Zobrazeno 1 - 10
of 871
pro vyhledávání: '"Korba, P."'
This work considers the problem of sampling from a probability distribution known up to a normalization constant while satisfying a set of statistical constraints specified by the expected values of general nonlinear functions. This problem finds app
Externí odkaz:
http://arxiv.org/abs/2411.00568
Geometric tempering is a popular approach to sampling from challenging multi-modal probability distributions by instead sampling from a sequence of distributions which interpolate, using the geometric mean, between an easier proposal distribution and
Externí odkaz:
http://arxiv.org/abs/2410.09697
Autor:
Chen, Zonghao, Mustafi, Aratrika, Glaser, Pierre, Korba, Anna, Gretton, Arthur, Sriperumbudur, Bharath K.
We introduce a (de)-regularization of the Maximum Mean Discrepancy (DrMMD) and its Wasserstein gradient flow. Existing gradient flows that transport samples from source distribution to target distribution with only target samples, either lack tractab
Externí odkaz:
http://arxiv.org/abs/2409.14980
In this paper, we study the statistical and geometrical properties of the Kullback-Leibler divergence with kernel covariance operators (KKL) introduced by Bach [2022]. Unlike the classical Kullback-Leibler (KL) divergence that involves density ratios
Externí odkaz:
http://arxiv.org/abs/2408.16543
In recent years, numerous large-scale cyberattacks have exploited Internet of Things (IoT) devices, a phenomenon that is expected to escalate with the continuing proliferation of IoT technology. Despite considerable efforts in attack detection, intru
Externí odkaz:
http://arxiv.org/abs/2408.14045
Autor:
korba, Abdelaziz Amara, Sebaa, Souad, Mabrouki, Malik, Ghamri-Doudane, Yacine, Benatchba, Karima
The introduction of 6G technology into the Internet of Vehicles (IoV) promises to revolutionize connectivity with ultra-high data rates and seamless network coverage. However, this technological leap also brings significant challenges, particularly f
Externí odkaz:
http://arxiv.org/abs/2407.15700
In the rapidly evolving landscape of cyber threats targeting the Internet of Things (IoT) ecosystem, and in light of the surge in botnet-driven Distributed Denial of Service (DDoS) and brute force attacks, this study focuses on the early detection of
Externí odkaz:
http://arxiv.org/abs/2407.15688
Intrusion Detection Systems (IDS) play a crucial role in ensuring the security of computer networks. Machine learning has emerged as a popular approach for intrusion detection due to its ability to analyze and detect patterns in large volumes of data
Externí odkaz:
http://arxiv.org/abs/2407.05766
The Advanced Metering Infrastructure (AMI) is one of the key components of the smart grid. It provides interactive services for managing billing and electricity consumption, but it also introduces new vectors for cyberattacks. Although, the devastati
Externí odkaz:
http://arxiv.org/abs/2407.03264
Autor:
korba, Abdelaziz Amara, Boualouache, Abdelwahab, Brik, Bouziane, Rahal, Rabah, Ghamri-Doudane, Yacine, Senouci, Sidi Mohammed
Deploying Connected and Automated Vehicles (CAVs) on top of 5G and Beyond networks (5GB) makes them vulnerable to increasing vectors of security and privacy attacks. In this context, a wide range of advanced machine/deep learning based solutions have
Externí odkaz:
http://arxiv.org/abs/2407.03070