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Exploring the Open World Using Incremental Extreme Value Machines [article]

Tobias Koch, Felix Liebezeit, Christian Riess, Vincent Christlein, Thomas Köhler
2022 arXiv   pre-print
This work introduces a modification of the widely known Extreme Value Machine (EVM) to enable open world recognition.  ...  One particular machine learning problem in dynamic environments is open world recognition.  ...  The EVM has achieved state-of-the-art results in intrusion detection [5] and open set face recognition [3] .  ... 
arXiv:2205.14892v1 fatcat:yuaj3zxzf5ctzgheb6zmx3i4di

A Survey of Stealth Malware: Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions [article]

Ethan M. Rudd, Andras Rozsa, Manuel Günther, Terrance E. Boult
2016 arXiv   pre-print
We present a formalized adaptive open world framework for stealth malware recognition and relate it mathematically to research from other machine learning domains.  ...  suggest that several flawed assumptions inherent to most recognition algorithms prevent a direct mapping between the stealth malware recognition problem and a machine learning solution.  ...  [160] formulated extreme value machine (EVM) classifiers to perform open set classification in a feature space learnt from a convolutional neural network.  ... 
arXiv:1603.06028v2 fatcat:dyyemahzjze6bltxlwnnqgeyzy

Cyberspace Security Using Adversarial Learning and Conformal Prediction

Harry Wechsler
2015 Intelligent Information Management  
Conformal prediction is the principled and unified adaptive and learning framework used to design, develop, and deploy a multi-faceted self-managing defensive shield to detect, disrupt, and deny intrusive  ...  Conformal prediction leverages apparent relationships between immunity and intrusion detection using non-conformity measures characteristic of affinity, a typicality, and surprise, to recognize patterns  ...  We also recall that both the strangeness and p-values provide the information needed for open set recognition.  ... 
doi:10.4236/iim.2015.74016 fatcat:wqiu3pkl6zeurlr3mizdahhgd4

An Improved Method to Detect Intrusion Using Machine Learning Algorithms

Urvashi Modi, Anurag Jain
2016 Informatics Engineering an International Journal  
To resolve the problems of IDS scheme this research work propose "an improved method to detect intrusion using machine learning algorithms".  ...  In our paper we use KDDCUP 99 dataset to analyze efficiency of intrusion detection with different machine learning algorithms like Bayes, NaiveBayes, J48, J48Graft and Random forest.  ...  The IDS procedure on the firewall router examines packet headers for intrusion recognition by using those 59 signatures.  ... 
doi:10.5121/ieij.2016.4203 fatcat:6yn7a6hcjffqhmyaijg2cq67zi

XFinder: Detecting Unknown Anomalies in Distributed Machine Learning Scenario

Haizhou Du, Shiwei Wang, Huan Huo
2021 Frontiers in Computer Science  
Although deep neural networks have made remarkable achievements in anomaly detection for network traffic, they mainly focus on closed sets, that is, assuming that all anomalies are known.  ...  Anomaly detection for network traffic in distributed machine learning scenarios is of great significance for network security.  ...  Zhang et al. (2020) investigated how to apply the extreme value theory (EVT) to unknown network anomaly detection systems and proposed a network intrusion detection method based on open set recognition  ... 
doi:10.3389/fcomp.2021.710384 fatcat:gd27iynrznc3zohj5ihpyt255q

A Review of Open-World Learning and Steps Toward Open-World Learning Without Labels [article]

Mohsen Jafarzadeh, Akshay Raj Dhamija, Steve Cruz, Chunchun Li, Touqeer Ahmad, Terrance E. Boult
2022 arXiv   pre-print
and use them to define seven baselines for performance on the open-world learning without labels problem.  ...  In open-world learning, an agent starts with a set of known classes, detects, and manages things that it does not know, and learns them over time from a non-stationary stream of data.  ...  Incremental open set intrusion IEEE, 2019. 8 recognition using extreme value machine.  ... 
arXiv:2011.12906v3 fatcat:psf56evv2vg63jaqwpzwgjwqhy

Integration of Fuzzy with Incremental Import Vector Machine for Intrusion Detection

Arun Kumar Ramamoorthy, K. Karuppasamy
2022 International Journal of Computers Communications & Control  
The new population is then put into the Import Vector Machine, a strong classifier that has been used to solve a wide range of pattern recognition issues.  ...  To train the I2VM classifier, FGA uses three sets of operations to produce a new set of inhabitants with distinct patterns: cross over operation, selection, and finally mutation.  ...  Related Works Bagging, boosting, hybrid ensemble, and other machine-learning (ML) methods for network intrusion recognition have widely explored.  ... 
doi:10.15837/ijccc.2022.3.4481 fatcat:mt6hugoqfnb6lgqujbgcjyzfxm

