Adaptive Artificial Immune Networks for Mitigating DoS flooding Attacks

• URL: http://arxiv.org/abs/2402.07714v1
• Date: Mon, 12 Feb 2024 15:26:37 GMT
• Title: Adaptive Artificial Immune Networks for Mitigating DoS flooding Attacks
• Authors: Jorge Maestre Vidal, Ana Lucila Sandoval Orozco, Luis Javier García Villalba,
• Abstract summary: This paper proposes the use of artificial immune systems to mitigate denial of service attacks. The approach is based on building networks of distributed sensors suited to the requirements of the monitored environment.
• Score: 13.580747080271825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Denial of service attacks pose a threat in constant growth. This is mainly due to their tendency to gain in sophistication, ease of implementation, obfuscation and the recent improvements in occultation of fingerprints. On the other hand, progress towards self-organizing networks, and the different techniques involved in their development, such as software-defined networking, network-function virtualization, artificial intelligence or cloud computing, facilitates the design of new defensive strategies, more complete, consistent and able to adapt the defensive deployment to the current status of the network. In order to contribute to their development, in this paper, the use of artificial immune systems to mitigate denial of service attacks is proposed. The approach is based on building networks of distributed sensors suited to the requirements of the monitored environment. These components are capable of identifying threats and reacting according to the behavior of the biological defense mechanisms in human beings. It is accomplished by emulating the different immune reactions, the establishment of quarantine areas and the construction of immune memory. For their assessment, experiments with public domain datasets (KDD'99, CAIDA'07 and CAIDA'08) and simulations on various network configurations based on traffic samples gathered by the University Complutense of Madrid and flooding attacks generated by the tool DDoSIM were performed.

Related papers

• An Attentive Graph Agent for Topology-Adaptive Cyber Defence [1.0812794909131096]
  We develop a custom version of the Cyber Operations Research Gym (CybORG) environment, encoding network state as a directed graph. We employ a Graph Attention Network (GAT) architecture to process node, edge, and global features, and adapt its output to be compatible with policy gradient methods in reinforcement learning. We demonstrate that GAT defensive policies can be trained using our low-level directed graph observations, even when unexpected connections arise during simulation.
  arXiv  Detail & Related papers  (2025-01-24T18:22:37Z)
• AIM: Additional Image Guided Generation of Transferable Adversarial Attacks [72.24101555828256]
  Transferable adversarial examples highlight the vulnerability of deep neural networks (DNNs) to imperceptible perturbations across various real-world applications. In this work, we focus on generative approaches for targeted transferable attacks. We introduce a novel plug-and-play module into the general generator architecture to enhance adversarial transferability.
  arXiv  Detail & Related papers  (2025-01-02T07:06:49Z)
• AI-based Attacker Models for Enhancing Multi-Stage Cyberattack Simulations in Smart Grids Using Co-Simulation Environments [1.4563527353943984]
  The transition to smart grids has increased the vulnerability of electrical power systems to advanced cyber threats. We propose a co-simulation framework that employs an autonomous agent to execute modular cyberattacks. Our approach offers a flexible, versatile source for data generation, aiding in faster prototyping and reducing development resources and time.
  arXiv  Detail & Related papers  (2024-12-05T08:56:38Z)
• Designing Robust Cyber-Defense Agents with Evolving Behavior Trees [0.0]
  We develop an approach to design autonomous cyber defense agents using behavior trees with learning-enabled components. Learning-enabled components are optimized for adapting to various cyber-attacks and deploying security mechanisms. Our results demonstrate that the EBT-based agent is robust to adaptive cyber-attacks and provides high-level explanations for interpreting its decisions and actions.
  arXiv  Detail & Related papers  (2024-10-21T18:00:38Z)
• Optimal Control of Malware Propagation in IoT Networks [5.761202124246859]
  Recent data indicates that the number of such attacks has increased by over 100 percent. To mitigate this attack, a new patch must be applied immediately. In this paper, we address the issue of how to mitigate cyber-attacks before the new patch is applied.
  arXiv  Detail & Related papers  (2024-01-20T01:22:28Z)
• Unscrambling the Rectification of Adversarial Attacks Transferability across Computer Networks [4.576324217026666]
  Convolutional neural networks (CNNs) models play a vital role in achieving state-of-the-art performances. CNNs can be compromised because of their susceptibility to adversarial attacks. We present a novel and comprehensive method to improve the strength of attacks and assess the transferability of adversarial examples in CNNs.
  arXiv  Detail & Related papers  (2023-10-26T22:36:24Z)
• Adaptive Attack Detection in Text Classification: Leveraging Space Exploration Features for Text Sentiment Classification [44.99833362998488]
  Adversarial example detection plays a vital role in adaptive cyber defense, especially in the face of rapidly evolving attacks. We propose a novel approach that leverages the power of BERT (Bidirectional Representations from Transformers) and introduces the concept of Space Exploration Features.
  arXiv  Detail & Related papers  (2023-08-29T23:02:26Z)
• Dynamics-aware Adversarial Attack of Adaptive Neural Networks [75.50214601278455]
  We investigate the dynamics-aware adversarial attack problem of adaptive neural networks. We propose a Leaded Gradient Method (LGM) and show the significant effects of the lagged gradient. Our LGM achieves impressive adversarial attack performance compared with the dynamic-unaware attack methods.
  arXiv  Detail & Related papers  (2022-10-15T01:32:08Z)
• The Feasibility and Inevitability of Stealth Attacks [63.14766152741211]
  We study new adversarial perturbations that enable an attacker to gain control over decisions in generic Artificial Intelligence systems. In contrast to adversarial data modification, the attack mechanism we consider here involves alterations to the AI system itself.
  arXiv  Detail & Related papers  (2021-06-26T10:50:07Z)
• TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack [46.79557381882643]
  We present TANTRA, a novel end-to-end Timing-based Adversarial Network Traffic Reshaping Attack. Our evasion attack utilizes a long short-term memory (LSTM) deep neural network (DNN) which is trained to learn the time differences between the target network's benign packets. TANTRA achieves an average success rate of 99.99% in network intrusion detection system evasion.
  arXiv  Detail & Related papers  (2021-03-10T19:03:38Z)
• Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift [62.997667081978825]
  Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
  arXiv  Detail & Related papers  (2020-01-13T13:54:36Z)

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.