@inproceedings{deldari2020espresso,
    title={Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor data}, 
    author={Deldari, Shohreh and Smith, Daniel V. and Sadri and  Amin and Salim and  Flora D. },
    journal={Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)},
    volume={4},
    number={3},
    articleno={77},
    year={2020},
    url = {https://doi.org/10.1145/3411832},
    doi = {10.1145/3411832}
}

@inproceedings{10.1145/3081333.3081340,
author = {Virmani and  Aditya and Shahzad and  Muhammad},
title = {Position and Orientation Agnostic Gesture Recognition Using WiFi},
year = {2017},
isbn = {9781450349284},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3081333.3081340},
doi = {10.1145/3081333.3081340},
abstract = {WiFi based gesture recognition systems have recently proliferated due to the ubiquitous availability of WiFi in almost every modern building. The key limitation of existing WiFi based gesture recognition systems is that they require the user to be in the same configuration (i.e., at the same position and in same orientation) when performing gestures at runtime as when providing training samples, which significantly restricts their practical usability. In this paper, we propose a WiFi based gesture recognition system, namely WiAG, which recognizes the gestures of the user irrespective of his/her configuration. The key idea behind WiAG is that it first requests the user to provide training samples for all gestures in only one configuration and then automatically generates virtual samples for all gestures in all possible configurations by applying our novel translation function on the training samples. Next, for each configuration, it generates a classification model using virtual samples corresponding to that configuration. To recognize gestures of a user at runtime, as soon as the user performs a gesture, WiAG first automatically estimates the configuration of the user and then evaluates the gesture against the classification model corresponding to that estimated configuration. Our evaluation results show that when user's configuration is not the same at runtime as at the time of providing training samples, WiAG significantly improves the gesture recognition accuracy from just 51.4\% to 91.4\%.},
booktitle = {Proceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services},
pages = {252–264},
numpages = {13},
keywords = {agnostic, position, orientation, WiFi, gesture recognition},
location = {Niagara Falls, New York, USA},
series = {MobiSys '17}
}

@article{aminikhanghahi2018real,
  title={Real-time change point detection with application to smart home time series data},
  author={Aminikhanghahi, Samaneh and Wang and  Tinghui and Cook and  Diane J},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  volume={31},
  number={5},
  pages={1010--1023},
  year={2018},
  publisher={IEEE}
}

%Fancourt, C.L., Principe, J.C., 1996. A neighborhood map of competing one step predictors for piecewise segmentation and identification of time series. In: Proceedings of the International Conference on Neural Network, vol. 4, pp. 1906–1911.

@article{xiao2022self,
  title={Self-Supervised Few-Shot Time-series Segmentation for Activity Recognition},
  author={Xiao, Chunjing and Chen, Shiming and Zhou and  Fan and Wu and  Jie},
  journal={IEEE Transactions on Mobile Computing},
  year={2022},
  publisher={IEEE}
}

@misc{2207.09925,
  doi = {10.48550/ARXIV.2207.09925},
  
  url = {https://arxiv.org/abs/2207.09925},
  
  author = {Xu, Leiyang and Wang, Qiang and Lin and  Xiaotian and Yuan and  Lin},
  
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
  
  title = {An Efficient Framework for Few-shot Skeleton-based Temporal Action Segmentation},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@article{sarker2018individualized,
  title={Individualized time-series segmentation for mining mobile phone user behavior},
  author={Sarker, Iqbal H and Colman, Alan and Kabir and  Muhammad Ashad and Han and  Jun},
  journal={The Computer Journal},
  volume={61},
  number={3},
  pages={349--368},
  year={2018},
  publisher={Oxford University Press}
}

@article{4445667,  author={Liu, Xiaoyan and Lin, Zhenjiang and Wang, Huaiqing},  journal={IEEE Transactions on Knowledge and Data Engineering},   title={Novel Online Methods for Time Series Segmentation},   year={2008},  volume={20},  number={12},   pages={1616-1626},   doi={10.1109/TKDE.2008.29}}

@article{4160958,
    author={Yujian, Li and Bo, Liu},
    journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
    title={A Normalized Levenshtein Distance Metric},
    year={2007},
    volume={29},
    number={6},
    pages={1091-1095},
    doi={10.1109/TPAMI.2007.1078}
}

