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1. A comparison of machine learning and human performance in the real-time acoustic detection of drones

   Alaparthy, V; Mandal, S.; Cummings, M.L. (January 2021, IEEE Aerospace)

   null (Ed.)

   Usage of drones has increased substantially in both recreation and commercial applications and is projected to proliferate in the near future. As this demand rises, the threat they pose to both privacy and safety also increases. Delivering contraband and unauthorized surveillance are new risks that accompany the growth in this technology. Prisons and other commercial settings where venue managers are concerned about public safety need cost-effective detection solutions in light of their increasingly strained budgets. Hence, there arises a need to design a drone detection system that is low cost, easy to maintain, and without the need for expensive real-time human monitoring and supervision. To this end, this paper presents a low-cost drone detection system, which employs a Convolutional Neural Network (CNN) algorithm, making use of acoustic features. The Mel Frequency Cepstral Coefficients (MFCC) derived from audio signatures are fed as features to the CNN, which then predicts the presence of a drone. We compare field test results with an earlier Support Vector Machine (SVM) detection algorithm. Using the CNN yielded a decrease in the false positives and an increase in the correct detection rate.Previous tests showed that the SVM was particularly susceptible to false alarms for lawn equipment and helicopters, which were significantly improved when using the CNN. Also,in order to determine how well such a system compared to human performance and also explore including the end-user in the detection loop, a human performance experiment was conducted.With a sample of 35 participants, the human classification accuracy was 92.47%. These preliminary results clearly indicate that humans are very good at identifying drone’s acoustic signatures from other sounds and can augment the CNN’s performance. 

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2. Implementation of an Artificial Immune System to Mitigate Cybersecurity Threats in Unmanned Aerial Systems

   https://doi.org/10.1109/ICII.2019.00013  

   Shivers, Meagan; Llanes, Christian; Sherman, Maxwell (November 2019, IEEE International Workshop on Sensing, Communication, and Control for Unmanned Aerial Systems)

   he pervasive operation of customer drones, or small-scale unmanned aerial vehicles (UAVs), has raised serious concerns about their privacy threats to the public. In recent years, privacy invasion events caused by customer drones have been frequently reported. Given such a fact, timely detection of invading drones has become an emerging task. Existing solutions using active radar, video or acoustic sensors are usually too costly (especially for individuals) or exhibit various constraints (e.g., requiring visual line of sight). Recent research on drone detection with passive RF signals provides an opportunity for low-cost deployment of drone detectors on commodity wireless devices. However, the state of the arts in this direction rely on line-of-sight (LOS) RF signals, which makes them only work under very constrained conditions. The support of more common scenarios, i.e., non-line-of-sight (NLOS), is still missing for low-cost solutions. In this paper, we propose a novel detection system for privacy invasion caused by customer drone. Our system is featured with accurate NLOS detection with low-cost hardware (under $50). By exploring and validating the relationship between drone motions and RF signal under the NLOS condition, we find that RF signatures of drones are somewhat “amplified” by multipaths in NLOS. Based on this observation, we design a two-step solution which first classifies received RSS measurements into LOS and NLOS categories; deep learning is then used to extract the signatures and ultimately detect the drones. Our experimental results show that LOS and NLOS signals can be identified at accuracy rates of 98.4% and 96% respectively. Our drone detection rate for NLOS condition is above 97% with a system implemented using Raspberry PI 3 B+. 

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3. Detection and Tracking of Drone Swarms using LiDAR

   Abir, Tasnim; Le, Vu; Kuantama, Endrowednes; Gupta, Pranjol; Copley, Austin; Dawes, Judith; Islam, Mohammad; Han, Richard; Nguyen, Phuc (June 2025, ACM MobiSys 2025)

