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Magnetoresistive (MR) sensors have been identified as promising candidates for the development of high-performance magnetometers due to their high sensitivity, low cost, low power consumption, and small size. The rapid advance of MR sensor technology has opened up a variety of MR sensor applications. These applications are in different areas that require MR sensors with different properties. Future MR sensor development in each of these areas requires an overview and a strategic guide. A MR sensor roadmap (non-recording applications) was therefore developed and made public by the Technical Committee of The IEEE Magnetics Society with the aim to provide an R&D guide for MR sensors intended to be used by industry, government, and academia. The roadmap was developed over a three-year period and coordinated by an international effort of 22 taskforce members from 10 countries and 17 organizations, including universities, research institutes, and sensor companies. In this paper, the current status of MR sensors for non-recording

How can we detect fraudulent lockstep behavior in large-scale multi-aspect data (i.e., tensors)? Can we detect it when data are too large to fit in memory or even on a disk? Past studies have shown that dense subtensors in real-world tensors (e.g., social media, Wikipedia, TCP dumps, etc.) signal anomalous or fraudulent behavior such as retweet boosting, bot activities, and network attacks. Thus, various approaches, including tensor decomposition and search, have been proposed for detecting dense subtensors rapidly and accurately. However, existing methods have low accuracy, or they assume that tensors are small enough to fit in main memory, which is unrealistic in many real-world applications such as social media and web. To overcome these limitations, we propose D-CUBE, a disk-based dense-subtensor detection method, which also can run in a distributed manner across multiple machines. Compared to state-of-the-art methods, D-CUBE is (1) Memory Efficient: requires up to 1,600× less memory and handles 1,000× larger data (2.6TB), (2) Fast: up to 7× faster due to its near-linear scalability, (3) Provably Accurate: gives a guarantee on the densities of the detected subtensors, and (4) Effective: spotted network attacks from TCP dumps and synchronized behavior in rating data most accurately.

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Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory? Existing approaches aim to detect individually surprising edges. In this work, we propose Midas, which focuses on detecting microcluster anomalies, or suddenly arriving groups of suspiciously similar edges, such as lockstep behavior, including denial of service attacks in network traffic data. Midas has the following properties: (a) it detects microcluster anomalies while providing theoretical guarantees about its false positive probability; (b) it is online, thus processing each edge in constant time and constant memory, and also processes the data 108–505 times faster than state-of-the-art approaches; (c) it provides 46%-52% higher accuracy (in terms of AUC) than state-of-the-art approaches.

Given a bipartite graph of users and the products that they review, or followers and followees, how can we detect fake reviews or follows? Existing fraud detection methods (spectral, etc.) try to identify dense subgraphs of nodes that are sparsely connected to the remaining graph. Fraudsters can evade these methods using camouflage , by adding reviews or follows with honest targets so that they look “normal.” Even worse, some fraudsters use hijacked accounts from honest users, and then the camouflage is indeed organic. Our focus is to spot fraudsters in the presence of camouflage or hijacked accounts. We propose FRAUDAR, an algorithm that (a) is camouflage resistant, (b) provides upper bounds on the effectiveness of fraudsters, and (c) is effective in real-world data. Experimental results under various attacks show that FRAUDAR outperforms the top competitor in accuracy of detecting both camouflaged and non-camouflaged fraud. Additionally, in real-world experiments with a Twitter follower--followee graph of 1.47 billion edges, FRAUDAR successfully detected a subgraph of more than 4, 000 detected accounts, of which a majority had tweets showing that they used follower-buying services.