@article{aminikhanghahi2017survey,
  title={A survey of methods for time series change point detection},
  author={Aminikhanghahi and  Samaneh and Cook and  Diane J},
  journal={Knowledge and information systems},
  volume={51},
  number={2},
  pages={339--367},
  year={2017},
  publisher={Springer}
}

@misc{palitronica,
  title = {Palitronica - Palisade},
  howpublished = {https://www.palitronica.com/products/palisade},
  note = {Accessed: 2010-03-26}
}

@misc{hidden-palitronica,
  title = {Reference hidden for peer-review},
}

@misc{articlemlcs,
  title = {Side-channel Based Runtime Intrusion Detection for Network Equipment},
  author = {Arthur Grisel-Davy and Goksen U. Guler and Julian Dickert and Philippe Vibien and Waleed Khan and Jack Morgan and Carlos Moreno and Sebastian Fischmeister},
  year = {2023},
  Journal = {Machine Learning for Cyber Security. ML4CS 2023}
}

@inbook{278e1df91d22494f9be2adfca2559f92,
title = "A data management platform for personalised real-time energy feedback",
keywords = "smart homes, real-time energy, smart energy meter, energy consumption, Electrical engineering. Electronics Nuclear engineering, Electrical and Electronic Engineering",
author = "David Murray and Jing Liao and Lina Stankovic and Vladimir Stankovic and Richard Hauxwell-Baldwin and Charlie Wilson and Michael Coleman and Tom Kane and Steven Firth",
year = "2015",
booktitle = "Proceedings of the 8th International Conference on Energy Efficiency in Domestic Appliances and Lighting",
}

@Article{Hunter:2007,
  Author    = {Hunter, J. D.},
  Title     = {Matplotlib: A 2D graphics environment},
  Journal   = {Computing in Science \& Engineering},
  Volume    = {9},
  Number    = {3},
  Pages     = {90--95},
  abstract  = {Matplotlib is a 2D graphics package used for Python for
  application development, interactive scripting, and publication-quality
  image generation across user interfaces and operating systems.},
  publisher = {IEEE COMPUTER SOC},
  doi       = {10.1109/MCSE.2007.55},
  year      = 2007
}

@inproceedings{kocher1996timing,
  title={Timing attacks on implementations of Diffie-Hellman, RSA, DSS, and other systems},
  author={Kocher and  Paul C},
  booktitle={Advances in Cryptology—CRYPTO’96: 16th Annual International Cryptology Conference Santa Barbara, California, USA August 18--22, 1996 Proceedings 16},
  pages={104--113},
  year={1996},
  organization={Springer}
}

@article{villalobos2021flexible,
  title={A flexible alarm prediction system for smart manufacturing scenarios following a forecaster--analyzer approach},
  author={Villalobos, Kevin and Suykens and  Johan and Illarramendi and  Arantza},
  journal={Journal of Intelligent Manufacturing},
  volume={32},
  pages={1323--1344},
  year={2021},
  publisher={Springer}
}

@article{belikovetsky2018digital,
  title={Digital audio signature for 3D printing integrity},
  author={Belikovetsky, Sofia and Solewicz, Yosef A and Yampolskiy, Mark and Toh and  Jinghui and Elovici and  Yuval},
  journal={IEEE Transactions on Information Forensics and Security},
  volume={14},
  number={5},
  pages={1127--1141},
  year={2018},
  publisher={IEEE}
}

@article{al2016forensics,
  title={Forensics of thermal side-channel in additive manufacturing systems},
  author={Al Faruque, Mohammad Abdullah and Chhetri, Sujit Rokka and Canedo and  A and Wan and  J},
  journal={University of California, Irvine},
  volume={12},
  number={13},
  pages={176},
  year={2016}
}

