Self-organizing maps for anomaly detection in fuel consumption. Case study: Illegal fuel storage in Bolivia

Aquize, Vanessa Gironda; Emery, Eduardo; Neto, Fernando Buarque de Lima

doi:10.1109/la-cci.2017.8285697

Cited by 6 publications

(9 citation statements)

References 10 publications

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“…The signature-based evaluation relies on the interpretation of known attacks even during inspection trail ; the attack is recognized as an attack operation that suits for an already defined attack signatures. Aquize (2017) [13] implemented the self organize map for anomaly detection. Here, for ID, machine learning algorithms can be proposed.…”

Section: Related Workmentioning

confidence: 99%

Classification Model for IDS Using Auto Cryptographic Denoising Technique

Karthikeyan¹,

Sivaprakash²,

Karthik³

2023

Computer Systems Science and Engineering

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Intrusion detection systems (IDS) are one of the most promising ways for securing data and networks; In recent decades, IDS has used a variety of categorization algorithms. These classifiers, on the other hand, do not work effectively unless they are combined with additional algorithms that can alter the classifier's parameters or select the optimal sub-set of features for the problem. Optimizers are used in tandem with classifiers to increase the stability and with efficiency of the classifiers in detecting invasion. These algorithms, on the other hand, have a number of limitations, particularly when used to detect new types of threats. In this paper, the NSL KDD dataset and KDD Cup 99 is used to find the performance of the proposed classifier model and compared; These two IDS dataset is preprocessed, then Auto Cryptographic Denoising (ACD) adopted to remove noise in the feature of the IDS dataset; the classifier algorithms, K-Means and Neural network classifies the dataset with adam optimizer. IDS classifier is evaluated by measuring performance measures like f-measure, recall, precision, detection rate and accuracy. The neural network obtained the highest classifying accuracy as 91.12% with drop-out function that shows the efficiency of the classifier model with drop-out function for KDD Cup99 dataset. Explaining their power and limitations in the proposed methodology that could be used in future works in the IDS area.

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Section: Related Workmentioning

confidence: 99%

Classification Model for IDS Using Auto Cryptographic Denoising Technique

Karthikeyan¹,

Sivaprakash²,

Karthik³

2023

Computer Systems Science and Engineering

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“…SOMs are referred to as "Kohonen Neural Networks", a type of unsupervised learning based on competitive learning. They are typically used for classification or pattern recognition [26]. The basic algorithm is explained in [26], and, in summary, it is iteratively trained to find all necessary weight vectors that are eventually grouped based on their distance.…”

Section: Self Organizing Maps (Som)mentioning

confidence: 99%

“…They are typically used for classification or pattern recognition [26]. The basic algorithm is explained in [26], and, in summary, it is iteratively trained to find all necessary weight vectors that are eventually grouped based on their distance. After training is completed, the SOM is created and used for clustering.…”

Section: Self Organizing Maps (Som)mentioning

confidence: 99%

Combining Artificial Immune System and Clustering Analysis: A Stock Market Anomaly Detection Model

Close¹,

Kashef²

2020

JILSA

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Artificial intelligence research in the stock market sector has been heavily geared towards stock price prediction rather than stock price manipulation. As online trading systems have increased the amount of high volume and real-time data transactions, the stock market has increased vulnerability to attacks. This paper aims to detect these attacks based on normal trade behavior using an Artificial Immune System (AIS) approach combined with one of four clustering algorithms. The AIS approach is inspired by its proven ability to handle time-series data and its ability to detect abnormal behavior while only being trained on regular trade behavior. These two main points are essential as the models need to adapt over time to adjust to normal trade behavior as it evolves, and due to confidentiality and data restrictions, real-world manipulations are not available for training. This paper discovers a competitive alternative to the leading approach and investigates the effects of combining AIS with clustering algorithms; Kernel Density Estimation, Self-Organized Maps, Density-Based Spatial Clustering of Applications with Noise and Spectral clustering. The best performing solution achieves leading performance using common clustering metrics, including Area Under the Curve, False Alarm Rate, False Negative Rate, and Computation Time.

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“…For clarity, we now introduce some definitions and assumptions to refer the previous model evaluated in [1]. This is also used as axiom for our proposed anomaly detection method.…”

Section: About the Anomaliesmentioning

confidence: 99%

Business Intelligence for the detection of anomalies in records of fueling

Aquize

Filho²

2018

REPA

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Today, the organizations that store very large amounts of data need decision support technologies to achieve a satisfactory development of their organizational objectives. It is the case of the National Hydrocarbon Agency of Bolivia (ANH), which due the smuggling of fuel, implemented the RFID technology in order to store the records of fueling of all fleet vehicular in Bolivia. From the model of Anomaly Detection in Records of Fueling trough machine learning techniques proposed by Buarque et al., we propose a Business Intelligence (BI) solution able to deal with huge volume of information from records of fueling and anomalies scores in a local and global level. The proposal permitted the analysis of high anomalies presented in a specific time, by vehicle type, by department and also geo-referenced service stations (with this, the specialist could take decisions of control in some risk zones and specific service stations or vehicle type). We use the Open Source Pentaho Business Intelligence platform, one of the most used currently. This management platform covers data analysis integrated with R and reporting operations, making this a flexible solution to cover our study case: "Anomaly Detection in Records of Fueling in automobiles used for illegal fuel storage". So, the principal contribution in this paper is design and development a BI solution responsible of analyze in large amount of records of anomalies in Bolivia and in this way to allow to make better decisions of control of fuel smuggling by the ANH, having the right information in the right place at the right time.

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Self-organizing maps for anomaly detection in fuel consumption. Case study: Illegal fuel storage in Bolivia

Cited by 6 publications

References 10 publications

Classification Model for IDS Using Auto Cryptographic Denoising Technique

Classification Model for IDS Using Auto Cryptographic Denoising Technique

Combining Artificial Immune System and Clustering Analysis: A Stock Market Anomaly Detection Model

Business Intelligence for the detection of anomalies in records of fueling

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