Hardware-assisted machine learning in resource-constrained IoT environments for security: review and future prospective
G Kornaros - IEEE Access, 2022 - ieeexplore.ieee.org
As the Internet of Things (IoT) technology advances, billions of multidisciplinary smart
devices act in concert, rarely requiring human intervention, posing significant challenges in …
devices act in concert, rarely requiring human intervention, posing significant challenges in …
Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model
S Aljawarneh, M Aldwairi, MB Yassein - Journal of Computational Science, 2018 - Elsevier
Efficiently detecting network intrusions requires the gathering of sensitive information. This
means that one has to collect large amounts of network transactions including high details of …
means that one has to collect large amounts of network transactions including high details of …
Toward an online anomaly intrusion detection system based on deep learning
K Alrawashdeh, C Purdy - 2016 15th IEEE international …, 2016 - ieeexplore.ieee.org
In the past twenty years, progress in intrusion detection has been steady but slow. The
biggest challenge is to detect new attacks in real time. In this work, a deep learning …
biggest challenge is to detect new attacks in real time. In this work, a deep learning …
Accelerated deep neural networks for enhanced intrusion detection system
S Potluri, C Diedrich - 2016 IEEE 21st international conference …, 2016 - ieeexplore.ieee.org
Network based communication is more vulnerable to outsider and insider attacks in recent
days due to its wide spread applications in many fields. Intrusion Detection System (IDS) a …
days due to its wide spread applications in many fields. Intrusion Detection System (IDS) a …
A survey on the application of FPGAs for network infrastructure security
H Chen, Y Chen… - … Communications Surveys & …, 2010 - ieeexplore.ieee.org
Given the rapid evolution of attack methods and toolkits, software-based solutions to secure
the network infrastructure have become overburdened. The performance gap between the …
the network infrastructure have become overburdened. The performance gap between the …
Towards an energy-efficient anomaly-based intrusion detection engine for embedded systems
Nowadays, a significant part of all network accesses comes from embedded and battery-
powered devices, which must be energy efficient. This paper demonstrates that a hardware …
powered devices, which must be energy efficient. This paper demonstrates that a hardware …
Traffic analysis with off-the-shelf hardware: Challenges and lessons learned
In recent years, the progress in both hardware and software allows user-space applications
to capture packets at 10 Gb/s line rate or more, with cheap COTS hardware. However …
to capture packets at 10 Gb/s line rate or more, with cheap COTS hardware. However …
[PDF][PDF] Anomaly classification using genetic algorithm-based random forest model for network attack detection.
A Assiri - Computers, Materials & Continua, 2021 - cdn.techscience.cn
Anomaly classification based on network traffic features is an important task to monitor and
detect network intrusion attacks. Network-based intrusion detection systems (NIDSs) using …
detect network intrusion attacks. Network-based intrusion detection systems (NIDSs) using …
Network intrusion detection with a hashing based apriori algorithm using Hadoop MapReduce
Ubiquitous nature of Internet services across the globe has undoubtedly expanded the
strategies and operational mode being used by cybercriminals to perpetrate their unlawful …
strategies and operational mode being used by cybercriminals to perpetrate their unlawful …
Real-time anomaly detection for flight testing using AutoEncoder and LSTM
Flight testing is crucial in validating the functionality and safety in new commercial aircraft
design before mass production. The challenge is to support real-time analysis of high …
design before mass production. The challenge is to support real-time analysis of high …