Scaling network-based spectrum analyzer with constant communication cost

e propose a spectrum analyzer that leverages many networked commodity sensor nodes, each of which sam- ples its portion in a wideband spectrum. The sensors operate in parallel and transmit their measurements over a wireless network without performing any significant computations such as FFT. The measurements are forwarded to the backend of the system where spectrum analysis takes place. In particular, we propose a solution that compresses the raw measurements in a simple random linear projection and combines the compressed measurements from multiple sensors in-network. As a result, we achieve a substantial reduction in the network bandwidth requirement to operate the proposed system. We discover that the overall communication cost can be independent of the number of sensors and is affected only by sparsity of discretized spectrum under analysis. This principle founds the basis for a claim that our network-based spectrum analyzer can scale up the number of sensor nodes to process a very wide spectrum block potentially having a GHz bandwidth. We devise a novel recovery algorithm that systematically undoes compressive encoding and in-network combining done to the raw measurements, incorporating the least squares and l1-minimization decoding used in compressive sensing, and demonstrate that the algorithm can effectively restore an accurate estimate of the original data suitable for fine- rained spectrum analysis. We present mathematical analysis and empirical evaluation of the system with software-defined radios.Engineering and Applied Science

Dual Correction Strategy for Ranking Distillation in Top-N Recommender System

Knowledge Distillation (KD), which transfers the knowledge of a well-trained large model (teacher) to a small model (student), has become an important area of research for practical deployment of recommender systems. Recently, Relaxed Ranking Distillation (RRD) has shown that distilling the ranking information in the recommendation list significantly improves the performance. However, the method still has limitations in that 1) it does not fully utilize the prediction errors of the student model, which makes the training not fully efficient, and 2) it only distills the user-side ranking information, which provides an insufficient view under the sparse implicit feedback. This paper presents Dual Correction strategy for Distillation (DCD), which transfers the ranking information from the teacher model to the student model in a more efficient manner. Most importantly, DCD uses the discrepancy between the teacher model and the student model predictions to decide which knowledge to be distilled. By doing so, DCD essentially provides the learning guidance tailored to "correcting" what the student model has failed to accurately predict. This process is applied for transferring the ranking information from the user-side as well as the item-side to address sparse implicit user feedback. Our experiments show that the proposed method outperforms the state-of-the-art baselines, and ablation studies validate the effectiveness of each component

DISTROY: Detecting Integrated Circuit Trojans with Compressive Measurements

Detecting Trojans in an integrated circuit (IC) is an important but hard problem. A Trojan is malicious hardware it can be extremely small in size and dormant until triggered by some unknown circuit state. To allow wake-up, a Trojan could draw a minimal amount of power, for example, to run a clock or a state machine, or to monitor a triggering event. We introduce DISTROY (Discover Trojan), a new approach that can effciently and reliably detect extremely small background power leakage that a Trojan creates and as a result, we can detect the Trojan. We formulate our method based on compressive sensing, a recent advance in signal processing, which can recover a signal using the number of measurements approximately proportional to its sparsity rather than size. We argue that circuit states in which the Trojan background power consumption stands out are rare, and thus sparse, so that we can apply compressive sensing. We describe how this is done in DISTROY so as to afford suffcient measurement statistics to detect the presence of Trojans. Finally, we present our initial simulation results that validate DISTROY and discuss the impact of our work in the field of hardware security.Engineering and Applied Science

Compressive Sensing with Optimal Sparsifying Basis and Applications in Spectrum Sensing

We describe a method of integrating Karhunen-Loève Transform (KLT) into compressive sensing, which can as a result improve the compression ratio without affecting the accuracy of decoding. We present two complementary results: 1) by using KLT to find an optimal basis for decoding we can drastically reduce the number of measurements for compressive sensing used in applications such as radio spectrum analysis; 2) by using compressive sensing we can estimate and recover the KLT basis from compressive measurements of an input signal. In particular, we propose CS-KLT, an online estimation algorithm to cope with nonstationarity of wireless channels in reality. We validate our results with empirical data collected from a wideband UHF spectrum and eld experiments to detect multiple radio transmitters, using software-defined radios.Engineering and Applied Science

Is Cross-modal Information Retrieval Possible without Training?

