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IEEE Latin-American Conference on Communications (LATINCOM)

作者： Oliveira, Junia Maisa Almeida, Jonatan Macedo, Daniel Nogueira, Jose Marcos Univ Fed Minas Gerais Comp Sci Dept Belo Horizonte MG Brazil Univ Fed Minas Gerais Elect Engn Dept Belo Horizonte MG Brazil

ISBN: (纸本)9798350326871

The prevalence of cyberattacks has been increasing annually, emphasizing the need for data-driven approaches for the prevention and mitigation of attacks. Analysis of network data facilitates the acquisition of usage information, thereby enabling the detection of anomalous behavior that deviates from established patterns. The identification of such anomalies may indicate potential threats to the network and may assist security management systems in preempting and mitigating potential threats. The present study employs unsupervised machine learning algorithms with dimensionality reduction to detect anomalies in the NSL-KDD network dataset. The data is labeled although the real network datasets are unlabeled. We defined and applied a methodology to detect anomalies in the dataset. In the study, we used the following unsupervised algorithms: Local Outlier Factor (LOF), Elliptic Envelope (EE), SGD One Class SVM (SGD) and Isolation Forest (IF). For the kind of data we analysed, the unsupervised learning algorithm that obtained the highest AUC and F1-score evaluation rate was the Elliptic Envelope.

关键词： unsupervised learning algorithms network attack anomaly detection

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Smart Solution for Energy Communities: Integrating Demand Response and unsupervised learning Evaluation Metrics 22

Smart Solution for Energy Communities: Integrating Demand Re...

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IEEE 22nd Mediterranean Electrotechnical Conference (MELECON)

作者： Barreto, Ruben Gomes, Luis Vale, Zita GECAD Porto Portugal LASI Porto Portugal Polytech Porto Porto Portugal

ISBN: (纸本)9798350387032;9798350387025

The reduction of CO2 emissions is a critical imperative in the pursuit of sustainable energy solutions. A viable avenue for mitigating CO2 emissions within the power sector is adopting energy communities, which empowers communities to achieve decentralization and sustainability. Furthermore, the convergence of energy communities and machine learning algorithms represents a crucial boundary in developing contemporary energy systems. In this context, this paper implements a demand response-based model to balance the energy community's consumption and generation, considering the support of unsupervised learning algorithms and the active participation of the members in the planned demand response events. The planning of a demand response event is based on a ranking obtained by a set of unsupervised learning evaluation metrics that evaluate the members' data. This work's novelty consists of analyzing the impact of these metrics on the ranking of members and, consequently, on the demand response event's planning. For that, different combinations of these metrics will be considered during the members' ranking. The reliability of this approach was assessed using an energy community consisting of 50 buildings. The results show that an energy community could reduce 1.1 kg of CO2 emissions and increase sustainability by 13% through implementing the presented model.

关键词： Active Participation Demand Response Energy Community unsupervised learning algorithms

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A novel recursive algorithm used to model hardware programmable neighborhood mechanism of self-organizing neural networks

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APPLIED MATHEMATICS AND COMPUTATION 2015年 267卷 314-328页

作者： Kolasa, Marta Talaska, Tomasz Dlugosz, Rafal UTP Univ Sci & Technol Fac Telecommun Comp Sci & Elect Engn PL-85796 Bydgoszcz Poland Swiss Fed Inst Technol Lausanne Inst Microtechnol CH-2000 Neuchatel Switzerland

In this paper we propose a novel recursive algorithm that models the neighborhood mechanism, which is commonly used in self-organizing neural networks (NNs). The neighborhood can be viewed as a map of connections between particular neurons in the NN. Its relevance relies on a strong reduction of the number of neurons that remain inactive during the learning process. Thus it substantially reduces the quantization error that occurs during the learning process. This mechanism is usually difficult to implement, especially if the NN is realized as a specialized chip or in Field Programmable Gate Arrays (FPGAs). The main challenge in this case is how to realize a proper, collision-free, multi-path data flow of activations signals, especially if the neighborhood range is large. The proposed recursive algorithm allows for a very efficient realization of such mechanism. One of major advantages is that different learning algorithms and topologies of the NN are easily realized in one simple function. An additional feature is that the proposed solution accurately models hardware implementations of the neighborhood mechanism. (C) 2015 Elsevier Inc. All rights reserved.

