Karim Khamaisi

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Khamaisi

First name

Karim

karim.khamaisi@unisg.ch

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Distributed Pulse-Wave Simulator for DDoS Dataset Generation
(2026-09-02)
Karim Khamaisi
;
Pascal Kiechl
;
Katharina Müller
;
Stiller, Burkhard
;
Bruno Rodrigues
Type:conference paper
URL:https://arxiv.org/pdf/2511.12774
DOI:10.1109/NOMS69089.2026.11668114
URI:https://alexandria.unisg.ch/handle/20.500.14171/126261
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OpenCSI: Self-Calibration Layer for Heterogeneous Mesh Wireless Sensor Networks
(IEEE, 2026-09-07)
Khamaisi, Karim
;
Sigg, Simon
;
Rodrigues, Bruno
Type:conference paper
Journal:2026 IEEE 51st Conference on Local Computer Networks (LCN)
DOI:10.1109/LCN67947.2026.11660791
URI:https://alexandria.unisg.ch/handle/20.500.14171/137316
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Moving Target Offense: RL-Based Adaptive Evasion Strategies for C2 Frameworks
(2025-07-23)
Karim Khamaisi
;
Anton Crazzolara
;
Aleksandar Ristic
;
Samuel Brugger
;
Bruno Rodrigues
Modern Intrusion Detection Systems (IDS) that rely on signature-based detection struggle to detect novel threats like AI-based Command-and-Control (C2) frameworks, highlighting a gap in the ability of current systems to detect and mitigate emerging botnet threats. This work addresses this gap by investigating NimPlant's detection and evasion capabilities. This paper (1) shows strategies to detect NimPlant bots using network traffic analysis, (2) enhances their evasion capabilities using Reinforcement Learning (RL), and (3) evaluates AI-driven evasion against IDS. Using a controlled testing environment, an infected device is simulated by implementing evasion strategies and integrating them into an RL-based AI system. Herewith, AI significantly improves evasion, with the RL-enhanced NimPlant achieving higher detection bypass rates. However, the IDS configuration heavily impacts AI effectiveness, underscoring the need for robust security setups. Thus, recommendations are provided for detecting bot infections and countering AI-enhanced botnets.
Type:conference paper
URL:https://ieeexplore.ieee.org/abstract/document/11146346/
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/123178
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From Noise to Knowledge: A Comparative Study of Acoustic Anomaly Detection Models in Pumped-storage Hydropower Plants
(2025)
Karim Khamaisi
;
Keller, Nicolas
;
Stefan Krummenacher
;
Valentin Huber
;
Fäßler, Bernhard
In the context of industrial factories and energy producers, unplanned outages are highly costly and difficult to service. However, existing acoustic-anomaly detection studies largely rely on generic industrial or synthetic datasets, with few focused on hydropower plants due to limited access. This paper presents a comparative analysis of acoustic-based anomaly detection methods, as a way to improve predictive maintenance in hydropower plants. We address key challenges in the acoustic preprocessing under highly noisy conditions before extracting time- and frequency-domain features. Then, we benchmark three machine learning models: LSTM AE, K-Means, and OC-SVM, which are tested on two real-world datasets from the Rodundwerk II pumped-storage plant in Austria, one with induced anomalies and one with real-world conditions. The One-Class SVM achieved the best trade-off of accuracy (ROC AUC 0.966-0.998) and minimal training time, while the LSTM autoencoder delivered strong detection (ROC AUC 0.889-0.997) at the expense of higher computational cost.
Type:conference paper
URL:http://arxiv.org/abs/2509.22881
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/123675
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Bridging Technical Capability and User Accessibility: Off-grid Civilian Emergency Communication
(2025-11-19)
Karim Khamaisi
;
Oliver Kamer
;
Bruno Rodrigues
;
Jan von der Assen
;
Burkhard Stiller
Type:conference paper
URL:https://dl.acm.org/doi/10.1145/3770501.3770505
DOI:10.1145/3770501.3770505
URI:https://www.alexandria.unisg.ch/handle/20.500.14171/123676

Karim Khamaisi

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