An effective fraud detection using competitive swarm optimization based deep neural network

Karthikeyan, T.; Govindarajan, M.; Vijayakumar, V.

doi:10.1016/j.measen.2023.100793

Cited by 13 publications

(1 citation statement)

References 25 publications

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“…Anomaly detection is crucial in many fields and applications, including in healthcare [3][4][5][6], cybersecurity and computer networks with Fast Reroute mechanisms [7,8], financial industry [9][10][11][12], robotics [13][14][15], and video surveillance [16][17][18], among many others [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

GAN-Based Anomaly Detection Tailored for Classifiers

Králik,

Kontšek,

Škvarek

et al. 2024

Mathematics

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Pattern recognition systems always misclassify anomalies, which can be dangerous for uninformed users. Therefore, anomalies must be filtered out from each classification. The main challenge for the anomaly filter design is the huge number of possible anomaly samples compared with the number of samples in the training set. Tailoring the filter for the given classifier is just the first step in this reduction. Paper tests the hypothesis that the filter trained in avoiding “near” anomalies will also refuse the “far” anomalies, and the anomaly detector is then just a classifier distinguishing between “far real” and “near anomaly” samples. As a “far real” samples generator was used, a Generative Adversarial Network (GAN) fake generator that transforms normally distributed random seeds into fakes similar to the training samples. The paper proves the assumption that seeds unused in fake training will generate anomalies. These seeds are distinguished according to their Chebyshev norms. While the fakes have seeds within the hypersphere with a given radius, the near anomalies have seeds within the sphere near cover. Experiments with various anomaly test sets have shown that GAN-based anomaly detectors create a reliable anti-anomaly shield using the abovementioned assumptions. The proposed anomaly detector is tailored to the given classifier, but its limitation is due to the need for the availability of the database on which the classifier was trained.

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Section: Introductionmentioning

confidence: 99%

GAN-Based Anomaly Detection Tailored for Classifiers

Králik,

Kontšek,

Škvarek

et al. 2024

Mathematics

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To secure an e‐commerce system using epidemic mathematical modeling with neural network

Yadav,

Keshri

2024

Concurrency and Computation

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SummarySecuring an e‐commerce system using epidemic mathematical modeling with neural networks involves adapting epidemiological principles to combat the spread of misinformation. Just like how epidemiologists track the spread of diseases through populations, we can track the dissemination of fake news through online platforms. By modeling how fake news spreads, we gain insights into its propagation patterns, enabling us to develop more effective countermeasures. Neural networks, with their ability to learn from data, play a crucial role in this process by analyzing vast amounts of information to identify and mitigate the impact of fake news. One potential disadvantage of using epidemic mathematical modeling with neural networks to secure e‐commerce systems is the complexity of the approach. The epidemic‐based recurrent long short‐term memory (E‐RLSTM) technique addresses the complexity and evolving nature of fake news propagation by leveraging the strengths of recurrent neural networks (RNNs), specifically long short‐term memory (LSTM) units, within an epidemic modeling framework. One advantage of using epidemic mathematical modeling with neural networks to secure e‐commerce systems is its proactive nature. One significant finding in employing this approach is the ability to uncover hidden connections and correlations within the data. E‐RLSTM stands out by capturing temporal dynamics and integrating epidemic parameters into its LSTM architecture, ensuring robustness and adaptability in detecting and combating fake news within e‐commerce systems, outperforming other techniques in accuracy and performance. Description of the NSL‐KDD dataset offers easy access to a valuable repository for benchmarking cyber security. Contained within are more than 120,000 authentic samples of cyber‐attacks across 41 distinct categories, providing an excellent environment for testing intrusion detection systems.

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An Insight into Recent Advances in the Intelligent Controller Methods

Singh,

Saini

2024

Lecture Notes in Networks and Systems

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An effective fraud detection using competitive swarm optimization based deep neural network

Cited by 13 publications

References 25 publications

GAN-Based Anomaly Detection Tailored for Classifiers

GAN-Based Anomaly Detection Tailored for Classifiers

To secure an e‐commerce system using epidemic mathematical modeling with neural network

An Insight into Recent Advances in the Intelligent Controller Methods

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