A novel deep learning strategy for classifying different attack patterns for deep brain implants
Open accessAbstract
Deep brain stimulators (DBSs), a widely used and comprehensively acknowledged restorative methodology, are a type of implantable medical device which uses electrical stimulation to treat neurological disorders. These devices are widely used to treat diseases such as Parkinson, movement disorder, epilepsy, and psychiatric disorders. Security in such devices plays a vital role since it can directly affect the mental, emotional, and physical state of human bodies. In worst-case situations, it can even lead to the patient's death. An adversary in such devices, for instance, can inhibit the normal functionality of the brain by introducing fake stimulation inside the human brain. Nonetheless, the adversary can impair the motor functions, alter impulse control, induce pain, or even modify the emotional pattern of the patient by giving fake stimulations through DBSs. This paper presents a deep learning methodology to predict different attack stimulations in DBSs. The proposed work uses long short-term memory, a type of recurrent network for forecasting and predicting rest tremor velocity. (A type of characteristic observed to evaluate the intensity of the neurological diseases) The prediction helps in diagnosing fake versus genuine stimulations. The effect of deep brain stimulation was tested on Parkinson tremor patients. The proposed methodology was able to detect different types of emulated attack patterns efficiently and thereby notifying the patient about the possible attack.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Assaults on implantable devices, such as brain implants, have been reported to lead to death.
“For example, assaults on implantable devices, such as brain implants, have been reported to lead to death (Nanayakkara et al., 2019; Rathore et al., 2019).”
From IoMT amid COVID-19 pandemic: Application, architecture, technology, and security (2021) · cites this paper as Rathore et al., 2019
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Projected number of people with Parkinson disease in the most populous nations, 2005 through 2030
Dorsey, E. R., Constantinescu, R., Thompson, J. P., Biglan, K., Holloway, R. G., Kieburtz, K. D., Marshall, F. J., Ravina, B. M., Schifitto, G., Siderowf, A. D., et al. (2006) · Neurology · doi:10.1212/01.wnl.0000247740.47667.03 · Cited by 2,479 · cited 1× · see citing works
“It has been estimated that there are over 180,000 individuals in the United States below 70 years of age with Parkinson Disease [6].”
BackgroundTheano: A Python framework for fast computation of mathematical expressionsPreprint
Al-Rfou, R., Alain, G., Almahairi, A., Angermueller, C., Bahdanau, D., Ballas, N., Bastien, F., Bayer, J., Belikov, A., Belopolsky, A., et al. (2016) · arXiv.org · doi:10.48550/arxiv.1605.02688 · Cited by 2,438 · see citing works
Pacemakers and Implantable Cardiac Defibrillators: Software Radio Attacks and Zero-Power Defenses
Halperin, D., Heydt-Benjamin, T. S., Ransford, B., Clark, S. S., Defend, B., Morgan, W., Fu, K. E., Kohno, T., & Maisel, W. H. (2008) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2008.31 · Cited by 762 · see citing works
A survey of key management schemes in wireless sensor networks
Xiao, Y., Rayi, V. K., Sun, B., Du, X., Hu, F., & Galloway, M. (2007) · Computer Communications · doi:10.1016/j.comcom.2007.04.009 · Cited by 578 · cited 1× · see citing works
“, [40]–[42]) studied related security issues to such gadgets.”
BackgroundAn effective key management scheme for heterogeneous sensor networks
Du, X., Xiao, Y., Guizani, M., & Chen, H. (2006) · Ad Hoc Networks · doi:10.1016/j.adhoc.2006.05.012 · Cited by 471 · see citing works
Security in wireless sensor networks
Du, X., & Chen, H. (2008) · IEEE Wireless Communications · doi:10.1109/mwc.2008.4599222 · Cited by 382 · cited 1× · see citing works
“, [40]–[42]) studied related security issues to such gadgets.”
BackgroundDu, X., Guizani, M., Xiao, Y., & Chen, H. (2009) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2009.060598 · Cited by 380 · see citing works
Gollakota, S., Hassanieh, H., Ransford, B., Katabi, D., & Fu, K. E. (2011) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/2018436.2018438 · Cited by 331 · cited 1× · see citing works
“External device methodologies employ extra devices to be worn to provide authentication such as IMDGaurd [36], MedMon [37], Cloaker [38] and IMDShield [39].”