关键词： unsupervised learning algorithms Recursive neighborhood mechanism Topology of neural network Software and hardware implementations

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Neural methods based on modified reputation rules for detection and identification of intrusion attacks in wireless ad hoc sensor networks

Neural methods based on modified reputation rules for detect...

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Conference on Evolutionary and Bio-Inspired Computation - Theory and Applications IV

作者： Hortos, William S. ACERT Orlando FL 32819 USA

ISBN: (纸本)9780819481689

Determining methods to secure the process of data fusion against attacks by compromised nodes in wireless sensor networks (WSNs) and to quantify the uncertainty that may exist in the aggregation results is a critical issue in mitigating the effects of intrusion attacks. Published research has introduced the concept of the trustworthiness (reputation) of a single sensor node. Reputation is evaluated using an information-theoretic concept, the Kullback-Leibler (KL) distance. Reputation is added to the set of security features. In data aggregation, an opinion, a metric of the degree of belief, is generated to represent the uncertainty in the aggregation result. As aggregate information is disseminated along routes to the sink node(s), its corresponding opinion is propagated and regulated by Josang's belief model. By applying subjective logic on the opinion to manage trust propagation, the uncertainty inherent in aggregation results can be quantified for use in decision making. The concepts of reputation and opinion are modified to allow their application to a class of dynamic WSNs. Using reputation as a factor in determining interim aggregate information is equivalent to implementation of a reputation-based security filter at each processing stage of data fusion, thereby improving the intrusion detection and identification results based on unsupervised techniques. In particular, the reputation-based version of the probabilistic neural network (PNN) learns the signature of normal network traffic with the random probability weights normally used in the PNN replaced by the trust-based quantified reputations of sensor data or subsequent aggregation results generated by the sequential implementation of a version of Josang's belief model. A two-stage, intrusion detection and identification algorithm is implemented to overcome the problems of large sensor data loads and resource restrictions in WSNs. Performance of the two-stage algorithm is assessed in simulations of WSN sce

关键词： Cross-layer features intrusion detection and identification Josang's belief model Kullback-Leibler (KL) distance mobile software agents recurrent neural networks reputation quantification unsupervised learning algorithms wireless ad hoc sensor networks

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Early Prediction of Diabetic Using Data Mining

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SN Computer Science 2023年第2期4卷 1-7页

作者： Jaber, Fayzeh Abdulkareem James, Joy Winston University of Technology Bahrain Salmabad Manama Bahrain

The World Health Organization (WHO) reports that in 2018, 422 million people throughout the globe are living with diabetes, making it one of the most widespread chronic life-threatening conditions. Early diagnosis is often favoured for clinically relevant findings due to the comparatively longer asymptomatic period associated with diabetes. It is estimated that around 50% of people with diabetes go undiagnosed because of the length of time it takes for symptoms to appear. The appropriate evaluation of both common and less common sign symptoms, which may be present at various times between the onset of the illness and diagnosis, is essential for early detection of diabetes. Researchers have relied heavily on data mining-based categorization algorithms for illness risk prediction models. To estimate a person’s risk of developing diabetes, it is required to have access to data on people who have recently developed diabetes or who are at high risk of developing diabetes. A dataset of 768 instances was provided to us via Kaggle and was created by the National Institute of Diabetes and Digestive and Kidney Diseases. This set of examples was narrowed down from a bigger database using a variety of criteria. All our female patients are at least 21 years old and are indigenous Pima. We performed statistical analysis on the dataset using the Naïve–Bayes Algorithm, the Logistic Regression Algorithm, and the Random Forest Algorithm. We found that Random Forest provided the best accuracy for this dataset when evaluated using both ten-fold Cross- Validation and the percentage split method. The National Institute of Diabetes and Digestive and Kidney Diseases is the original source of this data. The goal is to diagnose a patient and then forecast whether they have diabetes based on those results. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Data mining Dataset Diabetes risk Evaluation model Mining tools Supervised learning algorithms Symptom unsupervised learning algorithms

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Analysis of Autoencoders for Network Intrusion Detection

