Zobrazeno 1 - 10
of 719
pro vyhledávání: '"Gelenbe, Erol"'
Major challenges of assisting passengers to safely and quickly escape from ships when an emergency occurs, include complex realistic features such as human behavior uncertainty, dynamic human traversal times, and the computation and communication del
Externí odkaz:
http://arxiv.org/abs/2306.14241
Autor:
Nakıp, Mert, Gelenbe, Erol
Publikováno v:
Nak{\i}p, M., & Gelenbe, E. (2024). Online Self-Supervised Deep Learning for Intrusion Detection Systems. IEEE Transactions on Information Forensics and Security
This paper proposes a novel Self-Supervised Intrusion Detection (SSID) framework, which enables a fully online Deep Learning (DL) based Intrusion Detection System (IDS) that requires no human intervention or prior off-line learning. The proposed fram
Externí odkaz:
http://arxiv.org/abs/2306.13030
Cyberattacks are increasingly threatening networked systems, often with the emergence of new types of unknown (zero-day) attacks and the rise of vulnerable devices. While Machine Learning (ML)-based Intrusion Detection Systems (IDSs) have been shown
Externí odkaz:
http://arxiv.org/abs/2306.13029
Autor:
Gelenbe, Erol, Nasereddin, Mohammed
IoT Servers that receive and process packets from IoT devices should meet the QoS needs of incoming packets, and support Attack Detection software that analyzes the incoming traffic to identify and discard packets that may be part of a Cyberattack. S
Externí odkaz:
http://arxiv.org/abs/2306.11007
The IoT is vulnerable to network attacks, and Intrusion Detection Systems (IDS) can provide high attack detection accuracy and are easily installed in IoT Servers. However, IDS are seldom evaluated in operational conditions which are seriously impair
Externí odkaz:
http://arxiv.org/abs/2305.10565
Autor:
Gelenbe, Erol, Nakıp, Mert
Botnet attacks are a major threat to networked systems because of their ability to turn the network nodes that they compromise into additional attackers, leading to the spread of high volume attacks over long periods. The detection of such Botnets is
Externí odkaz:
http://arxiv.org/abs/2303.13627
Autor:
Gelenbe, Erol, Nakıp, Mert
This paper presents several novel algorithms for real-time cyberattack detection using the Auto-Associative Deep Random Neural Network, which were developed in the HORIZON 2020 IoTAC Project. Some of these algorithms require offline learning, while o
Externí odkaz:
http://arxiv.org/abs/2303.11760
Classical Random Neural Networks (RNNs) have demonstrated effective applications in decision making, signal processing, and image recognition tasks. However, their implementation has been limited to deterministic digital systems that output probabili
Externí odkaz:
http://arxiv.org/abs/2203.01764
Publikováno v:
In Computers & Industrial Engineering October 2024 196