MethodHijacking an insulin pump: Security attacks and defenses for a diabetes therapy system
Li, C., Raghunathan, A., & Jha, N. K. (2011) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/health.2011.6026732 · Cited by 328 · cited 2× · see citing works
“A few reviews have considered such an attack of listening stealthily or eavesdropping to steal patients’ information [13], [14].”
BackgroundIMDGuard: Securing implantable medical devices with the external wearable guardian
Xu, F., Qin, Z., Tan, C. C., Wang, B., & Li, Q. (2011) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infcom.2011.5934987 · Cited by 272 · see citing works
Gardner, J. (2013) · Social Studies of Science · doi:10.1177/0306312713483678 · Cited by 243 · cited 1× · see citing works
“In 2002, Deep Brain Stimulators (DBS) were granted endorsement by the US Food and Drug Administration (FDA) for the treatment of Parkinson’s ailment [3].”
BackgroundProximity-based access control for implantable medical devices
Rasmussen, K., Castelluccia, C., Heydt-Benjamin, T. S., & Čapkun, S. (2009) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/1653662.1653712 · Cited by 233 · cited 1× · see citing works
“Distance based approaches estimate the physical distance between the IMD and the caregiver by utilizing the transmitted and received transmission in proximity through piezoelectric elements [15], Diffie Hellam protocol [21], and near field communication [22].”
MethodMedMon: Securing Medical Devices Through Wireless Monitoring and Anomaly Detection
Zhang, M., Raghunathan, A., & Jha, N. K. (2013) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2013.2245664 · Cited by 185 · cited 2× · see citing works
“[37] M. Zhang, A. Raghunathan, and N. Jha, ‘‘MedMon: Securing medical devices through wireless monitoring and anomaly detection,’’ IEEE Trans.”
MethodBrainjacking: Implant Security Issues in Invasive Neuromodulation
Pycroft, L., Boccard, S., Owen, S. L. F., Stein, J. F., FitzGerald, J. J., Green, A. L., & Aziz, T. Z. (2016) · World Neurosurgery · doi:10.1016/j.wneu.2016.05.010 · Cited by 177 · cited 2× · see citing works
“[25] have divided the types of attacks in DBS in two ways namely blind attacks and targeted attacks.”
BackgroundMethodGollakota, S., Hassanieh, H., Ransford, B., Katabi, D., & Fu, K. E. (2011) · ACM SIGCOMM Computer Communication Review · doi:10.1145/2043164.2018438 · Cited by 160 · see citing works
Absence makes the heart grow fonder: new directions for implantable medical device security
Denning, T., Fu, K., & Kohno, T. (2008) · Cited by 147 · see citing works
The History and Future of Deep Brain Stimulation
Schwalb, J. M., & Hamani, C. (2008) · Neurotherapeutics · doi:10.1016/j.nurt.2007.11.003 · Cited by 146 · cited 1× · see citing works
“Moreover, depression influences more than 18 million individuals in the United States alone [7].”
BackgroundDesign challenges for secure implantable medical devices
Burleson, W. P., Clark, S. S., Ransford, B., & Fu, K. E. (2012) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/2228360.2228364 · Cited by 133 · cited 1× · see citing works
“, they require patients to change the pump setting [16].”
BackgroundDefending Resource Depletion Attacks on Implantable Medical Devices
Hei, X., Du, X., Wu, J., & Hu, F. (2010) · Globecom · doi:10.1109/glocom.2010.5685228 · Cited by 126 · see citing works
Neurosecurity: security and privacy for neural devices
Denning, T., Matsuoka, Y., & Kohno, T. (2009) · Neurosurgical FOCUS · doi:10.3171/2009.4.focus0985 · Cited by 111 · cited 1× · see citing works
“In the near future, DBSs hold the potential to be ubiquitous, mechanically fit, thereby addressing more extensive clinical needs [26].”
BackgroundIssues in treatment-resistant depression.
Keller, M. B. (2005) · PubMed · Cited by 108 · see citing works
Biometric-based two-level secure access control for Implantable Medical Devices during emergencies
Hei, X., & Du, X. (2011) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infcom.2011.5935179 · Cited by 97 · cited 1× · see citing works
“Besides this, there are a diverse set of the conventional schemes utilized for overcoming attacks in wireless medical devices [32]–[35].”
