Layered Approach using Conditional Random Fields For Intrusion Detection

Dec 19, 2023

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This paper addresses the challenges of accuracy and efficiency in intrusion detection using Conditional Random Fields and a layered approach. It demonstrates that high attack detection accuracy can be achieved while maintaining high efficiency. The system allows for flexibility in the number of layers, catering to different environments. Future research areas include feature extraction for signature-based systems.

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Layered Approach using Conditional Random Fields For Intrusion Detection

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Conditional Random Fields

Conditional Random Fields. A form of discriminative modelling Has been used successfully in various domains such as part of speech tagging and other Natural Language Processing tasks Processes evidence bottom-up Combines multiple features of the data

338 views • 14 slides

Hidden Conditional Random Fields

Hidden Conditional Random Fields. Asela Gunawardana, Milind Mahajan, Alex Acero, John C. Platt Microsoft Research. reference. [ INTERSPEECH 2005 ] Asela Gunawardana, Milind Mahajan, Alex Acero, John C. Platt, “Hidden Conditional Random Fields for Phone Classification”

519 views • 22 slides

Combining Phonetic Attributes Using Conditional Random Fields

/sh/. /sh/. /iy/. /iy/. /iy/. X 1. X 2. X 3. X 4. X 5. FIGURE 1: Spectrogram of the word /she/, shown with phonetic boundaries and corresponding phonetic attributes for each phone. Attributes are listed descending in the same order that they appear in Table 1.

159 views • 1 slides

Network Intrusion Detection Using Random Forests

PST2005. Jiong Zhang and Mohammad Zulkernine. 2. Outline. MotivationIntrusion detection systemData mining meets intrusion detectionProposed architectureChallenges and solutionsExperimental resultsConclusion and future work. PST2005. Jiong Zhang and Mohammad Zulkernine. 3. Motivation. Intrusion

402 views • 19 slides

Conditional Random Fields for ASR

Conditional Random Fields for ASR. Jeremy Morris 1 1/23/2009. Outline. Background Maximum Entropy models and CRFs CRF Example ASR experiments with CRFs. Background. Conditional Random Fields (CRFs) Discriminative probabilistic sequence model

680 views • 50 slides

Conditional Random Fields

Conditional Random Fields. Advanced Statistical Methods in NLP Ling 572 February 9, 2012. Roadmap. Graphical Models Modeling independence Models revisited Generative & discriminative models Conditional random fields Linear chain models Skip chain models. Preview.

1.36k views • 102 slides

Conditional Random Fields

Conditional Random Fields. Sequence Labeling: The Problem. Given a sequence (in NLP, words), assign appropriate labels to each word. For example, POS tagging:. DT. NN. VBD. IN. DT. NN. The cat sat on the mat. Sequence Labeling: The Problem.

677 views • 47 slides

Markov Random Fields & Conditional Random Fields

Markov Random Fields & Conditional Random Fields. John Winn MSR Cambridge. Road map. Markov Random Fields What they are Uses in vision/object recognition Advantages Difficulties Conditional Random Fields What they are Further difficulties.  12.  23. X 1. X 2. X 3.  234. X 4.

449 views • 14 slides

Conditional Random Fields for ASR

Conditional Random Fields for ASR. Jeremy Morris July 25, 2006. Overview. Problem Statement (Motivation) Conditional Random Fields Experiments Attribute Selection Experimental Setup Results Future Work. Problem Statement. Developed as part of the ASAT Project

387 views • 21 slides

Conditional Random Fields

Representation. Probabilistic Graphical Models. Markov Networks. Conditional Random Fields. Motivation. Observed variables X Target variables Y. CRF Representation. CRFs and Logistic Model. CRFs for Language.

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Conditional Topic Random Fields

Conditional Topic Random Fields. Jun Zhu and Eric P. Xing (@ cs.cmu.edu ) ICML 2010 Presentation and Discussion by Eric Wang January 12, 2011. Overview. Introduction – nontrivial input features for text. Conditional Random Fields CdTM and CTRF Model Inference Experimental Results.