Encoded representations from a pretrained deep learning model (e.g., BERT text embeddings, penultimate CNN layer activations of an image) convey a rich set of features beneficial for information retrieval. Embeddings for a particular modality of data occupy a high-dimensional space of its own, but it can be semantically aligned to another by a simple mapping without training a deep neural net. In this paper, we take a simple mapping computed from the least squares and singular value decomposition (SVD) for a solution to the Procrustes problem to serve a means to cross-modal information retrieval. That is, given information in one modality such as text, the mapping helps us locate a semantically equivalent data item in another modality such as image. Using off-the-shelf pretrained deep learning models, we have experimented the aforementioned simple cross-modal mappings in tasks of text-to-image and image-to-text retrieval. Despite simplicity, our mappings perform reasonably well reaching the highest accuracy of 77% on recall@10, which is comparable to those requiring costly neural net training and fine-tuning. We have improved the simple mappings by contrastive learning on the pretrained models. Contrastive learning can be thought as properly biasing the pretrained encoders to enhance the cross-modal mapping quality. We have further improved the performance by multilayer perceptron with gating (gMLP), a simple neural architecture

Optimizing Media Access Strategy for Competing Cognitive Radio Networks

This paper describes an adaptation of cognitive radio technology for tactical wireless networking. We introduce Competing Cognitive Radio Network (CCRN) featuring both communicator and jamming cognitive radio nodes that strategize in taking actions on an open spectrum under the presence of adversarial threats. We present the problem in the Multi-armed Bandit (MAB) framework and develop the optimal media access strategy consisting of mixed communicator and jammer actions in a Bayesian setting for Thompson sampling based on extreme value theory. Empirical results are promising that the proposed strategy seems to outperform Lai & Robbins and UCB, some of the most important MAB algorithms known to date.Engineering and Applied Science

MvFS: Multi-view Feature Selection for Recommender System

Feature selection, which is a technique to select key features in recommender systems, has received increasing research attention. Recently, Adaptive Feature Selection (AdaFS) has shown remarkable performance by adaptively selecting features for each data instance, considering that the importance of a given feature field can vary significantly across data. However, this method still has limitations in that its selection process could be easily biased to major features that frequently occur. To address these problems, we propose Multi-view Feature Selection (MvFS), which selects informative features for each instance more effectively. Most importantly, MvFS employs a multi-view network consisting of multiple sub-networks, each of which learns to measure the feature importance of a part of data with different feature patterns. By doing so, MvFS mitigates the bias problem towards dominant patterns and promotes a more balanced feature selection process. Moreover, MvFS adopts an effective importance score modeling strategy which is applied independently to each field without incurring dependency among features. Experimental results on real-world datasets demonstrate the effectiveness of MvFS compared to state-of-the-art baselines.Comment: CIKM 202

Competing Mobile Network Game: Embracing antijamming and jamming strategies with reinforcement learning

We introduce Competing Mobile Network Game (CMNG), a stochastic game played by cognitive radio networks that compete for dominating an open spectrum access. Differentiated from existing approaches, we incorporate both communicator and jamming nodes to form a network for friendly coalition, integrate antijamming and jamming subgames into a stochastic framework, and apply Q-learning techniques to solve for an optimal channel access strategy. We empirically evaluate our Q-learning based strategies and find that Minimax-Q learning is more suitable for an aggressive environment than Nash-Q while Friend-or-foe Q-learning can provide the best solution under distributed mobile ad hoc networking scenarios in which the centralized control can hardly be available.Engineering and Applied Science

Inferring Origin Flow Patterns in Wi-Fi with Deep Learning

We present a novel application of deep learning in networking. The envisioned system can learn the original flow characteristics such as a burst size and inter-burst gaps conceived at the source from packet sampling done at a receiverWi-Fi node. This problem is challenging because CSMA introduces complex, irregular alterations to the origin pattern of the flow in the presence of competing flows. Our approach is semi-supervised learning. We first work through multiple layers of feature extraction and subsampling from unlabeled flow measurements.We use a feature extractor based on sparse coding and dictionary learning, and our subsampler performs overlapping max pooling. Given the layers of learned feature mapping, we train SVM classifiers with deep feature representation resulted at the top layer. The proposed scheme has been evaluated empirically in a custom wireless simulator and OPNET. The results are promising that we achieve superior classification performance over ARMAX, Naïve Bayes classifiers, and Gaussian mixture models optimized by the EM algorithm.Engineering and Applied Science

Statistical screening for IC Trojan detection

We present statistical screening of test vectors for detecting a Trojan, malicious circuitry hidden inside an integrated circuit (IC). When applied a test vector, a Trojan-embedded chip draws extra leakage current that is unfortunately too small for the detector in most cases and concealed by process variation related to chip fabrication. To remedy the problem, we formulate a statistical approach that can screen and select test vectors in detecting Trojans. We validate our approach analytically and with gate-level simulations and show that our screening method leads to a substantial reduction in false positives and false negatives when detecting IC Trojans of various sizes.Engineering and Applied Science