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1. Systematic planning of moving target defence for maximising detection effectiveness against false data injection attacks in smart grid

   https://doi.org/10.1049/cps2.12012  

   Liu, Bo; Wu, Hongyu (May 2021, IET Cyber-Physical Systems: Theory & Applications)

   Abstract Moving target defence (MTD) has been gaining traction to thwart false data injection attacks against state estimation (SE) in the power grid. MTD actively perturbs the reactance of transmission lines equipped with distributed flexible AC transmission system (D‐FACTS) devices to falsify the attacker's knowledge about the system configuration. However, the existing literature has not systematically studied what influences the detection effectiveness of MTD and how it can be improved based on the topology analysis. These problems are tackled here from the perspective of an MTD plan in which the D‐FACTS placement is determined. We first exploit the relation between the rank of the composite matrix and the detecting effectiveness. Then, we rigorously derive upper and lower bounds on the attack detecting probability of MTDs with a given rank of the composite matrix. Furthermore, we analyse existing planning methods and highlight the importance of bus coverage by D‐FACTS devices. To improve the detection effectiveness, we propose a novel graph theory–based planning algorithm to retain the maximum rank of the composite matrix while covering all necessary buses. Comparative results on multiple systems show the high detecting effectiveness of the proposed algorithm in both DC‐ and AC‐SE. 

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2. A Comparative Analysis of the Ensemble Models for Detecting GPS Spoofing attacks on UAVs

   https://doi.org/10.1109/CCWC54503.2022.9720738  

   Gasimova, Aydan; Khoei, Tala Talaei; Kaabouch, Naima (January 2022, IEEE Annual Computing and Communication Workshop and Conference)

   Unmanned Aerial Vehicles have been widely used in military and civilian areas. The positioning and return-to-home tasks of UAVs deliberately depend on Global Positioning Systems (GPS). However, the civilian GPS signals are not encrypted, which can motivate numerous cyber-attacks on UAVs, including Global Positioning System spoofing attacks. In these spoofing attacks, a malicious user transmits counterfeit GPS signals. Numerous studies have proposed techniques to detect these attacks. However, these techniques have some limitations, including low probability of detection, high probability of misdetection, and high probability of false alarm. In this paper, we investigate and compare the performances of three ensemble-based machine learning techniques, namely bagging, stacking, and boosting, in detecting GPS attacks. The evaluation metrics are the accuracy, probability of detection, probability of misdetection, probability of false alarm, memory size, processing time, and prediction time per sample. The results show that the stacking model has the best performance compared to the two other ensemble models in terms of all the considered evaluation metrics. 

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3. 2dFDR: a new approach to confounder adjustment substantially increases detection power in omics association studies

   https://doi.org/10.1186/s13059-021-02418-8  

   Yi, Sangyoon; Zhang, Xianyang; Yang, Lu; Huang, Jinyan; Liu, Yuanhang; Wang, Chen; Schaid, Daniel_J; Chen, Jun (July 2021, Genome Biology)

   Abstract One challenge facing omics association studies is the loss of statistical power when adjusting for confounders and multiple testing. The traditional statistical procedure involves fitting a confounder-adjusted regression model for each omics feature, followed by multiple testing correction. Here we show that the traditional procedure is not optimal and present a new approach, 2dFDR, a two-dimensional false discovery rate control procedure, for powerful confounder adjustment in multiple testing. Through extensive evaluation, we demonstrate that 2dFDR is more powerful than the traditional procedure, and in the presence of strong confounding and weak signals, the power improvement could be more than 100%. 

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4. Dynamic Selection Techniques for Detecting GPS Spoofing Attacks on UAVs

   https://doi.org/10.3390/s22020662  

   Tala Talaei Khoei, Shereen Ismail (January 2022, Sensors)

   Unmanned aerial vehicles are prone to several cyber-attacks, including Global Positioning System spoofing. Several techniques have been proposed for detecting such attacks. However, the recurrence and frequent Global Positioning System spoofing incidents show a need for effective security solutions to protect unmanned aerial vehicles. In this paper, we propose two dynamic selection techniques, Metric Optimized Dynamic selector and Weighted Metric Optimized Dynamic selector, which identify the most effective classifier for the detection of such attacks. We develop a one-stage ensemble feature selection method to identify and discard the correlated and low importance features from the dataset. We implement the proposed techniques using ten machine-learning models and compare their performance in terms of four evaluation metrics: accuracy, probability of detection, probability of false alarm, probability of misdetection, and processing time. The proposed techniques dynamically choose the classifier with the best results for detecting attacks. The results indicate that the proposed dynamic techniques outperform the existing ensemble models with an accuracy of 99.6%, a probability of detection of 98.9%, a probability of false alarm of 1.56%, a probability of misdetection of 1.09%, and a processing time of 1.24 s. 

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5. A Backward Procedure for Change-point Detection with Application to Copy Number Variation Detection.

   https://doi.org/doi.org/10.1002/cjs.11535  

   Shin, S.J.; Wu, Y; Hao, N. (September 2020, The Canadian journal of statistics)

   Yao, Fang (Ed.)

   Change-point detection regains much attention recently for analyzing array or sequencing data for copy number variation (CNV) detection. In such applications, the true signals are typically very short and buried in the long data sequence, which makes it challenging to identify the variations efficiently and accurately. In this article, we propose a new change-point detection method, a backward procedure, which is not only fast and simple enough to exploit high-dimensional data but also performs very well for detecting short signals. Although motivated by CNV detection, the backward procedure is generally applicable to assorted change-point problems that arise in a variety of scientific applications. It is illustrated by both simulated and real CNV data that the backward detection has clear advantages over other competing methods, especially when the true signal is short. 

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