Driver Sleepiness Detection Algorithm Based on Relevance Vector Machine

Lingxiang Wei, Tianliu Feng, Pengfei Zhao, Mingjun Liao
2021 Baltic Journal of Road and Bridge Engineering  
used in this work were closely related to the fatigue state.  ...  The results show that the recognition accuracy of the RVM-driven state classifiers with different kernel functions was higher than 90%, which indicated that the mouth-opening degree and the eye state index  ...  The mouth opening degree for different states is shown in Figure 3 . In this work, the threshold value of mouth opening degree is set at 0.6.  ... 
doi:10.7250/bjrbe.2021-16.518 fatcat:wg2qkt5pbrd5jdrezalyj7d7pi

Learning and the Unknown: Surveying Steps toward Open World Recognition

T. E. Boult, S. Cruz, A.R. Dhamija, M. Gunther, J. Henrydoss, W.J. Scheirer
The past decade has seen the formalization and development of many open set algorithms, which provably bound the risk from unknown classes.  ...  As science attempts to close the gap between man and machine by building systems capable of learning, we must embrace the importance of the unknown.  ...  In (Rudd et al. 2018 ), we developed the Extreme Value Machine (EVM), a new non-linear radial basis function approach that supports both high-quality non-linear decision boundaries and efficient incremental  ... 
doi:10.1609/aaai.v33i01.33019801 fatcat:mh76vgoegvabjjnpil33e4ark4

Data Analytics in the Internet of Things: A Survey

Tausifa Jan Saleem, Mohammad Ahsan Chishti
2019 Scalable Computing : Practice and Experience  
Furthermore, open research challenges and future research opportunities are also discussed. This article can be used as a basis to foster advanced research in the arena of IoT data analytics.  ...  Data collected from these sensors can be used to comprehend, examine and control intricate environments around us, facilitating greater intelligence, smarter decision-making, and better performance.  ...  Deep learning is appropriate for modeling complex behaviours of diverse data sets and transfer learning is mostly useful for scenarios with limited data sets while as incremental learning means real-time  ... 
doi:10.12694/scpe.v20i4.1562 fatcat:y2fiya3q2bawhdg6hhczfpdbee

Using weighted Support Vector Machine to address the imbalanced classes problem of Intrusion Detection System

2018 KSII Transactions on Internet and Information Systems  
Machine Learning (ML) is one of the most important fields which have great contribution to address the intrusion detection issues.  ...  This model achieved good results of total accuracy and superior results in the small classes like the User-To-Remote and Remote-To-Local attacks using the improved version of the benchmark dataset KDDCup99  ...  There are set of open points in the network intrusion detection.  ... 
doi:10.3837/tiis.2018.10.027 fatcat:5mbwutjifzczdnuatxp3xrcgdm

Recent Advances in Open Set Recognition: A Survey [article]

Chuanxing Geng, Sheng-jun Huang, Songcan Chen
2020 arXiv   pre-print
A more realistic scenario is open set recognition (OSR), where incomplete knowledge of the world exists at training time, and unknown classes can be submitted to an algorithm during testing, requiring  ...  This paper provides a comprehensive survey of existing open set recognition techniques covering various aspects ranging from related definitions, representations of models, datasets, evaluation criteria  ...  Besides, W-SVM was further used for open set intrusion recognition on the KDDCUP'99 dataset [96] . More works on intrusion detection in open set scenario can be found in [97] .  ... 
arXiv:1811.08581v3 fatcat:4gt3ppj5ofcxto22bq4x67ap4m

A novel statistical technique for intrusion detection systems

Enamul Kabir, Jiankun Hu, Hua Wang, Guangping Zhuo
2018 Future generations computer systems  
In the second stage, least square support vector machine (LS-SVM) is applied to the extracted samples to detect intrusions.  ...  This paper proposes a novel approach for intrusion detection system based on sampling with Least Square Support Vector Machine (LS-SVM). Decision making is performed in two stages.  ...  [17] used the support vector machine for intrusion detection.  ... 
doi:10.1016/j.future.2017.01.029 fatcat:vyeq5psb5rek5bixcxvmrsmsem

Survey on Incremental Approaches for Network Anomaly Detection [article]

Monowar H. Bhuyan and D. K. Bhattacharyya and J. K. Kalita
2012 arXiv   pre-print
The technological trends, open problems, and challenges over anomaly detection using incremental approach are also discussed.  ...  System administrators can attempt to prevent such attacks using intrusion detection tools and systems. There are many commercially available signature-based Intrusion Detection Systems (IDSs).  ...  Some of them abuse a computer's legitimate features, some of them use social engineering techniques, and some of them use weaknesses of the machine. A set of probe attacks are shown in Table 4 .  ... 
arXiv:1211.4493v2 fatcat:vqmysyr2fnfy3bjismgtt4pjku

Intrusion Detection System on Big data using Deep Learning Techniques

In this paper, the detailed review has been done on intrusion detection on various fields using deep learning and gives an idea of applications of deep learning.  ...  Using traditional techniques to detect attacks is very difficult.  ...  In this paper, Authors proposed "Extreme Learning Machine algorithm for ELM, MR ELM", to solve big data problems.  ... 
doi:10.35940/ijitee.d2011.029420 fatcat:t4woonejwzd3njpqix5o42uf5q
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