BackgroundSecure and Efficient Time Synchronization in Heterogeneous Sensor Networks
Du, X., Guizani, M., Xiao, Y., & Chen, H. (2008) · IEEE Transactions on Vehicular Technology · doi:10.1109/tvt.2007.912327 · Cited by 84 · see citing works
PIPAC: Patient infusion pattern based access control scheme for wireless insulin pump system
Hei, X., Du, X., Lin, S., & Lee, I. (2013) · IEEE Conference on Computer Communications (INFOCOM) · doi:10.1109/infcom.2013.6567115 · Cited by 83 · cited 1× · see citing works
“Additionally, anomaly detection mechanism such as deep learning [29], [30] and support vector machines [28], [31] have been used for determining the dosage pattern for the insulin pump security.”
MethodA Review of the Security of Insulin Pump Infusion Systems
Paul, N. R., Kohno, T., & Klonoff, D. C. (2011) · Journal of Diabetes Science and Technology · doi:10.1177/193229681100500632 · Cited by 68 · cited 1× · see citing works
“A few reviews have considered such an attack of listening stealthily or eavesdropping to steal patients’ information [13], [14].”
BackgroundHeida, T., Wentink, E., & Marani, E. (2013) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-10-70 · Cited by 66 · see citing works
Deep brain stimulation and cluster headache
Leone, M., Franzini, A., Felisati, G., Mea, E., Curone, M., Tullo, V., Broggi, G., & Bussone, G. (2005) · Neurological Sciences · doi:10.1007/s10072-005-0427-4 · Cited by 58 · cited 1× · see citing works
“Also, it is also being used for neurological pains and headaches [8].”
BackgroundBeuter, A. C. (2001) · Thalamus & Related Systems · doi:10.1016/s1472-9288(01)00020-6 · Cited by 53 · cited 3× · see citing works
“Besides this, Beuter [43] has studied four more tremor characteristics.”
BackgroundMethodInfluentialPatient Infusion Pattern based Access Control Schemes for Wireless Insulin Pump System
Hei, X., Du, X., Lin, S., Lee, I., & Sokolsky, O. V. (2014) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2014.2370045 · Cited by 47 · cited 1× · see citing works
“Additionally, anomaly detection mechanism such as deep learning [29], [30] and support vector machines [28], [31] have been used for determining the dosage pattern for the insulin pump security.”
MethodA review of security challenges, attacks and resolutions for wireless medical devices
Rathore, H., Mohamed, A. M. S., Al-Ali, A. K., Du, X., & Guizani, M. (2017) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc.2017.7986505 · Cited by 47 · cited 5× · see citing works
“Besides this, another type of attack can be introduced in IMDs where in the intruder has the capability to create radio transmissions to replay repeated operations and commands.”
BackgroundInfluentialAuthenticated Key Establishment Protocols for a Home Health Care System
Singh, K., & Muthukkumarasamy, V. (2007) · International Conference on Intelligent Sensors, Sensor Networks and Information Processing · doi:10.1109/issnip.2007.4496869 · Cited by 39 · cited 1× · see citing works
“Key management protocols are used for providing authentication to the authorised users using symmetric [15], public key [23] and physiological [24] signals for the generation of keys.”
BackgroundMulti-layer security scheme for implantable medical devices
Rathore, H., Fu, C., Mohamed, A. M. S., Al-Ali, A. K., Du, X., Guizani, M., & Yu, Z. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3819-0 · Cited by 36 · cited 1× · see citing works
“Though the mechanism is secure and lightweight, the scheme lacks to accommodate the changes of bio-metric with respect to time [19], [20].”
BackgroundMulti-Layer Perceptron Model on Chip for Secure Diabetic Treatment
Rathore, H., Wenzel, L., Al-Ali, A. K., Mohamed, A. M. S., Du, X., & Guizani, M. (2018) · IEEE Access · doi:10.1109/access.2018.2854822 · Cited by 32 · cited 1× · see citing works
“Additionally, anomaly detection mechanism such as deep learning [29], [30] and support vector machines [28], [31] have been used for determining the dosage pattern for the insulin pump security.”
MethodConsensus-Aware Sociopsychological Trust Model for Wireless Sensor Networks
Rathore, H., Badarla, V., & Shit, S. (2016) · ACM Transactions on Sensor Networks · doi:10.1145/2903721 · Cited by 30 · cited 1× · see citing works
“The adversary can employ different types of attack strategies [47] inDBS.”