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Conditional Random Fields

Conditional Random Fields. Probabilistic Graphical Models (10-708) Ramesh Nallapati. Y 1. Y 2. …. …. …. Y n. X 1. X 2. …. …. …. X n. Motivation: Shortcomings of Hidden Markov Model. HMM models direct dependence between each state and only its corresponding observation

545 views • 26 slides

Network Intrusion Detection Using Random Forests

Network Intrusion Detection Using Random Forests. Jiong Zhang Mohammad Zulkernine School of Computing Queen's University Kingston, Ontario, Canada. Outline. Motivation Intrusion detection system Data mining meets intrusion detection Proposed architecture Challenges and solutions

456 views • 19 slides

Conditional Random Fields

Conditional Random Fields. Presented by Shira Kritchman & Lena Gorelick May 13, 2007. Advanced Topics in Computer and Human Vision Spring 2007. Outline. Introduction Statistical Modeling Generative vs. Discriminative Models Naïve Bayes vs. Logistic Regression Sequence Modeling: HMM

1.19k views • 86 slides

Combining Phonetic Attributes Using Conditional Random Fields

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Conditional Random Fields

Conditional Random Fields. William W. Cohen CALD. Announcements. Upcoming assignments: Today: Sha & Pereira, Lafferty et al Mon 2/23: Klein & Manning, Toutanova et al Wed 2/25: no writeup due Mon 3/1: no writeup due Wed 3/3: project proposal due: personnel + 1-2 page

543 views • 32 slides

Bayesian Conditional Random Fields using Power EP

Bayesian Conditional Random Fields using Power EP. Tom Minka Joint work with Yuan Qi and Martin Szummer. Why should you care?. New way to train Conditional Random Fields Significant improvement on small training sets Demonstration of Bayesian methods

347 views • 20 slides

Conditional Random Fields

Conditional Random Fields. A brief description. 1. 1. 1. 1. …. 2. 2. 2. 2. …. …. …. …. …. K. K. K. K. …. HMMs and CRFs. 1. Features used in objective function P(x,  ) for an HMM: a kl , e k (b), where b  

587 views • 44 slides

Conditional Random Fields

Conditional Random Fields. Jie Tang KEG, DCST, Tsinghua 24, Nov, 2005. Sequence Labeling. Pos Tagging E.g. [He/PRP] [reckons/VBZ] [the/DT] [current/JJ] [account/NN] [deficit/NN] [will/MD] [narrow/VB] [to/TO] [only/RB] [#/#] [1.8/CD] [billion/CD] [in/IN] [September/NNP] [./.] Term Extraction

487 views • 26 slides

Bayesian Conditional Random Fields using Power EP

209 views • 20 slides

Table Extraction Using Conditional Random Fields

Table Extraction Using Conditional Random Fields. D. Pinto, A. McCallum, X. Wei and W. Bruce Croft - on SIGIR03 - Presented by Vitor R. Carvalho March 15 th 2004. Warm up. Why table extraction? Applications: Question-Answering, data mining and IR

140 views • 11 slides

Conditional Random Fields

350 views • 32 slides

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Layered Approach using Conditional Random Fields For Intrusion Detection

Layered Approach using Conditional Random Fields For Intrusion Detection

Presentation Transcript

Conditional Random Fields

Hidden Conditional Random Fields

Combining Phonetic Attributes Using Conditional Random Fields

Network Intrusion Detection Using Random Forests

Conditional Random Fields for ASR

Conditional Random Fields

Conditional Random Fields

Markov Random Fields &amp; Conditional Random Fields

Conditional Random Fields for ASR

Conditional Random Fields

Conditional Topic Random Fields

Conditional Random Fields

Network Intrusion Detection Using Random Forests

Conditional Random Fields

Combining Phonetic Attributes Using Conditional Random Fields

Conditional Random Fields

Bayesian Conditional Random Fields using Power EP

Conditional Random Fields

Conditional Random Fields

Bayesian Conditional Random Fields using Power EP

Table Extraction Using Conditional Random Fields

Conditional Random Fields

Markov Random Fields & Conditional Random Fields