   This paper introduces LiSWARM, a low-cost LiDAR system to detect and track individual drones in a large swarm. LiSWARM provides robust and precise localization and recognition of drones in 3D space, which is not possible with state-of-the-art drone tracking systems that rely on radio-frequency (RF), acoustic, or RGB image signatures. It includes (1) an efficient data processing pipeline to process the point clouds, (2) robust priority-aware clustering algorithms to isolate swarm data from the background, (3) a reliable neural network-based algorithm to recognize the drones, and (4) a technique to track the trajectory of every drone in the swarm. We develop the LiSWARM prototype and validate it through both in-lab and field experiments. Notably, we measure its performance during two drone light shows involving 150 and 500 drones and confirm that the system achieves up to 98% accuracy in recognizing drones and reliably tracking drone trajectories. To evaluate the scalability of LiSWARM, we conduct a thorough analysis to benchmark the system’s performance with a swarm consisting of 15,000 drones. The results demonstrate the potential to leverage LiSWARM for other applications, such as battlefield operations, errant drone detection, and securing sensitive areas such as airports and prisons. 

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4. Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms

   https://doi.org/10.1007/978-3-030-36844-9  

   Golmohammadi, Meysam; Shah, Vinit; Obeid, Iyad; Picone, Joseph (April 2020, Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications)

   Obeid, Iyad; Selesnick, Ivan; Picone, Joseph (Ed.)

   Scalp electroencephalograms (EEGs) are the primary means by which phy-sicians diagnose brain-related illnesses such as epilepsy and seizures. Au-tomated seizure detection using clinical EEGs is a very difficult machine learning problem due to the low fidelity of a scalp EEG signal. Neverthe-less, despite the poor signal quality, clinicians can reliably diagnose ill-nesses from visual inspection of the signal waveform. Commercially avail-able automated seizure detection systems, however, suffer from unaccepta-bly high false alarm rates. Deep learning algorithms that require large amounts of training data have not previously been effective on this task due to the lack of big data resources necessary for building such models and the complexity of the signals involved. The evolution of big data science, most notably the release of the Temple University EEG (TUEG) Corpus, has mo-tivated renewed interest in this problem. In this chapter, we discuss the application of a variety of deep learning ar-chitectures to automated seizure detection. Architectures explored include multilayer perceptrons, convolutional neural networks (CNNs), long short-term memory networks (LSTMs), gated recurrent units and residual neural networks. We use the TUEG Corpus, supplemented with data from Duke University, to evaluate the performance of these hybrid deep structures. Since TUEG contains a significant amount of unlabeled data, we also dis-cuss unsupervised pre-training methods used prior to training these com-plex recurrent networks. Exploiting spatial and temporal context is critical for accurate disambigua-tion of seizures from artifacts. We explore how effectively several conven-tional architectures are able to model context and introduce a hybrid system that integrates CNNs and LSTMs. The primary error modalities observed by this state-of-the-art system were false alarms generated during brief delta range slowing patterns such as intermittent rhythmic delta activity. A varie-ty of these types of events have been observed during inter-ictal and post-ictal stages. Training models on such events with diverse morphologies has the potential to significantly reduce the remaining false alarms. This is one reason we are continuing our efforts to annotate a larger portion of TUEG. Increasing the data set size significantly allows us to leverage more ad-vanced machine learning methodologies. 

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5. Manipulating Drone Position Control

   https://doi.org/10.1109/CNS.2019.8802817  

   Chen, Wenxin; Dong, Yingfei; Duan, Zhenhai (June 2019, 2019 IEEE Conference on Communications and Network Security (CNS))

   Although consumer drones have been used in many attacks, besides specific methods such as jamming, very little research has been conducted on systematical methods to counter these drones. In this paper, we develop generic methods to compromise drone position control algorithms in order to make malicious drones deviate from their targets. Taking advantage of existing methods to remotely manipulate drone sensors through cyber or physical attacks (e.g., [1], [2]), we exploited the weaknesses of position estimation and autopilot controller algorithms on consumer drones in the proposed attacks. For compromising drone position control, we first designed two state estimation attacks: a maximum False Data Injection (FDI) attack and a generic FDI attack that compromised the Kalman-Filter-based position estimation (arguably the most popular method). Furthermore, based on the above attacks, we proposed two attacks on autopilot-based navigation, to compromise the actual position of a malicious drone. To the best of our knowledge, this is the first piece of work in this area. Our analysis and simulation results show that the proposed attacks can significantly affect the position estimation and the actual positions of drones. We also proposed potential countermeasures to address these attacks. 

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