BackgroundA Comparative Review on the Wireless Implantable Medical Devices Privacy and Security
Ankaralı, Z. E., Abbasi, Q. H., Demir, A. F., Serpedin, E., Qaraqe, K. A., & Arslan, H. (2014) · Wireless Mobile Communication and Healthcare · doi:10.4108/icst.mobihealth.2014.257411 · Cited by 25 · cited 1× · see citing works
“This capability provides room for the attackers to eavesdrop the information, compromise the patient’s data with the intent to physically harm them by reprogramming the implanted devices [12].”
BackgroundRathore, H. (2016) · Springer eBooks · doi:10.1007/978-3-319-29782-8_7 · Cited by 22 · see citing works
A comparative review on the wireless implantable medical devices privacy and security
Ankaralı, Z. E., Abbasi, Q. H., Demir, A. F., Serpedin, E., Qaraqe, K. A., & Arslan, H. (2014) · International Conference on Wireless Mobile Communication and Healthcare · doi:10.1109/mobihealth.2014.7015957 · Cited by 21 · see citing works
Zheng, G., Fang, G., Shankaran, R., Orgun, M. A., & Dutkiewicz, E. (2014) · International Symposium on Wireless Personal Multimedia Communications (WPMC) · doi:10.1109/wpmc.2014.7014892 · Cited by 19 · cited 1× · see citing works
“Key management protocols are used for providing authentication to the authorised users using symmetric [15], public key [23] and physiological [24] signals for the generation of keys.”
BackgroundDLRT: Deep Learning Approach for Reliable Diabetic Treatment
Rathore, H., Al-Ali, A. K., Mohamed, A. M. S., Du, X., & Guizani, M. (2017) · Globecom · doi:10.1109/glocom.2017.8255028 · Cited by 19 · cited 1× · see citing works
“Additionally, anomaly detection mechanism such as deep learning [29], [30] and support vector machines [28], [31] have been used for determining the dosage pattern for the insulin pump security.”
MethodDTW based Authentication for Wireless Medical Device Security
Rathore, H., Al-Ali, A. K., Mohamed, A. M. S., Du, X., & Guizani, M. (2018) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc.2018.8450419 · Cited by 18 · cited 1× · see citing works
“Though the mechanism is secure and lightweight, the scheme lacks to accommodate the changes of bio-metric with respect to time [19], [20].”
BackgroundKim, B., Yu, J., & Kim, H. (2012) · ACM Conference on Embedded Networked Sensor Systems (SenSys) · doi:10.1145/2426656.2426691 · Cited by 14 · cited 1× · see citing works
“Distance based approaches estimate the physical distance between the IMD and the caregiver by utilizing the transmitted and received transmission in proximity through piezoelectric elements [15], Diffie Hellam protocol [21], and near field communication [22].”
MethodElectrical stimulation of the nervous system for pain control.
Dm, L. (1978) · PubMed · Cited by 12 · see citing works
Bio-inspired Approaches in Various Engineering Domain
Rathore, H. (2016) · Springer eBooks · doi:10.1007/978-3-319-29782-8_11 · Cited by 2 · cited 1× · see citing works
“Biological systems have caught the attention of scientific community in diverse fields such as computer science, mechanical, agriculture, energy etc [27].”
Background
Flagged first, then by citations
Research works printed but not linked: 3
- J. Radcliffe, ''Hacking medical devices for fun and insulin: Breaking the human SCADA system,'' in Proc. Black Hat Conf. Presentation Slides, 2011.
- B. Kim, J. Yu, and H. Kim, ''In-vivo NFC: Remote monitoring of implanted medical devices with improved privacy,'' in Proc. 10th ACM Conf. Embed- ded Netw. Sensor Syst., 2012, pp. 327-328.
- X. Du, M. Guizani, Y. Xiao, and H.-H. Chen, ''A routing-driven ellip- tic curve cryptography based key management scheme for heteroge- neous sensor networks,'' IEEE Trans. Wireless Commun., vol. 8, no. 3, pp. 1223-1229, Mar. 2009.
Cited by
Sorted by citations
COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray ImagesPreprint
Hemdan, E. E., Shouman, M. A., & Karar, M. E. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.11055 · Cited by 1,181 · see citing works
“Now, artificial intelligence techniques such as machine learning and deep learning become the core of advanced CAD systems in many medical applications; for example, pulmonary diseases [21, 22], cardiology [23, 24], and brain surgery [25, 26].”
BackgroundIoMT amid COVID-19 pandemic: Application, architecture, technology, and security
Mohd Aman, A. H., Hassan, W. H., Sameen, S., Attarbashi, Z. S., Alizadeh, M., & Latiff, L. A. (2021) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102886 · Cited by 237 · see citing works
“For example, assaults on implantable devices, such as brain implants, have been re-ported to lead to death (Nanayakkara et al., 2019; Rathore et al., 2019).”
