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  2. ELKI - Wikipedia

    en.wikipedia.org/wiki/ELKI

    Version 0.7.5 (February 2019) adds additional clustering algorithms, anomaly detection algorithms, evaluation measures, and indexing structures. [ 18 ] Version 0.8 (October 2022) adds automatic index creation, garbage collection, and incremental priority search, as well as many more algorithms such as BIRCH .

  3. Caffe (software) - Wikipedia

    en.wikipedia.org/wiki/Caffe_(software)

    Caffe supports many different types of deep learning architectures geared towards image classification and image segmentation.It supports CNN, RCNN, LSTM and fully-connected neural network designs. [8]

  4. OSSEC - Wikipedia

    en.wikipedia.org/wiki/OSSEC

    It provides intrusion detection for most operating systems, including Linux, OpenBSD, FreeBSD, OS X, Solaris and Windows. OSSEC has a centralized, cross-platform architecture allowing multiple systems to be easily monitored and managed. [2] OSSEC has a log analysis engine that is able to correlate and analyze logs from multiple devices and ...

  5. MindSpore - Wikipedia

    en.wikipedia.org/wiki/MindSpore

    On April 24, 2024, Huawei's MindSpore 2.3.RC1 was released to open source community with Foundation Model Training, Full-Stack Upgrade of Foundation Model Inference, Static Graph Optimization, IT Features and new MindSpore Elec MT (MindSpore-powered magnetotelluric) Intelligent Inversion Model.

  6. Snort (software) - Wikipedia

    en.wikipedia.org/wiki/Snort_(software)

    Snort is a free open source network intrusion detection system (IDS) and intrusion prevention system (IPS) [4] created in 1998 by Martin Roesch, founder and former CTO of Sourcefire. [ 5 ] [ 6 ] Snort is now developed by Cisco , which purchased Sourcefire in 2013.

  7. ML.NET - Wikipedia

    en.wikipedia.org/wiki/ML.NET

    ML.NET is a free software machine learning library for the C# and F# programming languages. [4] [5] [6] It also supports Python models when used together with NimbusML.The preview release of ML.NET included transforms for feature engineering like n-gram creation, and learners to handle binary classification, multi-class classification, and regression tasks. [7]

  8. Anomaly detection - Wikipedia

    en.wikipedia.org/wiki/Anomaly_detection

    Anomaly detection for IDS is normally accomplished with thresholds and statistics, but can also be done with soft computing, and inductive learning. [7] Types of features proposed by 1999 included profiles of users, workstations, networks, remote hosts, groups of users, and programs based on frequencies, means, variances, covariances, and ...

  9. Network behavior anomaly detection - Wikipedia

    en.wikipedia.org/wiki/Network_Behavior_Anomaly...

    Network behavior anomaly detection (NBAD) is a security technique that provides network security threat detection. It is a complementary technology to systems that detect security threats based on packet signatures. [1] NBAD is the continuous monitoring of a network for unusual events or trends.