Results 131 to 140 of about 404,854 (339)
Improving Transferability of Network Intrusion Detection in a Federated Learning Setup [PDF]
arXivNetwork Intrusion Detection Systems (IDS) aim to detect the presence of an intruder by analyzing network packets arriving at an internet connected device. Data-driven deep learning systems, popular due to their superior performance compared to traditional IDS, depend on availability of high quality training data for diverse intrusion classes.
arxiv
Balancing cooperation and risk in intrusion detection [PDF]
, 2000 Deborah Frincke
openalex +1 more source
Molecular Oncology, EarlyView.Epithelial–mesenchymal transition (EMT) and tumor‐infiltrating lymphocytes (TILs) are associated with early breast cancer response to neoadjuvant chemotherapy (NAC). This study evaluated EMT and TIL shifts, with immunofluorescence and RNA sequencing, at diagnosis and in residual tumors as potential biomarkers associated with treatment response.
Françoise Derouane +16 more
wiley +1 more source
A Case Study on Using Deep Learning for Network Intrusion Detection [PDF]
arXiv, 2019 Deep Learning has been very successful in many application domains. However, its usefulness in the context of network intrusion detection has not been systematically investigated. In this paper, we report a case study on using deep learning for both supervised network intrusion detection and unsupervised network anomaly detection.
arxiv
Mining TCP/IP Traffic for Network Intrusion Detection by Using a Distributed Genetic Algorithm [PDF]
, 2000 Filippo Neri
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Molecular and functional profiling unravels targetable vulnerabilities in colorectal cancer
Molecular Oncology, EarlyView.We used whole exome and RNA‐sequencing to profile divergent genomic and transcriptomic landscapes of microsatellite stable (MSS) and microsatellite instable (MSI) colorectal cancer. Alterations were classified using a computational score for integrative cancer variant annotation and prioritization.
Efstathios‐Iason Vlachavas +15 more
wiley +1 more source
Classification of artificial intelligence ids for smurf attack [PDF]
IJAIA (2012);, 2012 Many methods have been developed to secure the network infrastructure and communication over the Internet. Intrusion detection is a relatively new addition to such techniques. Intrusion detection systems (IDS) are used to find out if someone has intrusion into or is trying to get it the network.
arxiv
Molecular Oncology, EarlyView.Cancer stem cells are associated with aggressive disease, but a deep characterization of such markers is lacking in endometrial cancer. This study uses imaging mass cytometry to explore putative cancer stem cell markers in endometrial tumors and corresponding organoid models.
Hilde E. Lien +7 more
wiley +1 more source
Data Mining Approaches for Intrusion Detection [PDF]
, 2000 Wenke Lee, Salvatore J. Stolfo
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Peripheral blood proteome biomarkers distinguish immunosuppressive features of cancer progression
Molecular Oncology, EarlyView.Immune status significantly influences cancer progression. This study used plasma proteomics to analyze benign 67NR and malignant 4T1 breast tumor models at early and late tumor stages. Immune‐related proteins–osteopontin (Spp1), lactotransferrin (Ltf), calreticulin (Calr) and peroxiredoxin 2 (Prdx2)–were associated with systemic myeloid‐derived ...
Yeon Ji Park +6 more
wiley +1 more source