BackgroundSurvey of Machine Learning based intrusion detection methods for Internet of Medical Things
Si-Ahmed, A., Al-Garadi, M. A., & Boustia, N. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110227 · Cited by 188 · see citing works
Rolling Bearing Fault Diagnosis Based on Convolutional Neural Network and Support Vector Machine
Yuan, L., Lian, D., Kang, X., Chen, Y., & Zhai, K. (2020) · IEEE Access · doi:10.1109/access.2020.3012053 · Cited by 181 · see citing works
“Recently, deep learning has become a vital research direction and gradually applied to various fields [16]–[19].”
BackgroundA tree classifier based network intrusion detection model for Internet of Medical Things
Gupta, K., Sharma, D. K., Gupta, K. D., & Kumar, A. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.108158 · Cited by 158 · see citing works
Hameed, S. S., Hassan, W. H., Latiff, L. A., & Ghabban, F. M. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.414 · Cited by 134 · see citing works
“…device category IoMT layer-References Supervised ML—Implantable, wearable Sensor layer-{Haque, Rahman & Aziz (2015), Abdaoui et al. (2020), Rathore et al. (2018c), Khan et al. (2017), Gao & Thamilarasu (2017), Newaz et al. (2019), Ben Amor, Lahyani & Jmaiel (2020), Mohamed,…”
BackgroundMethodInfluentialSecurity and Privacy Management in Internet of Medical Things (IoMT): A Synthesis
Hireche, R., Mansouri, H., & Pathan, A. K. (2022) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp2030033 · Cited by 117 · see citing works
“According to a recent Comparitech report, these attacks have cost the healthcare industry more than $160 million since 2016 [44,45].”
BackgroundA Survey of Blockchain Enabled Cyber-Physical Systems
Rathore, H., Mohamed, A. M. S., & Guizani, M. (2020) · Sensors · doi:10.3390/s20010282 · Cited by 112 · see citing works
Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost
Nasiri, H., & Hasani, S. (2022) · Radiography · doi:10.1016/j.radi.2022.03.011 · Cited by 101 · see citing works
“For instance, in areas like lung [19], [20] and cardiovascular [21], [22] diseases and brain surgery [23], [24] it has contributed so effectively to the medical community and patients.”
Machine Learning and Deep Learning Methods for Intrusion Detection Systems in IoMT: A survey
Rbah, Y., Mahfoudi, M., Balboul, Y., Fattah, M. Y., Mazer, S., Elbekkali, M., & Bernoussi, B. (2022) · International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET) · doi:10.1109/iraset52964.2022.9738218 · Cited by 97 · see citing works
Classification of VLF/LF Lightning Signals Using Sensors and Deep Learning Methods
Wang, J., Huang, Q., Ma, Q., Chang, S., He, J. L., Wang, H., Zhou, X. F., Xiao, F., & Gao, C. (2020) · Sensors · doi:10.3390/s20041030 · Cited by 67 · see citing works
“[24] proposed a new deep learning strategy for classifying different attack methods of deep brain implants.”
BackgroundAbdel‐Basset, M., Ding, W., & Abdel‐Fatah, L. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.03.010 · Cited by 41 · see citing works
Tervoort, T., de Oliveira, M. T., Pieters, W., van Gelder, P. H. A. J. M., Olabarriaga, S. D., & Marquering, H. A. (2020) · IEEE Access · doi:10.1109/access.2020.2984376 · Cited by 32 · see citing works
“Rathore et al. [48] take a similar approach and examine stimulation patterns from a deep brain implant.”
BackgroundBlockchain applications for healthcare
Rathore, H., Mohamed, A. M. S., & Guizani, M. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819045-6.00008-x · Cited by 31 · see citing works
Deep learning-empowered intrusion detection framework for the Internet of Medical Things environment
Shambharkar, P. G., & Sharma, N. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-024-02149-9 · Cited by 29 · see citing works
Risk Detection in Wireless Body Sensor Networks for Health Monitoring Using Hybrid Deep Learning
Rajawat, A. S., Barhanpurkar, K., Shaw, R. N., & Ghosh, A. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-0749-3_54 · Cited by 28 · see citing works
Chen, J., Pan, F., Zhong, P., He, T., Qi, L., Lu, J., He, P., & Zheng, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3013339 · Cited by 22 · see citing works
“It has been successfully applied in many real-world problems involving sequence data, for instance, travel time prediction [25], speech recognition [26], and patterns prediction in medical area [27].”
