Designing an Artificial Immune System inspired Intrusion Detection System

• URL: http://arxiv.org/abs/2208.07801v1
• Date: Tue, 16 Aug 2022 15:30:51 GMT
• Title: Designing an Artificial Immune System inspired Intrusion Detection System
• Authors: William Anderson and Kaneesha Moore and Jesse Ables and Sudip Mittal and Shahram Rahimi and Ioana Banicescu and Maria Seale
• Abstract summary: The Human Immune System (HIS) works to protect a body from infection, illness, and disease. This system can inspire cybersecurity professionals to design an Artificial Immune System (AIS) based Intrusion Detection System (IDS) These biologically inspired algorithms using Self/Nonself and Danger Theory can directly augmentIDS designs and implementations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Human Immune System (HIS) works to protect a body from infection, illness, and disease. This system can inspire cybersecurity professionals to design an Artificial Immune System (AIS) based Intrusion Detection System (IDS). These biologically inspired algorithms using Self/Nonself and Danger Theory can directly augmentIDS designs and implementations. In this paper, we include an examination into the elements of design necessary for building an AIS-IDS framework and present an architecture to create such systems.

Related papers

• EAIRiskBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [47.69642609574771]
  Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction. Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results. However, the deployment of these agents in physical environments presents significant safety challenges. This study introduces EAIRiskBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• Adaptive Artificial Immune Networks for Mitigating DoS flooding Attacks [13.580747080271825]
  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.
  arXiv  Detail & Related papers  (2024-02-12T15:26:37Z)
• Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignment [65.268245109828]
  We examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology. We organize the ever-growing set of brain-inspired learning schemes into six general families and consider these in the context of backpropagation of errors. The results of this review are meant to encourage future developments in neuro-mimetic systems and their constituent learning processes.
  arXiv  Detail & Related papers  (2023-12-01T05:20:57Z)
• Detection of Unknown-Unknowns in Human-in-Plant Human-in-Loop Systems Using Physics Guided Process Models [4.702143872609881]
  We propose a novel framework for analyzing the operational output characteristics of safety-critical HIL-HIP systems. We propose dynamics-induced hybrid recurrent neural networks (DiH-RNN) to mine a physics-guided surrogate model (PGSM) The PGSM enables early detection of unknown-unknowns based on the physical laws governing the system.
  arXiv  Detail & Related papers  (2023-09-05T22:22:30Z)
• Robotic Navigation Autonomy for Subretinal Injection via Intelligent Real-Time Virtual iOCT Volume Slicing [88.99939660183881]
  We propose a framework for autonomous robotic navigation for subretinal injection. Our method consists of an instrument pose estimation method, an online registration between the robotic and the i OCT system, and trajectory planning tailored for navigation to an injection target. Our experiments on ex-vivo porcine eyes demonstrate the precision and repeatability of the method.
  arXiv  Detail & Related papers  (2023-01-17T21:41:21Z)
• Neuro-symbolic Explainable Artificial Intelligence Twin for Zero-touch IoE in Wireless Network [61.90504487270785]
  Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) A reliable XAI twin system for ZSM requires two composites: an extreme analytical ability for discretizing the physical behavior of the Internet of Everything (IoE) and rigorous methods for characterizing the reasoning of such behavior. A novel neuro-symbolic explainable artificial intelligence twin framework is proposed to enable trustworthy ZSM for a wireless IoE.
  arXiv  Detail & Related papers  (2022-10-13T01:08:06Z)
• A Novel Online Incremental Learning Intrusion Prevention System [2.5234156040689237]
  This paper proposes a novel Network Intrusion Prevention System that utilise a SelfOrganizing Incremental Neural Network along with a Support Vector Machine. Due to its structure, the proposed system provides a security solution that does not rely on signatures or rules and is capable to mitigate known and unknown attacks in real-time with high accuracy.
  arXiv  Detail & Related papers  (2021-09-20T13:30:11Z)
• Towards robust sensing for Autonomous Vehicles: An adversarial perspective [82.83630604517249]
  It is of primary importance that the resulting decisions are robust to perturbations. Adversarial perturbations are purposefully crafted alterations of the environment or of the sensory measurements. A careful evaluation of the vulnerabilities of their sensing system(s) is necessary in order to build and deploy safer systems.
  arXiv  Detail & Related papers  (2020-07-14T05:25:15Z)
• Towards an Interface Description Template for AI-enabled Systems [77.34726150561087]
  Reuse is a common system architecture approach that seeks to instantiate a system architecture with existing components. There is currently no framework that guides the selection of necessary information to assess their portability to operate in a system different than the one for which the component was originally purposed. We present ongoing work on establishing an interface description template that captures the main information of an AI-enabled component.
  arXiv  Detail & Related papers  (2020-07-13T20:30:26Z)
• Identifying Vulnerabilities of Industrial Control Systems using Evolutionary Multiobjective Optimisation [1.8275108630751844]
  We identify vulnerabilities in real-world industrial control systems (ICS) using evolutionary multiobjective optimisation (EMO) algorithms. Our approach is evaluated on a benchmark chemical plant simulator, the Tennessee Eastman (TE) process model. A defence against these attacks in the form of a novel intrusion detection system was developed.
  arXiv  Detail & Related papers  (2020-05-27T00:22:48Z)
• Securing of Unmanned Aerial Systems (UAS) against security threats using human immune system [1.2691047660244335]
  An Intrusion Detection System (IDS) has been proposed to protect against the security problems using the human immune system (HIS) The IDSs are used to detect and respond to attempts to compromise the target system.
  arXiv  Detail & Related papers  (2020-03-01T19:05:16Z)

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.