@article{10.1145/3571288,
author = {Thakur, Shailja and Moreno and  Carlos and Fischmeister and  Sebastian},
title = {CANOA: CAN Origin Authentication Through Power Side-Channel Monitoring},
year = {2022},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
issn = {2378-962X},
url = {https://doi.org/10.1145/3571288},
doi = {10.1145/3571288},
abstract = {The lack of any sender authentication mechanism in place makes Controller Area Network (CAN) vulnerable to security threats. For instance, an attacker can impersonate an Electronic Control Unit (ECU) on the bus and send spoofed messages unobtrusively with the identifier of the impersonated ECU. To address this problem, we propose a novel source authentication technique that uses power consumption measurements of the ECU to authenticate the source of a message. A transmission of an ECU affects the power consumption and a characteristic pattern will appear. Our technique exploits the power consumption of each ECU during the transmission of a message to determine whether the message actually originated from the purported sender. We evaluate our approach in both a lab setup and a real vehicle. We also evaluate our approach against factors that can impact the power consumption measurement of the ECU. The results of the evaluation show that the proposed technique is applicable in a broad range of operating conditions with reasonable computational power requirements and attaining good accuracy.},
note = {Just Accepted},
journal = {ACM Trans. Cyber-Phys. Syst.},
month = {nov},
keywords = {CAN, transmissions, authentication, automotive security}
}

@article{gatlin2019detecting,
  title={Detecting sabotage attacks in additive manufacturing using actuator power signatures},
  author={Gatlin, Jacob and Belikovetsky, Sofia and Moore, Samuel B and Solewicz, Yosef and Elovici and  Yuval and Yampolskiy and  Mark},
  journal={IEEE Access},
  volume={7},
  pages={133421--133432},
  year={2019},
  publisher={IEEE}
}

@article{CHOU2014400,
title = {Real-time detection of anomalous power consumption},
journal = {Renewable and Sustainable Energy Reviews},
volume = {33},
pages = {400-411},
year = {2014},
issn = {1364-0321},
doi = {https://doi.org/10.1016/j.rser.2014.01.088},
url = {https://www.sciencedirect.com/science/article/pii/S1364032114001142},
author = {Jui-Sheng Chou and Abdi Suryadinata Telaga},
keywords = {Power consumption, Big data analytics, Anomaly detection, Pattern recognition, Real time detection, Time series prediction},
abstract = {Effective feedback can reduce building power consumption and carbon emissions. Therefore, providing information to building managers and tenants is the first step in identifying ways to reduce power consumption. Since reducing anomalous consumption can have a large impact, this study proposes a novel approach to using large sets of data for a building space to identify anomalous power consumption. This method identifies anomalies in two stages: consumption prediction and anomaly detection. Daily real-time consumption is predicted by using a hybrid neural net ARIMA (auto-regressive integrated moving average) model of daily consumption. Anomalies are then identified by differences between real and predicted consumption by applying the two-sigma rule. The experimental results for a 17-week study of electricity consumption in a building office space confirm that the method can detect anomalous values in real time. Another contribution of the study is the development of a formalized methodology for detecting anomalous patterns in large data sets for real-time of building office space energy consumption. Moreover, the prediction component can be used to plan electricity usage while the anomaly detection component can be used to understand the energy consumption behaviors of tenants.}
}

@INPROCEEDINGS{9934955,

  author={Grisel-Davy, Arthur and Bhogayata, Amrita Milan and Pabbi, Srijan and Narayan and  Apurva and Fischmeister and  Sebastian},

  booktitle={2022 International Conference on Embedded Software (EMSOFT)}, 

  title={Work-in-Progress: Boot Sequence Integrity Verification with Power Analysis}, 

  year={2022},

  volume={},

  number={},

  pages={3-4},

  doi={10.1109/EMSOFT55006.2022.00009}}

@INPROCEEDINGS{9061783,

  author={Li, Yanjie and He, Ruiwen and Ji and  Xiaoyu and Xu and  Wenyuan},

  booktitle={2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2)}, 

  title={Using power side-channel to implement anomaly-based intrusion detection on smart grid terminals}, 

  year={2019},

  volume={},

  number={},

  pages={2669-2674},

  doi={10.1109/EI247390.2019.9061783}}

@article{ilgun1995state,
  title={State transition analysis: A rule-based intrusion detection approach},
  author={Ilgun, Koral and Kemmerer and  Richard A and Porras and  Phillip A},
  journal={IEEE transactions on software engineering},
  volume={21},
  number={3},
  pages={181--199},
  year={1995},
  publisher={IEEE}
}