BackgroundSecure medical treatment with deep learning on embedded board
Abdaoui, A., Al-Ali, A. K., Riahi, A. A., Mohamed, A. M. S., Du, X., & Guizani, M. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819045-6.00007-8 · Cited by 19 · see citing works
Social Psychology Inspired Distributed Ledger Technique for Anomaly Detection in Connected Vehicles
Rathore, H., Sai, S., & Gundewar, A. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3262398 · Cited by 19 · see citing works
“For each road topology, we have chosen a sensor fault randomly from the available set of sensor faults [33].”
MethodVidushi, Agarwal, M., Rajak, A., & Shrivastava, A. K. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1998/1/012008 · Cited by 18 · see citing works
“Introduction Deep learning [1] inspired from brain simulators, the most acknowledging, promising and advance machine learning methodology that brings the breakthrough achievement for interpreting and classifying the image data.”
BackgroundGupta, K., Gupta, K. D., Kumar, D., Srivastava, G., & Sharma, D. K. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3325964 · Cited by 18 · see citing works
“[32]topreventattacksondeepbrainsimulators thatutilizedsimulationtechniquesasaccuracyandsimulated attackswerenotprovided.”
Ziogas, I., Lamprou, C., Hadjileontiadis, L. J., & Lamprou, C. (2023) · IEEE Access · doi:10.1109/access.2023.3324987 · Cited by 15 · see citing works
“Works that utilized this dataset so far used deep learning to predict different attack stimulations in DBS [34], employed discrete non-Markovian stochastic processes to study the Parkinsonian pathological tremor [16], applied nonlinear analysis to characterize tremor properties as chaotic or…”
MethodDeep Learning Model to Identify Hide Images using CNN Algorithm
Shukla, R. K., Sengar, A. S., Gupta, A., & Chauhan, N. R. (2022) · International Conference on System Modeling & Advancement in Research Trends (SMART) · doi:10.1109/smart55829.2022.10047661 · Cited by 11 · see citing works
“We demonstrate that the truth difference between synthetic and real world evidence can be reduced using semantinely segmented images as an insert in the real world by our approach algorithms [19].”
BackgroundEpileptic EEG Signal Classification using Multi-class Convolutional Neural Network
Ramakrishnan, S., Murugavel, A. S. M., & Saravanan, P. (2019) · International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN) · doi:10.1109/vitecon.2019.8899453 · Cited by 11 · see citing works
“Recently CNN and Recurrent Neural Network (RNN) have been applied in the domain of EEG signal classification [4].”
MethodFourati, J., Othmani, M., & Ltifi, H. (2022) · Signal Image and Video Processing · doi:10.1007/s11760-022-02180-9 · Cited by 9 · see citing works
“The ability to manipulate the pacemaker-like gadget enables performing numerous randomized trials to assess the efficiency of the device [4].”
BackgroundAI-Driven Resilient Fault Diagnosis of Bearings in Rotating Machinery
Naqvi, S. M. W. U. H., Arif, A., Khan, A. A., Bangash, F., Sirewal, G. J., & Huang, B. (2025) · Sensors · doi:10.3390/s25227092 · Cited by 7 · see citing works
“Deep learning has emerged as one of the most important areas of research and has been applied to many other sectors [65–68].”
Intrusion detection system for Healthcare based on Convolutional Neural Networks
Mohammed, R. A., & Alheeti, K. M. A. (2022) · Iraqi International Conference on Communication and Information Technologies (IICCIT) · doi:10.1109/iiccit55816.2022.10010441 · Cited by 7 · see citing works
“describe a deep learning strategy for predicting various Deep brain stimulation (DBS) attack stimulations.[20].”
