@inproceedings{deldari2020espresso,
    title={Entropy and ShaPe awaRe timE-Series SegmentatiOn for processing heterogeneous sensor data}, 
    author={Deldari, Shohreh and Smith, Daniel V. and Sadri, Amin and Salim, 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, Aditya and Shahzad, 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, Tinghui and Cook, 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, Fan and Wu, 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, Xiaotian and Yuan, 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, Muhammad Ashad and Han, 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, Samaneh and Cook, 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{hidden-articlemlcs,
  title = {Reference hidden for peer-review},
}

@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, 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, Johan and Illarramendi, 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, Jinghui and Elovici, 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, A and Wan, J},
  journal={University of California, Irvine},
  volume={12},
  number={13},
  pages={176},
  year={2016}
}

@article{10.1145/3571288,
author = {Thakur, Shailja and Moreno, Carlos and Fischmeister, 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, Yuval and Yampolskiy, 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, Apurva and Fischmeister, 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, Xiaoyu and Xu, 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, Richard A and Porras, 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, Subhash Chandra and Goswami, 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, Md Zia and Hassan, 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, Johan and Malekian, 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, Bennett and Papanikolopoulos, 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, Shouqi and Asomani, Stephen Ntiri},
  journal={Energies},
  volume={12},
  number={7},
  pages={1287},
  year={2019},
  publisher={MDPI}
}

@INPROCEEDINGS{4393062,

  author={Zhou, Wei and Habetler, Thomas G. and Harley, 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, Hongyu and Tang, 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, Harshit and Mukhopadhyay, 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, Shiyong and Li, 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, 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, Tibra and Alqudah, 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, Jyoti and Kaushal, 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, Mohieddine and Jelali, 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, Niranjan and Sekar, R},
  booktitle={LISA},
  volume={7},
  pages={1--10},
  year={2007}
}

@article{aly2005survey,
  title={Survey on multiclass classification methods},
  author={Aly, 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},
}

@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, Hamed and Janvekar, 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, Eran and Yarom, 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, Mark and Diehl, 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, Marco and Napoletano, 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, Laurent and Vayatis, 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, Gautam and Vishnu, 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, Lu and Long, Guodong},
  journal={Space},
  volume={2},
  number={T1},
  pages={T1},
  year={2019}
}

@INPROCEEDINGS{9647357,

  author={Gupta, Priyanka and Bhaskarpandit, Sathvik and Gupta, 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, Dick and Tax, David MJ},
  journal={Pattern Recognition Letters},
  volume={18},
  number={11-13},
  pages={1159--1166},
  year={1997},
  publisher={Elsevier}
}

@INPROCEEDINGS{8598355,

  author={Dash, Prajna and Naik, 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, Dimosthenis and Tzovaras, 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, F. et al.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