@INPROCEEDINGS{5563714,

  author={Lei Li and De-Zhang Yang and Fang-Cheng Shen},

  booktitle={2010 3rd International Conference on Computer Science and Information Technology}, 

  title={A novel rule-based Intrusion Detection System using data mining}, 

  year={2010},

  volume={6},

  number={},

  pages={169-172},

  doi={10.1109/ICCSIT.2010.5563714}}

@article{kumar2020integrated,
  title={An integrated rule based intrusion detection system: analysis on UNSW-NB15 data set and the real time online dataset},
  author={Kumar, Vikash and Sinha, Ditipriya and Das, Ayan Kumar and Pandey and  Subhash Chandra and Goswami and  Radha Tamal},
  journal={Cluster Computing},
  volume={23},
  pages={1397--1418},
  year={2020},
  publisher={Springer}
}

@article{uddin2018activity,
  title={Activity recognition for cognitive assistance using body sensors data and deep convolutional neural network},
  author={Uddin and  Md Zia and Hassan and  Mohammad Mehedi},
  journal={IEEE Sensors Journal},
  volume={19},
  number={19},
  pages={8413--8419},
  year={2018},
  publisher={IEEE}
}

@article{wannenburg2016physical,
  title={Physical activity recognition from smartphone accelerometer data for user context awareness sensing},
  author={Wannenburg and  Johan and Malekian and  Reza},
  journal={IEEE Transactions on Systems, Man, and Cybernetics: Systems},
  volume={47},
  number={12},
  pages={3142--3149},
  year={2016},
  publisher={IEEE}
}

@inproceedings{bodor2003vision,
  title={Vision-based human tracking and activity recognition},
  author={Bodor, Robert and Jackson and  Bennett and Papanikolopoulos and  Nikolaos},
  booktitle={Proc. of the 11th Mediterranean Conf. on Control and Automation},
  volume={1},
  pages={1--6},
  year={2003},
  organization={Citeseer}
}

@article{zhang2019numerical,
  title={Numerical delineation of 3D unsteady flow fields in side channel pumps for engineering processes},
  author={Zhang, Fan and Chen, Ke and Appiah, Desmond and Hu, Bo and Yuan and  Shouqi and Asomani and  Stephen Ntiri},
  journal={Energies},
  volume={12},
  number={7},
  pages={1287},
  year={2019},
  publisher={MDPI}
}

@INPROCEEDINGS{4393062,

  author={Zhou, Wei and Habetler and  Thomas G. and Harley and  Ronald G.},

  booktitle={2007 IEEE International Symposium on Diagnostics for Electric Machines, Power Electronics and Drives}, 

  title={Bearing Condition Monitoring Methods for Electric Machines: A General Review}, 

  year={2007},

  volume={},

  number={},

  pages={3-6},

  doi={10.1109/DEMPED.2007.4393062}}

@article{yang2016power,
  title={Power consumption based android malware detection},
  author={Yang and  Hongyu and Tang and  Ruiwen},
  journal={Journal of Electrical and Computer Engineering},
  volume={2016},
  year={2016},
  publisher={Hindawi}
}

@article{chawla2021machine,
  title={Machine learning in wavelet domain for electromagnetic emission based malware analysis},
  author={Chawla, Nikhil and Kumar and  Harshit and Mukhopadhyay and  Saibal},
  journal={IEEE Transactions on Information Forensics and Security},
  volume={16},
  pages={3426--3441},
  year={2021},
  publisher={IEEE}
}
@article{wang2015measurement,
  title={Measurement system of gear parameters based on machine vision},
  author={Wang, Wencheng and Guan, Fengnian and Ma and  Shiyong and Li and  Jian},
  journal={Measurement and Control},
  volume={48},
  number={8},
  pages={242--248},
  year={2015},
  publisher={SAGE Publications Sage UK: London, England}
}

