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1. A Feature Selection Method Based on Shapley Value to False Alarm Reduction in ICUs A Genetic-Algorithm Approach

   https://doi.org/10.1109/EMBC.2018.8512266  

   Zaeri-Amirani, Mohammad ; Afghah, Fatemeh ; Mousavi, Sajad ( July 2018 , A Feature Selection Method Based on Shapley Value to False Alarm Reduction in ICUs A Genetic-Algorithm Approach)

   High false alarm rate in intensive care units (ICUs) has been identified as one of the most critical medical challenges in recent years. This often results in overwhelming the clinical staff by numerous false or unurgent alarms and decreasing the quality of care through enhancing the probability of missing true alarms as well as causing delirium, stress, sleep deprivation and depressed immune systems for patients. One major cause of false alarms in clinical practice is that the collected signals from different devices are processed individually to trigger an alarm, while there exists a considerable chance that the signal collected from one device is corrupted by noise or motion artifacts. In this paper, we propose a low-computational complexity yet accurate game-theoretic feature selection method which is based on a genetic algorithm that identifies the most informative biomarkers across the signals collected from various monitoring devices and can considerably reduce the rate of false alarms. 

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2. Meteor Detection With a New Computer Vision Approach

   https://doi.org/10.1029/2022RS007515  

   Li, Yanlin ; Galindo, Freddy ; Urbina, Julio ; Zhou, Qihou ; Huang, Tai‐Yin ( October 2022 , Radio Science)

   A novel computer vision‐based meteor head echo detection algorithm is developed to study meteor fluxes and their physical properties, including initial range, range coverage, and radial velocity. The proposed Algorithm for Head Echo Automatic Detection (AHEAD) comprises a feature extraction function and a Convolutional Neural Network (CNN). The former is tailored to identify meteor head echoes, and then a CNN is employed to remove false alarms. In the testing of meteor data collected with the Jicamarca 50 MHz incoherent scatter radar, the new algorithm detects over 180 meteors per minute at dawn, which is 2 to 10 times more sensitive than prior manual or algorithmic approaches, with a false alarm rate less than 1 percent. The present work lays the foundation of developing a fully automatic AI‐meteor package that detects, analyzes, and distinguishes among many types of meteor echoes. Furthermore, although initially evaluated for meteor data collected with the Jicamarca VHF incoherent radar, the new algorithm is generic enough that can be applied to other facilities with minor modifications. The CNN removes up to 98 percent of false alarms according to the testing set. We also present and discuss the physical characteristics of meteors detected with AHEAD, including flux rate, initial range, line of sight velocity, Signal‐to‐Noise Ratio, and noise characteristics. Our results indicate that stronger meteor echoes are detected at a slightly lower altitude and lower radial velocity than other meteors.

    

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3. Detection of Man-in-the-Middle Attacks in Model-Free Reinforcement Learning

   Rishi Rani, Massimo Franceschetti ( June 2023 , Proceedings of Machine Learning Research)

   Nikolai Matni, Manfred Morari (Ed.)

   This paper proposes a Bellman Deviation algorithm for the detection of man-in-the-middle (MITM) attacks occurring when an agent controls a Markov Decision Process (MDP) system using model-free reinforcement learning. This algorithm is derived by constructing a "Bellman Deviation sequence" and finding stochastic bounds on its running sequence average. We show that an intuitive, necessary and sufficient "informational advantage" condition must be met for the proposed algorithm to guarantee the detection of attacks with high probability, while also avoiding false alarms. 

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4. Self-Supervised Learning for Online Anomaly Detection in High-Dimensional Data Streams

   https://doi.org/10.3390/electronics12091971  

   Mozaffari, Mahsa ; Doshi, Keval ; Yilmaz, Yasin ( May 2023 , Electronics)

   In this paper, we address the problem of detecting and learning anomalies in high-dimensional data-streams in real-time. Following a data-driven approach, we propose an online and multivariate anomaly detection method that is suitable for the timely and accurate detection of anomalies. We propose our method for both semi-supervised and supervised settings. By combining the semi-supervised and supervised algorithms, we present a self-supervised online learning algorithm in which the semi-supervised algorithm trains the supervised algorithm to improve its detection performance over time. The methods are comprehensively analyzed in terms of computational complexity, asymptotic optimality, and false alarm rate. The performances of the proposed algorithms are also evaluated using real-world cybersecurity datasets, that show a significant improvement over the state-of-the-art results. 

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5. Detecting Intruders by User File Access Patterns

   https://doi.org/10.1007/978-3-030-36938-5_19  

   Shou-Hsuan S. Huang, Zechun Cao ( December 2019 , International Conference on Network and System Security)

   Our society is facing a growing threat from data breaches where confidential information is stolen from computer servers. In order to steal data, hackers must first gain entry into the targeted systems. Commercial off-the-shelf intrusion detection systems are unable to defend against the intruders effectively. This research uses cyber behavior analytics to study and report how anomalies compare to normal behavior. In this paper, we present methods based on machine learning algorithms to detect intruders based on the file access patterns within a user file directory. We proposed a set of behavioral features of the user's file access patterns in a file system. We validate the effectiveness of the features by conducting experiments on an existing file system dataset with four classification algorithms. To limit the false alarms, we trained and tested the classifiers by optimizing the performance within the lower range of the false positive rate. The results from our experiments show that our approach was able to detect intruders with a 0.94 Fl score and false positive rate of less than 3%. 

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