UpCycling: Semi-supervised 3D Object Detection without Sharing Raw-level Unlabeled Scenes

Semi-supervised Learning (SSL) has received increasing attention in autonomous driving to relieve enormous burden for 3D annotation. In this paper, we propose UpCycling, a novel SSL framework for 3D object detection with zero additional raw-level point cloud: learning from unlabeled de-identified intermediate features (i.e., smashed data) for privacy preservation. The intermediate features do not require additional computation on autonomous vehicles since they are naturally produced by the inference pipeline. However, augmenting 3D scenes at a feature level turns out to be a critical issue: applying the augmentation methods in the latest semi-supervised 3D object detectors distorts intermediate features, which causes the pseudo-labels to suffer from significant noise. To solve the distortion problem while achieving highly effective SSL, we introduce hybrid pseudo labels, feature-level Ground Truth sampling (F-GT) and Rotation (F-RoT), which safely augment unlabeled multi-type 3D scene features and provide high-quality supervision. We implement UpCycling on two representative 3D object detection models, SECOND-IoU and PV-RCNN, and perform experiments on widely-used datasets (Waymo, KITTI, and Lyft). While preserving privacy with zero raw-point scene, UpCycling significantly outperforms the state-of-the-art SSL methods that utilize raw-point scenes, in both domain adaptation and partial-label scenarios

FDF: Frequency Detection-Based Filtering of Scanning Worms

Abstract — In this paper, we propose a simple algorithm for detecting scanning worms with high detection rate and low false positive rate. The novelty of our algorithm is inspecting the frequency characteristic of scanning worms from a monitored network. Its low complexity allows it to be used on any network-based intrusion detection system as a real time detection module for high-speed networks. Our algorithm need not be adjusted to network status because its parameters depend on application types, which are generally and widely used in any networks such as web and P2P services. By using real traces, we evaluate the performance of our algorithm and compare it with that of SNORT. The results confirm that our algorithm outperforms SNORT with respect to detection rate and false positive rate. I

A decentralized spectrum allocation and partitioning scheme for a two-tier macro-femtocell network with downlink beamforming

This article examines spectrum allocation and partitioning schemes to mitigate cross-tier interference under downlink beamforming environments. The enhanced SIR owing to beamforming allows more femtocells to share their spectrum with the macrocell and accordingly improves overall spectrum efficiency. We first design a simplified centralized scheme as the optimum and then propose a practical decentralized algorithm that determines which femtocells to use the full or partitioned spectrum with acceptable control overhead. To exploit limited information of the received signal strength efficiently, we consider two types of probabilistic femtocell base station (HeNB) selection policies. They are equal selection and interference weighted selection policies, and we drive their outage probabilities for a macrocell user. Through performance evaluation, we demonstrate that the outage probability and the cell capacity in our decentralized scheme are significantly better than those in a conventional cochannel deployment scheme. Furthermore, we show that the cell utility in our proposed scheme is close to that in the centralized scheme and better than that in the spectrum partitioning scheme with a fixed ratio.open0

CRUI: Collision Reduction and Utilization Improvement in OFDMA-based 802.11ax Networks

The number of IEEE 802.11 hotspots is increasing due to popularity and low price, resulting in a dense deployment of wireless local area networks (WLANs). To increase per station (STA) throughput in a densely deployed environment, a new amendment to the WLAN standard, namely, IEEE 802.11ax introduces orthogonal frequency-division multiple access (OFDMA). Especially, uplink OFDMA-based random access (UORA) enables multiple STAs to have simultaneous random access (RA) to an access point (AP) by using different subchannels. However, due to the nature of RA, UORA suffers from data collisions with the number of contending STAs. Moreover, the bandwidth underutilization problem degrades performance of UORA. In this paper, we propose a scheme, named CRUI, that substantially reduces data collisions and improves bandwidth utilization by using an extra backoff stage and opportunistic subchannel hopping. Through simulation, we demonstrate that our proposed scheme significantly improves UORA performance with lowered collision probability and improved bandwidth utilization while maintaining fairness.N

LoS/NLoS Detection based Authentication for IoT Systems

The number of IoT devices is increasing significantly these days, especially in private spaces. The data collected in personal space may include private information. Because a lot of IoT devices use wireless channels to communicate with other devices, illegal devices (attackers) can easily access the channel. The attacker can access IoT devices and pretend to be legitimate devices. This paper proposes a novel LoS/NLoS detection based authentication scheme that aims to guarantee security while providing proper user convenience. We propose a new metric called Gap Difference of Phase (GDP) that helps to differentiate between LoS and NLoS channels. We evaluate the performance of the GDP-based LoS/NLoS detection method that also uses Support Vector Machine (SVM). As a result we confirm that our proposed method successfully differentiates between LoS/NLoS channels and achieves a balance between a high level of security and user convenience.N

Fast Classification, Calibration, and Visualization of Network Attacks on Backbone Links

Abstract. This paper presents a novel approach that can simultane-ously detect, classify, calibrate and visualize attack traffic at high speed, in real time. In particular, upon a packet arrival, this approach makes it possible to immediately determine if the packet constitutes an attack and if so, what type of attack it is. In this approach, a flow is defined by a 3-tuple, composed of source address, destination address, and des-tination port. The core idea starts from the observation that only DoS attack, hostscan and portscan appear as a regular geometric shape in the hyperspace defined by the 3-tuple. Instead of employing complex pattern recognition techniques to identify the regular shapes in the hy-perspace, we apply an original algorithm called RADAR that captures the ”pivoted movement ” in one or more of the 3 coordinates. From the geometric perspective, such movement forms the aforementioned regular pattern along the axis of the pivoted dimension. Through real execution on a Gigabit link, we demonstrate that the algorithm is both fast and precise. Since we need only 3 to 4 memory lookups per packet to de-tect and classify an attack packet, while simultaneously running 2 copies of the algorithm on a Pentium-4 PC, the algorithm incurred no packet loss over 330Mbps live traffic. Memory requirement is also low- at most 200MB of memory suffices even for Gigabit pipes. Finally, the method is general enough to detect both DoS’s and scans, but the focus of the paper is on its capability to identify the latter on backbone links, in the light of recent global worm epidemics.

BeaconRider: Opportunistic Sharing of Beacon Air-Time in Densely Deployed WLANs

The explosion of mobile traffic volume has led to dense deployment of IEEE 802.11 WLANs. As a consequence, periodic beacon transmissions can overwhelm the air-time, leading to significant air-time depletion for data transmissions. In this work, we develop an opportunistic air-time sharing scheme, named BeaconRider, that facilitates simultaneous data and beacon transmissions aimed at improving spectrum efficiency in dense network environments. The proposed method works for downlink communication and allows access points (APs) to coordinate with each other in a distributed manner to exploit opportunities provided by the capture effect. Our protocol is backward compatible with legacy 802.11 APs. Through experiments with a prototype implementation using off-the-shelf IEEE 802.11n dongles as well as extensive ns-3 simulation, we show that the proposed method achieves substantial performance gains that increase with the number of APs.N