@ARTICLE{1702202,

  author={Denning and  D.E.},

  journal={IEEE Transactions on Software Engineering}, 

  title={An Intrusion-Detection Model}, 

  year={1987},

  volume={SE-13},

  number={2},

  pages={222-232},

  doi={10.1109/TSE.1987.232894}}

@INPROCEEDINGS{9491765,

  author={Alsmadi and  Tibra and Alqudah and  Nour},

  booktitle={2021 International Conference on Information Technology (ICIT)}, 

  title={A Survey on malware detection techniques}, 

  year={2021},

  volume={},

  number={},

  pages={371-376},

  doi={10.1109/ICIT52682.2021.9491765}}

@inproceedings{10.1145/2940343.2940348,
author = {Malik and  Jyoti and Kaushal and  Rishabh},
title = {CREDROID: Android Malware Detection by Network Traffic Analysis},
year = {2016},
isbn = {9781450343466},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2940343.2940348},
doi = {10.1145/2940343.2940348},
abstract = {Android, one of the most popular open source mobile operating system, is facing a lot of security issues. Being used by users with varying degrees of awareness complicates the problem further. Most of the security problems are due to maliciousness of android applications. The malwares get installed in mobile phones through various popular applications particularly gaming applications or some utility applications from various third party app-stores which are untrustworthy. A common feature of the malware is to access the sensitive information from the mobile device and transfer it to remote servers. For our work, we have confined ourselves to defining maliciousness as leakage of privacy information by Android application. In this paper we have proposed a method named as CREDROID which identifies malicious applications on the basis of their Domain Name Server(DNS) queries as well as the data it transmits to remote server by performing the in-depth analysis of network traffic logs in offline mode. Instead of performing signature based detection which is unable to detect polymorphic malwares, we propose a pattern based detection. Pattern in our work refers to the leakage of sensitive information being sent to the remote server. CREDROID is a semi-automated approach which works on various factors like the remote server where the application is connecting, data being sent and the protocol being used for communication for identifying the trustworthiness (credibility) of the application. In our work, we have observed that 63% of the applications from a standard dataset of malwares are generating network traffic which has been the focus of our work.},
booktitle = {Proceedings of the 1st ACM Workshop on Privacy-Aware Mobile Computing},
pages = {28–36},
numpages = {9},
keywords = {Android, malware detection, network traffic analysis},
location = {Paderborn, Germany},
series = {PAMCO '16}
}
}

@article{jelali2013statistical,
  title={Statistical process control},
  author={Jelali and  Mohieddine and Jelali and  Mohieddine},
  journal={Control Performance Management in Industrial Automation: Assessment, Diagnosis and Improvement of Control Loop Performance},
  pages={209--217},
  year={2013},
  publisher={Springer}
}

@inproceedings{tongaonkar2007inferring,
  title={Inferring Higher Level Policies from Firewall Rules.},
  author={Tongaonkar, Alok and Inamdar and  Niranjan and Sekar and  R},
  booktitle={LISA},
  volume={7},
  pages={1--10},
  year={2007}
}

@article{aly2005survey,
  title={Survey on multiclass classification methods},
  author={Aly and  Mohamed},
  journal={Neural Netw},
  volume={19},
  number={1-9},
  pages={2},
  year={2005},
  publisher={Citeseer}
}

@misc{grandini2020metrics,
      title={Metrics for Multi-Class Classification: an Overview}, 
      author={Margherita Grandini and Enrico Bagli and Giorgio Visani},
      year={2020},
      eprint={2008.05756},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
}

@misc{zenodo,
title={Evaluation Dataset for the Machine State Detector, https://zenodo.org/record/7782702\#.ZCR33byZNhE},
year={2023},
}

@misc{name_hidden_for_peer_review_2023_8192914,
  title        = {{160 Hours of Labeled Power Consumption Dataset of
                   Computer, https://doi.org/10.5281/zenodo.8192914}},
  year         = 2023,
}

@article{gupta2021novel,
  title={A novel failure mode effect and criticality analysis (FMECA) using fuzzy rule-based method: A case study of industrial centrifugal pump},
  author={Gupta, Gajanand and Ghasemian and  Hamed and Janvekar and  Ayub Ahmed},
  journal={Engineering Failure Analysis},
  volume={123},
  pages={105305},
  year={2021},
  publisher={Elsevier}
}

