Cyberattack Detection in Critical Infrastructure and Supply Chains

• URL: http://arxiv.org/abs/2510.19859v1
• Date: Tue, 21 Oct 2025 20:38:58 GMT
• Title: Cyberattack Detection in Critical Infrastructure and Supply Chains
• Authors: Smita Khapre,
• Abstract summary: Intrusion Detection Systems (IDS) are deployed to counter cyberattacks.<n>IDS effectively detects attacks based on the known signatures and patterns, Zero-day attacks go undetected.<n>To overcome this drawback in IDS, the integration of a Dense Neural Network (DNN) with Data Augmentation is proposed.<n>It makes IDS intelligent and enables it to self-learn with high accuracy when a novel attack is encountered.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Cyberattack detection in Critical Infrastructure and Supply Chains has become challenging in Industry 4.0. Intrusion Detection Systems (IDS) are deployed to counter the cyberattacks. However, an IDS effectively detects attacks based on the known signatures and patterns, Zero-day attacks go undetected. To overcome this drawback in IDS, the integration of a Dense Neural Network (DNN) with Data Augmentation is proposed. It makes IDS intelligent and enables it to self-learn with high accuracy when a novel attack is encountered. The network flow captures datasets are highly imbalanced same as the real network itself. The Data Augmentation plays a crucial role in balancing the data. The balancing of data is challenging as the minority class is as low as 0.000004\% of the dataset, and the abundant class is higher than 80\% of the dataset. Synthetic Minority Oversampling Technique is used for balancing the data. However, higher accuracies are achieved with balanced test data, lower accuracies are noticeable with the original imbalanced test data suggesting overfitting. A comparison with state-of-the-art research using Synthetic Minority Oversampling Technique with Edited Nearest Neighbor shows the classification of classes remains poor for the original dataset. This suggests highly imbalanced datasets of network flow require a different method of data augmentation.

Related papers

• Diffusion-Driven Synthetic Tabular Data Generation for Enhanced DoS/DDoS Attack Classification [0.0]
  Class imbalance in network intrusion detection using Tabular Denoising Diffusion Probability Models (TabDDPM)<n>Our approach synthesizes high-fidelity minority-class samples from the CIC-IDS 2017 dataset through iterative denoising processes.<n>For the minority classes that have smaller samples, synthetic samples were generated and merged with the original dataset.
  arXiv  Detail & Related papers  (2026-01-19T16:22:27Z)
• Can We Enhance the Quality of Mobile Crowdsensing Data Without Ground Truth? [45.875832406278214]
  This article proposes a prediction- and reputation-based truth discovery framework.<n>It can separate low-quality data from high-quality data in sensing tasks.<n>It outperforms existing methods in terms of identification accuracy and data quality enhancement.
  arXiv  Detail & Related papers  (2024-05-29T03:16:12Z)
• Dealing with Imbalanced Classes in Bot-IoT Dataset [3.7399138244928145]
  We propose a binary classification method with synthetic minority over-sampling techniques (SMOTE) to address the class imbalance problem in the Bot-IoT dataset. The proposed classifier aims to detect attack packets and overcome the class imbalance problem using the SMOTE algorithm.
  arXiv  Detail & Related papers  (2024-03-27T20:09:59Z)
• SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target Detection [53.19618419772467]
  Single-frame infrared small target (SIRST) detection aims to recognize small targets from clutter backgrounds. With the development of Transformer, the scale of SIRST models is constantly increasing. With a rich diversity of infrared small target data, our algorithm significantly improves the model performance and convergence speed.
  arXiv  Detail & Related papers  (2024-03-08T16:14:54Z)
• Advancing DDoS Attack Detection: A Synergistic Approach Using Deep Residual Neural Networks and Synthetic Oversampling [2.988269372716689]
  We introduce an enhanced approach for DDoS attack detection by leveraging the capabilities of Deep Residual Neural Networks (ResNets) We balance the representation of benign and malicious data points, enabling the model to better discern intricate patterns indicative of an attack. Experimental results on a real-world dataset demonstrate that our approach achieves an accuracy of 99.98%, significantly outperforming traditional methods.
  arXiv  Detail & Related papers  (2024-01-06T03:03:52Z)
• From Zero to Hero: Detecting Leaked Data through Synthetic Data Injection and Model Querying [10.919336198760808]
  We introduce a novel methodology to detect leaked data that are used to train classification models. textscLDSS involves injecting a small volume of synthetic data--characterized by local shifts in class distribution--into the owner's dataset. This enables the effective identification of models trained on leaked data through model querying alone.
  arXiv  Detail & Related papers  (2023-10-06T10:36:28Z)
• Adversarial training with informed data selection [53.19381941131439]
  Adrial training is the most efficient solution to defend the network against these malicious attacks. This work proposes a data selection strategy to be applied in the mini-batch training. The simulation results show that a good compromise can be obtained regarding robustness and standard accuracy.
  arXiv  Detail & Related papers  (2023-01-07T12:09:50Z)
• DRL-GAN: A Hybrid Approach for Binary and Multiclass Network Intrusion Detection [2.7122540465034106]
  Intrusion detection systems (IDS) are an essential security technology for detecting these attacks. We implement a novel hybrid technique using synthetic data produced by a Generative Adversarial Network (GAN) to use as input for training a Deep Reinforcement Learning (DRL) model. Our findings demonstrate that training the DRL on specific synthetic datasets can result in better performance in correctly classifying minority classes over training on the true imbalanced dataset.
  arXiv  Detail & Related papers  (2023-01-05T19:51:24Z)
• A Synthetic Dataset for 5G UAV Attacks Based on Observable Network Parameters [3.468596481227013]
  This paper presents the first synthetic dataset for Unmanned Aerial Vehicle (UAV) attacks in 5G and beyond networks. The main objective of this data is to enable deep network development for UAV communication security. The proposed dataset provides insights into network functionality when static or moving UAV attackers target authenticated UAVs in an urban environment.
  arXiv  Detail & Related papers  (2022-11-05T15:12:51Z)
• Autoregressive Perturbations for Data Poisoning [54.205200221427994]
  Data scraping from social media has led to growing concerns regarding unauthorized use of data. Data poisoning attacks have been proposed as a bulwark against scraping. We introduce autoregressive (AR) poisoning, a method that can generate poisoned data without access to the broader dataset.
  arXiv  Detail & Related papers  (2022-06-08T06:24:51Z)
• How Robust are Randomized Smoothing based Defenses to Data Poisoning? [66.80663779176979]
  We present a previously unrecognized threat to robust machine learning models that highlights the importance of training-data quality. We propose a novel bilevel optimization-based data poisoning attack that degrades the robustness guarantees of certifiably robust classifiers. Our attack is effective even when the victim trains the models from scratch using state-of-the-art robust training methods.
  arXiv  Detail & Related papers  (2020-12-02T15:30:21Z)
• Adversarial Self-Supervised Contrastive Learning [62.17538130778111]
  Existing adversarial learning approaches mostly use class labels to generate adversarial samples that lead to incorrect predictions. We propose a novel adversarial attack for unlabeled data, which makes the model confuse the instance-level identities of the perturbed data samples. We present a self-supervised contrastive learning framework to adversarially train a robust neural network without labeled data.
  arXiv  Detail & Related papers  (2020-06-13T08:24:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.