Command & Control (C2) Traffic Detection Via Algorithm Generated Domain (Dga) Classification Using Deep Learning And Natural Language Processing

• URL: http://arxiv.org/abs/2512.07866v1
• Date: Wed, 26 Nov 2025 18:59:56 GMT
• Title: Command & Control (C2) Traffic Detection Via Algorithm Generated Domain (Dga) Classification Using Deep Learning And Natural Language Processing
• Authors: Maria Milena Araujo Felix,
• Abstract summary: The sophistication of modern malware has rendered static blacklist-based defenses obsolete.<n>The use of Domain Generation Algorithms (DGA) allows attackers to generate thousands of dynamic addresses daily.<n>This paper proposes and evaluate a method for detecting DGA domains using Deep Learning and Natural Language Processing (NLP) techniques.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The sophistication of modern malware, specifically regarding communication with Command and Control (C2) servers, has rendered static blacklist-based defenses obsolete. The use of Domain Generation Algorithms (DGA) allows attackers to generate thousands of dynamic addresses daily, hindering blocking by traditional firewalls. This paper aims to propose and evaluate a method for detecting DGA domains using Deep Learning and Natural Language Processing (NLP) techniques. The methodology consisted of collecting a hybrid database containing 50,000 legitimate and 50,000 malicious domains, followed by the extraction of lexical features and the training of a Recurrent Neural Network (LSTM). Results demonstrated that while statistical entropy analysis is effective for simple DGAs, the Neural Network approach presents superiority in detecting complex patterns, reaching 97.2% accuracy and reducing the false positive rate in ambiguous lawful traffic scenarios.

Related papers

• Autonomous Chain-of-Thought Distillation for Graph-Based Fraud Detection [73.9189065770752]
  Graph-based fraud detection on text-attributed graphs (TAGs) requires jointly modeling rich textual semantics and relational dependencies.<n>We propose FraudCoT, a unified framework that advances TAG-based fraud detection through autonomous, graph-aware chain-of-thought (CoT) reasoning and scalable LLM-GNN co-training.
  arXiv  Detail & Related papers  (2026-01-30T13:12:12Z)
• Fine-tuning Large Language Models for DGA and DNS Exfiltration Detection [1.350128573715538]
  Large Language Models (LLMs) have demonstrated their proficiency in real-time detection tasks. Our work validates the effectiveness of fine-tuned LLMs for detecting DGAs and DNS exfiltration attacks.
  arXiv  Detail & Related papers  (2024-10-29T04:22:28Z)
• Comprehensive Botnet Detection by Mitigating Adversarial Attacks, Navigating the Subtleties of Perturbation Distances and Fortifying Predictions with Conformal Layers [1.6001193161043425]
  Botnets are computer networks controlled by malicious actors that present significant cybersecurity challenges. This research addresses the sophisticated adversarial manipulations posed by attackers, aiming to undermine machine learning-based botnet detection systems. We introduce a flow-based detection approach, leveraging machine learning and deep learning algorithms trained on the ISCX and ISOT datasets.
  arXiv  Detail & Related papers  (2024-09-01T08:53:21Z)
• RoBERTa-Augmented Synthesis for Detecting Malicious API Requests [9.035212370386846]
  We introduce a GAN-inspired learning framework that extends limited API traffic datasets through targeted, domain-aware synthesis.<n>We evaluate our framework on two benchmark datasets, CSIC 2010 and ATRDF 2023, and compare it with a previous data augmentation technique.<n>Our method achieves up to a 4.94% increase in F1 score on CSIC 2010 and up to 21.10% on ATRDF 2023.
  arXiv  Detail & Related papers  (2024-05-18T11:10:45Z)
• Deep Learning Algorithms Used in Intrusion Detection Systems -- A Review [0.0]
  This review paper studies recent advancements in the application of deep learning techniques, including CNN, Recurrent Neural Networks (RNN), Deep Belief Networks (DBN), Deep Neural Networks (DNN), Long Short-Term Memory (LSTM), autoencoders (AE), Multi-Layer Perceptrons (MLP), Self-Normalizing Networks (SNN) and hybrid models, within network intrusion detection systems.
  arXiv  Detail & Related papers  (2024-02-26T20:57:35Z)
• GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative model [69.71629949747884]
  Adversarial generative models, such as Generative Adversarial Networks (GANs), are widely applied for generating various types of data. In this work, we propose a novel algorithm named GE-AdvGAN to enhance the transferability of adversarial samples.
  arXiv  Detail & Related papers  (2024-01-11T16:43:16Z)
• A Novel Supervised Deep Learning Solution to Detect Distributed Denial of Service (DDoS) attacks on Edge Systems using Convolutional Neural Networks (CNN) [0.41436032949434404]
  This project presents a novel deep learning-based approach for detecting DDoS attacks in network traffic. The algorithm employed in this study exploits the properties of Convolutional Neural Networks (CNN) and common deep learning algorithms. The results of this study demonstrate the effectiveness of the proposed algorithm in detecting DDOS attacks, achieving an accuracy of.9883 on 2000 unseen flows in network traffic.
  arXiv  Detail & Related papers  (2023-09-11T17:37:35Z)
• Quantization-aware Interval Bound Propagation for Training Certifiably Robust Quantized Neural Networks [58.195261590442406]
  We study the problem of training and certifying adversarially robust quantized neural networks (QNNs) Recent work has shown that floating-point neural networks that have been verified to be robust can become vulnerable to adversarial attacks after quantization. We present quantization-aware interval bound propagation (QA-IBP), a novel method for training robust QNNs.
  arXiv  Detail & Related papers  (2022-11-29T13:32:38Z)
• Unsupervised Abnormal Traffic Detection through Topological Flow Analysis [1.933681537640272]
  topological connectivity component of a malicious flow is less exploited. We present a simple method that facilitate the use of connectivity graph features in unsupervised anomaly detection algorithms.
  arXiv  Detail & Related papers  (2022-05-14T18:52:49Z)
• Software Vulnerability Detection via Deep Learning over Disaggregated Code Graph Representation [57.92972327649165]
  This work explores a deep learning approach to automatically learn the insecure patterns from code corpora. Because code naturally admits graph structures with parsing, we develop a novel graph neural network (GNN) to exploit both the semantic context and structural regularity of a program.
  arXiv  Detail & Related papers  (2021-09-07T21:24:36Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)
• Cassandra: Detecting Trojaned Networks from Adversarial Perturbations [92.43879594465422]
  In many cases, pre-trained models are sourced from vendors who may have disrupted the training pipeline to insert Trojan behaviors into the models. We propose a method to verify if a pre-trained model is Trojaned or benign. Our method captures fingerprints of neural networks in the form of adversarial perturbations learned from the network gradients.
  arXiv  Detail & Related papers  (2020-07-28T19:00:40Z)

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.