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Random Neural Network Based Intelligent Intrusion Detection for Wireless Sensor Networks

Ahmed Saeed, Ali Ahmadinia, Abbas Javed, Hadi Larijani
2016 Procedia Computer Science  
To validate the feasibility of the proposed security solution, it is implemented for an existing wireless sensor network system and its functionality is practically demonstrated by successfully detecting  ...  Security and privacy of data are one of the prime concerns in today's embedded devices.  ...  Different anomaly based intrusion detection techniques have been proposed in the literature based on machine-learning techniques by learning a model, depicting both normal and anomalous behavior of the  ... 
doi:10.1016/j.procs.2016.05.453 fatcat:6ivcibpxdfef3iogx7mc4loze4

Lightweight Intrusion Detection for Resource-Constrained Embedded Control Systems [chapter]

Jason Reeves, Ashwin Ramaswamy, Michael Locasto, Sergey Bratus, Sean Smith
2011 IFIP Advances in Information and Communication Technology  
In this paper, we introduce Autoscopy, an experimental host intrusion detection mechanism that operates from within the kernel and leverages its built-in tracing framework to look for control-flow anomalies  ...  for the majority of our benchmark tests.  ...  Portions of sections 1 through 5 are based in part on the thesis work of the second author [30] .  ... 
doi:10.1007/978-3-642-24864-1_3 fatcat:hprliybdezcb3ml2j4jj5cz4uu

Power-Based Non-Intrusive Condition Monitoring for Terminal Device in Smart Grid

Guoming Zhang, Xiaoyu Ji, Yanjie Li, Wenyuan Xu
2020 Sensors  
To detect the tamper of the program running on DTU, we proposed a power-based non-intrusive condition monitoring method that collects and analyzes the power consumption of DTU using power sensors and machine  ...  learning (ML) techniques, the feasibility of this approach is that the power consumption is closely related to the executing code in CPUs, that is when the execution code is tampered with, the power consumption  ...  Acknowledgments: The authors would like to thank the anonymous reviewers for their valuable comments. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s20133635 pmid:32605307 pmcid:PMC7374375 fatcat:ufunl3xtpfeplnfahleubqtrqa

Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study

Davy Preuveneers, Vera Rimmer, Ilias Tsingenopoulos, Jan Spooren, Wouter Joosen, Elisabeth Ilie-Zudor
2018 Applied Sciences  
Experiments with a realistic intrusion detection use case and an autoencoder for anomaly detection illustrate that the increased complexity caused by blockchain technology has a limited performance impact  ...  The adoption of machine learning and deep learning is on the rise in the cybersecurity domain where these AI methods help strengthen traditional system monitoring and threat detection solutions.  ...  Acknowledgments: We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research.  ... 
doi:10.3390/app8122663 fatcat:22w3om3rwnbgjd7nuo3kdbjn3i

A Smart Home Energy Management System Using Two-Stage Non-Intrusive Appliance Load Monitoring over Fog-Cloud Analytics Based on Tridium's Niagara Framework for Residential Demand-Side Management

Yung-Yao Chen, Ming-Hung Chen, Che-Ming Chang, Fu-Sheng Chang, Yu-Hsiu Lin
2021 Sensors  
The core entity of the SHEMS prototype is based on a compact, cognitive, embedded IoT controller that connects IoT end devices, such as sensors and meters, and serves as a gateway in a smart house/smart  ...  The SHEMS prototype established over fog-cloud computing in this study utilizes an artificial neural network-based NIALM approach to non-intrusively monitor relevant electrical appliances without an intrusive  ...  The core entity of the prototype is based on an ARM ® processor-based embedded system.  ... 
doi:10.3390/s21082883 pmid:33924090 pmcid:PMC8074283 fatcat:awkswind7racpaqybrr5uvlusa

SecureCore: A multicore-based intrusion detection architecture for real-time embedded systems

Man-Ki Yoon, S. Mohan, Jaesik Choi, Jung-Eun Kim, Lui Sha
2013 2013 IEEE 19th Real-Time and Embedded Technology and Applications Symposium (RTAS)  
The increasing use of multicore architectures in such systems exacerbates the problem since shared resources on these processors increase the risk of being compromised.  ...  We aim to detect malicious activities by analyzing and observing the inherent properties of the real-time system using statistical analyses of their execution profiles.  ...  In fact, most of the work for intrusion detection systems using machine learning have focused on network activity monitoring. Sinclair et al.  ... 
doi:10.1109/rtas.2013.6531076 dblp:conf/rtas/YoonMCKS13 fatcat:lak4exawmreghlutglarukyrha

A Review on Network Intrusion Detection System Using Machine Learning

Bello Nazifi Kagara, Maheyzah Md Siraj
2020 International Journal of Innovative Computing  
The concept of machine learning is considered an important method used in intrusion detection systems to detect irregular network traffic activities.  ...  This paper targets to present a holistic approach to intrusion detection system and the popular machine learning techniques applied on IDS systems, bearing In mind the need to help research scholars in  ...  The school at large, University Teknology Malaysia, for providing me with the necessary skills, knowledge and learning facilities to prepare this paper, and my father, Bello Kagara for his unending support  ... 
doi:10.11113/ijic.v10n1.252 fatcat:6edwiqiufvazdnjn7pkejaatx4

