uses a combination of Bluetooth Low-Energy (BLE) and modulated LEDs to anchor AR content with no a priori training or labeling of an environment. Unlike traditional optical markers that encode data spatially, each active optical marker encodes a tag’s identifier by blinking over time, improving both the tag density and range compared to AR tags and QR codes.We show that with a low-power BLE-enabled micro-controller and a single 5 mm LED, we are able to accurately link AR content from potentially hundreds of tags simultaneously on a standard mobile phone from as far as 30 meters. Expanding upon this, using active optical markers as a primitive, we show how a constellation of active optical markers can be used for full 3D pose estimation, which is required for many AR applications, using either a single LED on a planar surface or two or more arbitrarily positioned LEDs. Our design supports 108 unique codes in a single field of view with a detection latency of less than 400 ms even when held by hand.

At the core of Network Functions Virtualization lie Network Functions (NFs) that run co-resident on the same server, contend over its hardware resources and, thus, might suffer from reduced performance relative to running alone on the same hardware. Therefore, to efficiently manage resources and meet performance SLAs, NFV orchestrators need mechanisms to predict contention-induced performance degradation. In this work, we find that prior performance prediction frameworks suffer from poor accuracy on modern architectures and NFs because they treat memory as a monolithic whole. In addition, we show that, in practice, there exist multiple components of the memory subsystem that can separately induce contention. By precisely characterizing (1) the pressure each NF applies on the server's shared hardware resources (contentiousness) and (2) how susceptible each NF is to performance drop due to competing contentiousness (sensitivity), we develop SLOMO, a multivariable performance prediction framework for Network Functions. We show that relative to prior work SLOMO reduces prediction error by 2-5x and enables 6-14% more efficient cluster utilization. SLOMO's codebase can be found at https://github.com/cmu-snap/SLOMO.

In this paper, we propose a fully automatic method to register football broadcast video frames on the static top view model of the playing surface. Automatic registration has been difficult due to the difficulty of finding sufficient point correspondences. We investigate an alternate approach exploiting the edge information from the line markings on the field. We formulate the registration problem as a nearest neighbour search over a synthetically generated dictionary of edge map and homography pairs. The synthetic dictionary generation allows us to exhaustively cover a wide variety of camera angles and positions and reduces this problem to a minimal per-frame edge map matching problem. We show that the per-frame results can be further improved in videos using an optimization framework for temporal camera stabilization. We demonstrate the efficacy of our approach by presenting extensive results on a dataset collected from matches of the football World Cup 2014 and show significant improvement over the current state of the art.