BackgroundYousra, D., Abdelhakim, B. A., & Ahmed, M. B. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-78901-5_17 · Cited by 6 · see citing works
Deep learning-based security schemes for implantable medical devices
Rathore, H., Mohamed, A. M. S., & Guizani, M. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819045-6.00006-6 · Cited by 6 · see citing works
WHTE: Weighted Hoeffding Tree Ensemble for Network Attack Detection at Fog-IoMT
Hameed, S. S., Selamat, A., Latiff, L. A., Razak, S. B. A., & Krejcar, O. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-08530-7_41 · Cited by 5 · see citing works
Artificial Intelligence driven Intrusion Detection Framework for the Internet of Medical ThingsPreprint
Shambharkar, P. G., & Sharma, N. (2023) · Research Square · doi:10.21203/rs.3.rs-2634004/v1 · Cited by 5 · see citing works
Explainability in AI-enabled medical neurotechnology: a scoping review
Schopp, L., Starke, G., & Ienca, M. (2026) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-026-01892-0 · Cited by 6 · see citing works
LLM-Based Fault Detection in Connected Vehicle Time-Series Data
Das, R., Griffith, H., & Rathore, H. (2025) · ACM Workshop on Secure and Trustworthy Cyber-Physical Systems (SaT-CPS) · doi:10.1145/3716816.3727968 · Cited by 4 · see citing works
BGRL: Basal Ganglia inspired Reinforcement Learning based framework for deep brain stimulators
Agarwal, H., & Rathore, H. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102736 · Cited by 4 · see citing works
Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach
Rathore, H., Mohamed, A. M. S., Guizani, M., & Rathore, S. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-05704-5 · Cited by 4 · see citing works
Towards securing Wireless insulin pump system using unsupervised deep learning techniquePreprint
Shobana, M., & Poonkuzhali, S. (2022) · Research Square · doi:10.21203/rs.3.rs-2109728/v1 · Cited by 4 · see citing works
G., A., Anurekha, G., Bharathi, N. A., & Ancy Micheal, A. (2025) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-025-02108-3 · Cited by 3 · see citing works
“By shifting healthcare from a reactive to a preventive model, IoMT significantly improves the quality of care and reduces systemic costs [3, 4].”
Textile Inspection Based on À trous Wavelet Transform
Saleh, E. H., Badawy, W. M., Fouad, M. M., Sayed, M. S., & El‐Samie, F. E. A. (2024) · Wireless Personal Communications · doi:10.1007/s11277-024-10958-y · Cited by 3 · see citing works
“They are also used to predict various attacks in deep brain simulators [24].”
Han, D., & Wang, L. (2020) · IEEE Access · doi:10.1109/access.2020.3021490 · Cited by 3 · see citing works
RetractedLai, Y., Chen, J., Wang, G., Wang, Z., & Miao, P. (2021) · International Conference on Neural Networks, Information and Communication Engineering (NNICE) · doi:10.1117/12.2615182 · Cited by 3 · see citing works
“Therefore, researchers began to try to use many deep learning models as feature extraction and classifier in the study of fault diagnosis methods [2-6], such as long short-term memory, deep confidence network, deep autoencoder, convolutional neural network (CNN).”
BackgroundReddy, B. P., Rangaswamy, K., Bharadwaja, D., Dupaty, M. M., Sarkar, P., & Ansari, M. S. A. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.01411108 · Cited by 3 · see citing works
“Rathore et al. [15] discussed in the paper that classifying various patterns of attack for profound implants in the brain is the goal of the deep learning approach.”
BackgroundPhuong, T. T., Ohishi, K., Yokokura, Y., & Miyazaki, T. (2024) · IEEE International Workshop on Advanced Motion Control (AMC) · doi:10.1109/amc58169.2024.10505674 · Cited by 3 · see citing works
Gourshettiwar, P., & Reddy, K. T. V. (2024) · AIP conference proceedings · doi:10.1063/5.0241091 · Cited by 2 · see citing works
Yu, P., Yang, D., Alattas, K. A., Mohammadzadeh, A., & Fekih, A. (2025) · IEEE Access · doi:10.1109/access.2025.3529477 · Cited by 2 · see citing works
“For example, Rathore et al. [15] employed deep learning to recognize various attacks in DBS.”
Vignesh Balaji, S., Vergin Raja Sarobin, M., Anbarasi, L. J., Anusha, N., Dhanya, D., Raj, B. E., & Valarmathi, M. L. (2021) · International Journal of Current Research and Review · doi:10.31782/ijcrr.2021.132022 · Cited by 2 · see citing works
“Heena Rathore et al. 13 have proposed an algorithm for third-party prediction of various attacks in DBSs.”