@inproceedings{10.1145/2976749.2978353,
author = {Genkin, Daniel and Pachmanov, Lev and Pipman, Itamar and Tromer and  Eran and Yarom and  Yuval},
title = {ECDSA Key Extraction from Mobile Devices via Nonintrusive Physical Side Channels},
year = {2016},
isbn = {9781450341394},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/2976749.2978353},
doi = {10.1145/2976749.2978353},
abstract = {We show that elliptic-curve cryptography implementations on mobile devices are vulnerable to electromagnetic and power side-channel attacks. We demonstrate full extraction of ECDSA secret signing keys from OpenSSL and CoreBitcoin running on iOS devices, and partial key leakage from OpenSSL running on Android and from iOS's CommonCrypto. These non-intrusive attacks use a simple magnetic probe placed in proximity to the device, or a power probe on the phone's USB cable. They use a bandwidth of merely a few hundred kHz, and can be performed cheaply using an audio card and an improvised magnetic probe.},
booktitle = {Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security},
pages = {1626–1638},
numpages = {13},
keywords = {elliptic curve, side channel attack, electromagnetic analysis, power analysis},
location = {Vienna, Austria},
series = {CCS '16}
}

@article{randolph2020power,
  title={Power side-channel attack analysis: A review of 20 years of study for the layman},
  author={Randolph and  Mark and Diehl and  William},
  journal={Cryptography},
  volume={4},
  number={2},
  pages={15},
  year={2020},
  publisher={MDPI}
}

@article{micucci2017unimib,
  title={Unimib shar: A dataset for human activity recognition using acceleration data from smartphones},
  author={Micucci, Daniela and Mobilio and  Marco and Napoletano and  Paolo},
  journal={Applied Sciences},
  volume={7},
  number={10},
  pages={1101},
  year={2017},
  publisher={Multidisciplinary Digital Publishing Institute}
}

@article{truong2020selective,
  title={Selective review of offline change point detection methods},
  author={Truong, Charles and Oudre and  Laurent and Vayatis and  Nicolas},
  journal={Signal Processing},
  volume={167},
  pages={107299},
  year={2020},
  publisher={Elsevier}
}

@inproceedings{10.1145/3371158.3371162,
author = {Narwariya, Jyoti and Malhotra, Pankaj and Vig, Lovekesh and Shroff and  Gautam and Vishnu and  T. V.},
title = {Meta-Learning for Few-Shot Time Series Classification},
year = {2020},
isbn = {9781450377386},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3371158.3371162},
doi = {10.1145/3371158.3371162},
abstract = {Deep neural networks (DNNs) have achieved state-of-the-art results on time series classification (TSC) tasks. In this work, we focus on leveraging DNNs in the often-encountered practical scenario where access to labeled training data is difficult, and where DNNs would be prone to overfitting. We leverage recent advancements in gradient-based meta-learning, and propose an approach to train a residual neural network with convolutional layers as a meta-learning agent for few-shot TSC. The network is trained on a diverse set of few-shot tasks sampled from various domains (e.g. healthcare, activity recognition, etc.) such that it can solve a target task from another domain using only a small number of training samples from the target task. Most existing meta-learning approaches are limited in practice as they assume a fixed number of target classes across tasks. We overcome this limitation in order to train a common agent across domains with each domain having different number of target classes, we utilize a triplet-loss based learning procedure that does not require any constraints to be enforced on the number of classes for the few-shot TSC tasks. To the best of our knowledge, we are the first to use meta-learning based pre-training for TSC. Our approach sets a new benchmark for few-shot TSC, outperforming several strong baselines on few-shot tasks sampled from 41 datasets in UCR TSC Archive. We observe that pre-training under the meta-learning paradigm allows the network to quickly adapt to new unseen tasks with small number of labeled instances.},
booktitle = {Proceedings of the 7th ACM IKDD CoDS and 25th COMAD},
pages = {28–36},
numpages = {9},
keywords = {Time Series Classification, Meta-Learning, Few-Shot Learning, Convolutional Neural Networks},
location = {Hyderabad, India},
series = {CoDS COMAD 2020}
}

