Adaptive Cybersecurity: Dynamically Retrainable Firewalls for Real-Time Network Protection

• URL: http://arxiv.org/abs/2501.09033v1
• Date: Tue, 14 Jan 2025 00:04:35 GMT
• Title: Adaptive Cybersecurity: Dynamically Retrainable Firewalls for Real-Time Network Protection
• Authors: Sina Ahmadi,
• Abstract summary: This research introduces "Dynamically Retrainable Firewalls" Unlike traditional firewalls that rely on static rules to inspect traffic, these advanced systems leverage machine learning algorithms to analyze network traffic pattern dynamically and identify threats. It also discusses strategies to improve performance, reduce latency, optimize resource utilization, and address integration issues with present-day concepts such as Zero Trust and mixed environments.
• Score: 4.169915659794567
• License:
• Abstract: The growing complexity of cyber attacks has necessitated the evolution of firewall technologies from static models to adaptive, machine learning-driven systems. This research introduces "Dynamically Retrainable Firewalls", which respond to emerging threats in real-time. Unlike traditional firewalls that rely on static rules to inspect traffic, these advanced systems leverage machine learning algorithms to analyze network traffic pattern dynamically and identify threats. The study explores architectures such as micro-services and distributed systems for real-time adaptability, data sources for model retraining, and dynamic threat identification through reinforcement and continual learning. It also discusses strategies to improve performance, reduce latency, optimize resource utilization, and address integration issues with present-day concepts such as Zero Trust and mixed environments. By critically assessing the literature, analyzing case studies, and elucidating areas of future research, this work suggests dynamically retrainable firewalls as a more robust form of network security. Additionally, it considers emerging trends such as advancements in AI and quantum computing, ethical issues, and other regulatory questions surrounding future AI systems. These findings provide valuable information on the future state of adaptive cyber security, focusing on the need for proactive and adaptive measures that counter cyber threats that continue to evolve.

Related papers

• Towards Robust Stability Prediction in Smart Grids: GAN-based Approach under Data Constraints and Adversarial Challenges [53.2306792009435]
  We introduce a novel framework to detect instability in smart grids by employing only stable data. It relies on a Generative Adversarial Network (GAN) where the generator is trained to create instability data that are used along with stable data to train the discriminator. Our solution, tested on a dataset composed of real-world stable and unstable samples, achieve accuracy up to 97.5% in predicting grid stability and up to 98.9% in detecting adversarial attacks.
  arXiv  Detail & Related papers  (2025-01-27T20:48:25Z)
• NetFlowGen: Leveraging Generative Pre-training for Network Traffic Dynamics [72.95483148058378]
  We propose to pre-train a general-purpose machine learning model to capture traffic dynamics with only traffic data from NetFlow records. We address challenges such as unifying network feature representations, learning from large unlabeled traffic data volume, and testing on real downstream tasks in DDoS attack detection.
  arXiv  Detail & Related papers  (2024-12-30T00:47:49Z)
• A Review of the Duality of Adversarial Learning in Network Intrusion: Attacks and Countermeasures [0.0]
  Adversarial attacks, particularly those targeting vulnerabilities in deep learning models, present a nuanced and substantial threat to cybersecurity. Our study delves into adversarial learning threats such as Data Poisoning, Test Time Evasion, and Reverse Engineering. Our research lays the groundwork for strengthening defense mechanisms to address the potential breaches in network security and privacy posed by adversarial attacks.
  arXiv  Detail & Related papers  (2024-12-18T14:21:46Z)
• Simulation of Multi-Stage Attack and Defense Mechanisms in Smart Grids [2.0766068042442174]
  We introduce a simulation environment that replicates the power grid's infrastructure and communication dynamics. The framework generates diverse, realistic attack data to train machine learning algorithms for detecting and mitigating cyber threats. It also provides a controlled, flexible platform to evaluate emerging security technologies, including advanced decision support systems.
  arXiv  Detail & Related papers  (2024-12-09T07:07:17Z)
• 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)
• Siren -- Advancing Cybersecurity through Deception and Adaptive Analysis [0.0]
  This project employs sophisticated methods to lure potential threats into controlled environments. The architectural framework includes a link monitoring proxy, a purpose-built machine learning model for dynamic link analysis. The incorporation of simulated user activity extends the system's capacity to capture and learn from potential attackers.
  arXiv  Detail & Related papers  (2024-06-10T12:47:49Z)
• Disentangling the Causes of Plasticity Loss in Neural Networks [55.23250269007988]
  We show that loss of plasticity can be decomposed into multiple independent mechanisms. We show that a combination of layer normalization and weight decay is highly effective at maintaining plasticity in a variety of synthetic nonstationary learning tasks.
  arXiv  Detail & Related papers  (2024-02-29T00:02:33Z)
• Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks [63.246437631458356]
  Next-generation wireless networks (e.g., 6G) will be artificial intelligence (AI)-native. This article introduces a novel framework for building AI-native wireless networks; grounded in the emerging field of causal reasoning. We highlight several wireless networking challenges that can be addressed by causal discovery and representation.
  arXiv  Detail & Related papers  (2023-09-23T00:05:39Z)
• 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)
• Anomaly Detection in Automatic Generation Control Systems Based on Traffic Pattern Analysis and Deep Transfer Learning [0.38073142980733]
  In modern highly interconnected power grids, automatic generation control (AGC) is crucial in maintaining the stability of the power grid. The dependence of the AGC system on the information and communications technology (ICT) system makes it vulnerable to various types of cyber-attacks. Information flow (IF) analysis and anomaly detection became paramount for preventing cyber attackers from driving the cyber-physical power system to instability.
  arXiv  Detail & Related papers  (2022-09-16T17:52:42Z)
• Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning [91.13113161754022]
  We introduce timing-based adversarial strategies against a DRL-based navigation system by jamming in physical noise patterns on the selected time frames. Our experimental results show that the adversarial timing attacks can lead to a significant performance drop.
  arXiv  Detail & Related papers  (2020-02-20T21:39:25Z)

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.