Near-Real-Time IDS for the U.S. FAA's NextGen ADS-B

Dustin M. Mink, Jeffrey McDonald, Sikha Bagui, William B. Glisson, Jordan Shropshire, Ryan Benton, Samuel Russ
2021 Big Data and Cognitive Computing  
This research provides a solution for attack mitigation by packaging a machine learning algorithm, SVM, into an intrusion detection system and calculating the feasibility of processing US ADS-B messages  ...  was used to visualize the data for feature selection, and Support Vector Machine (SVM) was used for classification.  ...  Acknowledgments: This work has been partially supported by the Askew Institute of the University of West Florida. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/bdcc5020027 fatcat:62jltjm66reutptn7ktan3kfea

A Survey on Performance Analysis through Dimensional Reduction and Classification Algorithm using KDD Cup and UNSW-NB15 Dataset

Manu V
2019 International Journal for Research in Applied Science and Engineering Technology  
In this system the intrusion detection is one of the major research problems in network security. This is the process of monitoring and analyzing network traffic data to detect security violations.  ...  Data mining approach can also play a very important role in developing an intrusion and detection technique.  ...  An important goal in the reported work is the possibility and feasibility of detecting intrusions based on characterizing various types of attacks such as DoS, probes, U2R and R2L attacks.  ... 
doi:10.22214/ijraset.2019.5162 fatcat:qf3z5t6spbaq5oqaxerbcnxsju

Machine Learning in Action: Examples [chapter]

Mariette Awad, Rahul Khanna
2015 Efficient Learning Machines  
These examples demonstrate an intelligent feedback control system based on the principles of machine learning.  ...  Machine learning exploits the power of generalization, which is an inherent and essential component of concept formation through human learning.  ...  Machine intrusion can be considered a result of several components' competing for the occurrences of the intrusion.  ... 
doi:10.1007/978-1-4302-5990-9_11 fatcat:mdmgulxgljginjs7jdwjdxg7qe

Towards NIC-based intrusion detection

M. Otey, S. Parthasarathy, A. Ghoting, G. Li, S. Narravula, D. Panda
2003 Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '03  
Simple anomaly detection and signature detection based models have been implemented on the NIC firmware, which has its own processor and memory.  ...  Intrusion detection at the NIC makes the system potentially tamper-proof and is naturally extensible to work in a distributed setting.  ...  We would like to thank Darius Buntinus for answering related questions about NIC-based programming.  ... 
doi:10.1145/956750.956847 dblp:conf/kdd/OteyPGLNP03 fatcat:r643a7evffeo3ituckcdgmucea

Towards NIC-based intrusion detection

M. Otey, S. Parthasarathy, A. Ghoting, G. Li, S. Narravula, D. Panda
2003 Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '03  
Simple anomaly detection and signature detection based models have been implemented on the NIC firmware, which has its own processor and memory.  ...  Intrusion detection at the NIC makes the system potentially tamper-proof and is naturally extensible to work in a distributed setting.  ...  We would like to thank Darius Buntinus for answering related questions about NIC-based programming.  ... 
doi:10.1145/956841.956847 fatcat:x3bg2tt67jdmrabbqkdc53ljii

An end-to-end framework for machine learning-based network intrusion detection system

Gustavo De Carvalho Bertoli, Lourenco Alves Pereira, Osamu Saotome, Aldri L. Santos, Filipe Alves Neto Verri, Cesar Augusto Cavalheiro Marcondes, Sidnei Barbieri, Moises S. Rodrigues, Jose M. Parente Oliveira
2021 IEEE Access  
The first is a helpful metric to determine the trained model's deployment feasibility on an embedded device.  ...  IMBALANCED DATASETS Imbalance in datasets for Network Intrusion Detection Systems is notorious for decreasing the effectiveness of Machine Learning (ML) based solutions.  ... 
doi:10.1109/access.2021.3101188 fatcat:va7omgwignallet2klcnqfs7pa

Intrusion detection for resource-constrained embedded control systems in the power grid

Jason Reeves, Ashwin Ramaswamy, Michael Locasto, Sergey Bratus, Sean Smith
2012 International Journal of Critical Infrastructure Protection  
This paper discusses the design and implementation of Autoscopy, an experimental host-based intrusion detection mechanism that operates from within the kernel and leverages its built-in tracing framework  ...  s design and effectiveness render it uniquely suited to intrusion detection for SCADA systems.  ...  Patagonix relies on the behavior of the hardware to verify the code being executed, while VMWatcher simply reconstructs the internal semantics of the monitored system for use by an intrusion detection  ... 
doi:10.1016/j.ijcip.2012.02.002 fatcat:fzokapdpefharfzhljx24yb53a

Research Roadmap Driven by Network Benchmarking Lab (NBL): Deep Packet Inspection, Traffic Forensics, Embedded Benchmarking, Software Defined Networking and Beyond

Ying-Dar Lin
2014 International Journal of Networking and Computing  
We spanned into the areas of cable TV networks, multi-hop cellular, Internet QoS, deep packet inspection, traffic forensics, embedded benchmarking, and software defined networking.  ...  Most researchers look for topics from the literature. But our research derived mostly from development, in turn driven by industrial projects or product testing.  ...  taint tracker for buffer overflow detection [66] , evasion through IDS [67] , attack session extraction [68] , false positive and negative analysis in intrusion detection [69] , weighted voting [70  ... 
doi:10.15803/ijnc.4.2_223 fatcat:dgwtoaiq3ze3dguukezx3iemeu
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