@article{tang2019few,
  title={Few-shot time-series classification with dual interpretability},
  author={Tang, Wensi and Liu and  Lu and Long and  Guodong},
  journal={Space},
  volume={2},
  number={T1},
  pages={T1},
  year={2019}
}

@INPROCEEDINGS{9647357,

  author={Gupta, Priyanka and Bhaskarpandit and  Sathvik and Gupta and  Manik},

  booktitle={2021 Digital Image Computing: Techniques and Applications (DICTA)}, 

  title={Similarity Learning based Few Shot Learning for ECG Time Series Classification}, 

  year={2021},

  volume={},

  number={},

  pages={1-8},

  doi={10.1109/DICTA52665.2021.9647357}}

@article{duin1997experiments,
  title={Experiments with a featureless approach to pattern recognition},
  author={Duin, Robert PW and de Ridder and  Dick and Tax and  David MJ},
  journal={Pattern Recognition Letters},
  volume={18},
  number={11-13},
  pages={1159--1166},
  year={1997},
  publisher={Elsevier}
}

@INPROCEEDINGS{8598355,

  author={Dash and  Prajna and Naik and  Kshirasagar},

  booktitle={2018 IEEE Electrical Power and Energy Conference (EPEC)}, 

  title={A Very Deep One Dimensional Convolutional Neural Network (VDOCNN) for Appliance Power Signature Classification}, 

  year={2018},

  volume={},

  number={},

  pages={1-6},

  doi={10.1109/EPEC.2018.8598355}}

@article{angelis2022nilm,
  title={NILM applications: Literature review of learning approaches, recent developments and challenges},
  author={Angelis, Georgios-Fotios and Timplalexis, Christos and Krinidis, Stelios and Ioannidis and  Dimosthenis and Tzovaras and  Dimitrios},
  journal={Energy and Buildings},
  pages={111951},
  year={2022},
  publisher={Elsevier}
}

@misc{sleep_states,
title={Sleep States Description},
url={https://learn.microsoft.com/en-us/windows-hardware/drivers/kernel/system-sleeping-states},
year={2023},
}


@misc{mitre_crypto,
  title={Mitre ATT\&CK - T1496 Resource Hijacking},
  url = {https://attack.mitre.org/versions/v13/techniques/T1496/},
}
@misc{mitre_botnet,
  title={Mitre ATT\&CK - T1583.005 Acquire Infrastructure: Botnet},
  url = {https://attack.mitre.org/versions/v13/techniques/T1583/005/},
}
@misc{mitre_prevent,
  title={Mitre ATT\&CK - T1562.001 Impair Defenses: Disable or Modify Tools},
  url = {https://attack.mitre.org/versions/v13/techniques/T1562/001/},
}
@misc{mitre_ransomware,
  title={Mitre ATT\&CK - T1486 Data Encrypted for Impact},
  url = {https://attack.mitre.org/versions/v13/techniques/T1486/},
}

@misc{opitz2021macro,
      title={Macro F1 and Macro F1},
      author={Juri Opitz and Sebastian Burst},
      year={2021},
      eprint={1911.03347},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@misc{keras,
author = {François Chollet },
title = {keras},
year = {2015},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {https://github.com/fchollet/keras},
}

@article{sklearn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa and  F. et al.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}

@INPROCEEDINGS{1253591,

  author={Saputra, H. and Vijaykrishnan, N. and Kandemir, M. and Irwin, M.J. and Brooks, R. and Kim and  S. and Zhang and  W.},

  booktitle={2003 Design, Automation and Test in Europe Conference and Exhibition},

  title={Masking the energy behavior of DES encryption [smart cards]},

  year={2003},

  volume={},

  number={},

  pages={84-89},

  doi={10.1109/DATE.2003.1253591}
}

@ARTICLE{6918465,

  author={Khedkar, Ganesh and Kudithipudi and  Dhireesha and Rose and  Garrett S.},

  journal={IEEE Transactions on Nanotechnology},

  title={Power Profile Obfuscation Using Nanoscale Memristive Devices to Counter DPA Attacks},

  year={2015},

  volume={14},

  number={1},

  pages={26-35},

  doi={10.1109/TNANO.2014.2362416}}