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SENSORS 2021年第13期21卷 4294页

作者： Song, Youngrok Hyun, Sangwon Cheong, Yun-Gyung Sungkyunkwan Univ Dept AI Suwon 16419 Gyeonggi Do South Korea Myongji Univ Dept Comp Engn Yongin 17058 Gyeonggi Do South Korea

As network attacks are constantly and dramatically evolving, demonstrating new patterns, intelligent Network Intrusion Detection Systems (NIDS), using deep-learning techniques, have been actively studied to tackle these problems. Recently, various autoencoders have been used for NIDS in order to accurately and promptly detect unknown types of attacks (i.e., zero-day attacks) and also alleviate the burden of the laborious labeling task. Although the autoencoders are effective in detecting unknown types of attacks, it takes tremendous time and effort to find the optimal model architecture and hyperparameter settings of the autoencoders that result in the best detection performance. This can be an obstacle that hinders practical applications of autoencoder-based NIDS. To address this challenge, we rigorously study autoencoders using the benchmark datasets, NSL-KDD, IoTID20, and N-BaIoT. We evaluate multiple combinations of different model structures and latent sizes, using a simple autoencoder model. The results indicate that the latent size of an autoencoder model can have a significant impact on the IDS performance.

关键词： IDS NIDS ML-NIDS autoencoders deep-learning models model design unsupervised learning algorithms IoT

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Verifying the proximity and size hypothesis for self-organizing maps

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JOURNAL OF MANAGEMENT INFORMATION SYSTEMS 1999年第3期16卷 57-70页

作者： Lin, CT Chen, HC Nunamaker, JF Univ Arizona Artificial Intelligence Lab Tucson AZ 85721 USA

The Kohonen Self-Organizing Mag (SOM) is an unsupervised learning technique for summarizing high-dimensional data so that similar inputs are, in general, mapped close to one another. When applied to textual data, SOM has been shown to be able to group together related concepts in a data collection and to present major topics within the collection with larger regions. This article presents research in which we sought to validate these properties of SOM, called the Proximity and Size Hypotheses, through a user evaluation study. Building upon our previous research in automatic concept generation and classification, we demonstrated that the Kohonen SOM was able to perform concept clustering effectively, based on its concept precision and recall7 scores as judged by human experts. We also demonstrated a positive relationship between the size of an SOM region and the number of documents contained in the region. We believe this research has established the Kohonen SOM algorithm as an intuitively appearing and promising neural-network-based textual classification technique for addressing part of the longstanding "information overload" problem.

关键词： document clustering techniques experimental research group support systems self-organizing maps unsupervised learning algorithms

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HIGH SPEED DEEP NETWORKS BASED ON DISCRETE COSINE TRANSFORMATION

HIGH SPEED DEEP NETWORKS BASED ON DISCRETE COSINE TRANSFORMA...

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IEEE International Conference on Image Processing

作者： Xiaoyi Zou Xiangmin Xu Chunmei Qing Xiaofen Xing Sch. of Electron. & Inf. Eng. South China Univ. of Technol. Guangzhou China

ISBN: (纸本)9781479957521

The traditional deep networks take raw pixels of data as input, and automatically learn features using unsupervised learning algorithms. In this configuration, in order to learn good features, the networks usually have multi-layer and many hidden units which lead to extremely high training time costs. As a widely used image compression algorithm, Discrete Cosine Transformation (DCT) is utilized to reduce image information redundancy because only a limited number of the DCT coefficients can preserve the most important image information. In this paper, it is proposed that a novel framework by combining DCT and deep networks for high speed object recognition system. The use of a small subset of DCT coefficients of data to feed into a 2-layer sparse auto-encoders instead of raw pixels. Because of the excellent decorrelation and energy compaction properties of DCT, this approach is proved experimentally not only efficient, but also it is a computationally attractive approach for processing high-resolution images in a deep architecture.

关键词： data compression discrete cosine transforms image coding image resolution object recognition unsupervised learning 2-layer sparse auto-encoders DCT discrete cosine transformation high speed deep networks high speed object recognition system image compression algorithm unsupervised learning algorithms Accuracy Computer architecture Discrete cosine transforms Educational institutions Training Vectors Discrete Cosine Transformation deep networks high speed discrete cosine transforms image resolution object recognition image compression algorithm DCT gene unsupervised learning High speed discrete transforms Computer Architecture Educational institutions image coding Data compression learning algorithms Functional training

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