Výsledky vyhledávání

Report

Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network

Autor: Nakıp, Mert, Çopur, Onur, Biyik, Emrah, Güzeliş, Cüneyt

Publikováno v: Nak{\i}p, M., \c{C}opur, O., Biyik, E., & G\"{u}zeli\c{s}, C. (2023). Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network. Applied Energy, 340, 121014

Smart home energy management systems help the distribution grid operate more efficiently and reliably, and enable effective penetration of distributed renewable energy sources. These systems rely on robust forecasting, optimization, and control/sched

Externí odkaz: http://arxiv.org/abs/2307.01622

Zobrazit plný text záznamu

Report

Online Self-Supervised Deep Learning for Intrusion Detection Systems

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

Zobrazit plný text záznamu

Report

Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection

Autor: Nakıp, Mert, Gül, Baran Can, Gelenbe, Erol

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

Zobrazit plný text záznamu

Report

Measurement Based Evaluation and Mitigation of Flood Attacks on a LAN Test-Bed

Autor: Nasereddin, Mohammed, Nakıp, Mert, Gelenbe, Erol

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

Zobrazit plný text záznamu

Report

Associated Random Neural Networks for Collective Classification of Nodes in Botnet Attacks

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

Zobrazit plný text záznamu

Report

Real-Time Cyberattack Detection with Offline and Online Learning

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

Zobrazit plný text záznamu

Report

Engagement Detection with Multi-Task Training in E-Learning Environments

Autor: Copur, Onur, Nakıp, Mert, Scardapane, Simone, Slowack, Jürgen

Recognition of user interaction, in particular engagement detection, became highly crucial for online working and learning environments, especially during the COVID-19 outbreak. Such recognition and detection systems significantly improve the user ex

Externí odkaz: http://arxiv.org/abs/2204.04020

Zobrazit plný text záznamu