BackgroundDeep Learning Based Singular Spectrum Analysis for Realization of Wideband Force Sensing
Phuong, T. T., Ohishi, K., & Yokokura, Y. (2021) · IEEE International Conference on Mechatronics (ICM) · doi:10.1109/icm46511.2021.9385603 · Cited by 2 · see citing works
Lightweight Convolutional Neural Network (CNN) based COVID-19 Detection using X-ray Images
Khan, M. A., & Park, H. (2021) · Journal of Multimedia Information System · doi:10.33851/jmis.2021.8.4.251 · Cited by 2 · see citing works
“252 many diseases, such as lung nodules [6, 24], heart diseases [16, 17] and brain diseases [9, 15, 22], to assist clinicians.”
BackgroundMethodAgarwal, H., & Rathore, H. (2023) · International IEEE/EMBS Conference on Neural Engineering · doi:10.1109/ner52421.2023.10123881 · Cited by 2 · see citing works
“Todate, DBS have been implanted on over 160,000 people globally to treat a variety of neurological and non-neurological disorders [4].”
BackgroundConvNet-Based Deep Brain Stimulation for Attack Patterns
Joseph, A. S., Lazer, A. J. P., Sharma, D. K., Maria, A. B., Ganesan, N., & Sengan, S. (2023) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-031-23602-0_16 · Cited by 2 · see citing works
Leveraging Large Language Models for Fault Detection in Connected Vehicles
Das, R., Griffith, H., & Rathore, H. (2025) · Intelligent Cybersecurity Conference (ICSC) · doi:10.1109/icsc65596.2025.11140222 · Cited by 1 · see citing works
“We implemented four faults namely stuck-at, trend, drift and hard-over [25], [26].”
Baalaji, S. V., Sarobin, M. V. R., Anbarasi, L. J., Jasmine, S. G., & Rukmani, P. (2022) · doi:10.1002/9781119792437.ch9 · Cited by 1 · see citing works
Analysis On Deep Learning And Its Types
PRABA, A. L. (2021) · i-manager’s Journal on Software Engineering · doi:10.26634/jse.15.2.18164
Deep Learning Based Kalman Filter for Variable-Frequency Disturbance Elimination in Force Sensing
Phuong, T. T., Ohishi, K., & Yokokura, Y. (2021) · IEEE International Symposium on Industrial Electronics (ISIE) · doi:10.1109/isie45552.2021.9576340
Blockchain for Cyber-Physical Systems
Srilatha, D., & Nadesan, T. (2023) · IntechOpen eBooks · doi:10.5772/intechopen.110394 · Cited by 1 · see citing works
Jeyaraj, J. P. G., Bennet, M. A., Subha, K. J., & Manimaraboopathy, M. (2023) · International Conference on Emerging Research in Computational Science (ICERCS) · doi:10.1109/icercs57948.2023.10434052
“It is often considered the standard treatment for conditions like Parkinson's disease, dystonia, or severe tremors that do not respond well to other treatment approaches [1].”
BackgroundHarnessing Neurotechnology: Advancements and applications of brain implant
Divya, B., Maheshwari, K., Balakoti, J., Pravalika, P., Shirisha, G., & Sharma, J. (2024) · International Journal of Pharmaceutical Sciences and Drug Analysis · doi:10.22271/27889246.2024.v4.i1a.78
Pramesti, P. A., Supriyadi, M. R., Alfin, M. R., Noveriza, R., Wahyuno, D., Manohara, D., Melati, Miftakhurohmah, Warman, R., Hardiyanti, S., et al. (2024) · Jurnal Ilmu Komputer dan Informasi · doi:10.21609/jiki.v17i2.1237
“The application of Deep Learning, which is issue 2, June 2024 part of intelligence is inspired by the neural network of the human brain [11].”
BackgroundNew Methodology for Attack Patterns Classification in Deep Brain Stimulation
Fourati, J., Othmani, M., & Ltifi, H. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-70259-4_12
Saheed, Y. K., & Chukwuere, J. E. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02034-0
- Authors
- Rathore, H., Al-Ali, A. K., Mohamed, A., Du, X., & Guizani, M.
- Affiliations
- Qatar University (QA)
- Temple University (US)
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 24154-24164
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2917460663
- Cited by
- 65
- References
- 42
- Citations by year
- 2026: 2 · 2025: 5 · 2024: 7 · 2023: 11 · 2022: 10 · 2021: 17 · 2020: 12 · 2019: 1
- FWCI
- 6.80
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Qatar University QSpace (Qatar University) (submittedVersion)
- Funders
- Qatar National Research Fund (8-408-2-172)
- Topics
- Health Sciences › Medicine › Neurology › Neurological disorders and treatments
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
- Keywords
- deep brain stimulator, long short-term memory, Parkinson's disease, deep learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
