Saflo: eBPF-Based MPTCP Scheduler for Mitigating Traffic Analysis Attacks in Cellular Networks

• URL: http://arxiv.org/abs/2502.04236v1
• Date: Thu, 06 Feb 2025 17:20:07 GMT
• Title: Saflo: eBPF-Based MPTCP Scheduler for Mitigating Traffic Analysis Attacks in Cellular Networks
• Authors: Sangwoo Lee, Liuyi Jin, Radu Stoleru,
• Abstract summary: Saflo scheduler employs multipath communication combined with additional security-related tasks. It significantly reduces the accuracy of video identification and user identification attacks in cellular networks.
• Score: 5.449956884943377
• License:
• Abstract: This paper presents the $\underline{\textbf{saf}}$e sub$\underline{\textbf{flo}}$w (Saflo) eBPF-based multipath TCP (MPTCP) scheduler, designed to mitigate traffic analysis attacks in cellular networks. Traffic analysis attacks, which exploit vulnerabilities in Downlink Control Information (DCI) messages, remain a significant security threat in LTE/5G networks. To counter such threats, the Saflo scheduler employs multipath communication combined with additional security-related tasks. Specifically, it utilizes eBPF tools to operate in both kernel and user spaces. In the kernel space, the eBPF scheduler performs multipath scheduling while excluding paths disabled by the user-space programs. The user-space programs conduct security-related computations and machine learning-based attack detection, determining whether each path should be enabled or disabled. This approach offloads computationally intensive tasks to user-space programs, enabling timely multipath scheduling in kernel space. The Saflo scheduler was evaluated in a private LTE/5G testbed. The results demonstrated that it significantly reduces the accuracy of video identification and user identification attacks in cellular networks while maintaining reasonable network performance for users.

Related papers

• TIMESAFE: Timing Interruption Monitoring and Security Assessment for Fronthaul Environments [25.43682473591802]
  We show how a spoofing attack is able to cause a production-ready O-RAN and 5G-compliant private cellular base station to catastrophically fail within 2 seconds of the attack. To counter this, we design a Machine Learning-based monitoring solution capable of detecting various malicious attacks with over 97.5% accuracy.
  arXiv  Detail & Related papers  (2024-12-17T16:13:37Z)
• Ransomware Detection Using Machine Learning in the Linux Kernel [0.0]
  Linux-based cloud environments have become lucrative targets for ransomware attacks. We propose leveraging the extended Berkeley Packet Filter (eBPF) to collect system call information regarding active processes and infer about the data directly at the kernel level.
  arXiv  Detail & Related papers  (2024-09-10T12:17:23Z)
• P4Control: Line-Rate Cross-Host Attack Prevention via In-Network Information Flow Control Enabled by Programmable Switches and eBPF [14.787290539225245]
  P4Control is a network defense system that confines end-to-end information flows in a network and prevents cross-host attacks at line rate. We conduct extensive evaluations to show that P4Control can effectively prevent cross-host attacks in real time.
  arXiv  Detail & Related papers  (2024-05-23T18:19:10Z)
• Cross-domain Learning Framework for Tracking Users in RIS-aided Multi-band ISAC Systems with Sparse Labeled Data [55.70071704247794]
  Integrated sensing and communications (ISAC) is pivotal for 6G communications and is boosted by the rapid development of reconfigurable intelligent surfaces (RISs) This paper proposes the X2Track framework, where we model the tracking function by a hierarchical architecture, jointly utilizing multi-modal CSI indicators across multiple bands, and optimize it in a cross-domain manner. Under X2Track, we design an efficient deep learning algorithm to minimize tracking errors, based on transformer neural networks and adversarial learning techniques.
  arXiv  Detail & Related papers  (2024-05-10T08:04:27Z)
• Congestion-aware Distributed Task Offloading in Wireless Multi-hop Networks Using Graph Neural Networks [33.79834562046449]
  Existing offloading schemes mainly focus on mobile devices and servers, while ignoring the potential network congestion caused by tasks from multiple mobile devices. We propose a congestion-aware distributed task offloading scheme by augmenting a distributed greedy framework with graph-based machine learning. Our approach is demonstrated to be effective in reducing congestion or unstable queues under the context-agnostic baseline, while improving the execution latency over local computing.
  arXiv  Detail & Related papers  (2023-12-05T03:46:30Z)
• Secure Deep Learning-based Distributed Intelligence on Pocket-sized Drones [75.80952211739185]
  Palm-sized nano-drones are an appealing class of edge nodes, but their limited computational resources prevent running large deep-learning models onboard. Adopting an edge-fog computational paradigm, we can offload part of the computation to the fog; however, this poses security concerns if the fog node, or the communication link, can not be trusted. We propose a novel distributed edge-fog execution scheme that validates fog computation by redundantly executing a random subnetwork aboard our nano-drone.
  arXiv  Detail & Related papers  (2023-07-04T08:29:41Z)
• Reinforcement Learning based Cyberattack Model for Adaptive Traffic Signal Controller in Connected Transportation Systems [61.39400591328625]
  In a connected transportation system, adaptive traffic signal controllers (ATSC) utilize real-time vehicle trajectory data received from vehicles to regulate green time. This wirelessly connected ATSC increases cyber-attack surfaces and increases their vulnerability to various cyber-attack modes. One such mode is a'sybil' attack in which an attacker creates fake vehicles in the network. An RL agent is trained to learn an optimal rate of sybil vehicle injection to create congestion for an approach(s)
  arXiv  Detail & Related papers  (2022-10-31T20:12:17Z)
• Computation Offloading and Resource Allocation in F-RANs: A Federated Deep Reinforcement Learning Approach [67.06539298956854]
  fog radio access network (F-RAN) is a promising technology in which the user mobile devices (MDs) can offload computation tasks to the nearby fog access points (F-APs)
  arXiv  Detail & Related papers  (2022-06-13T02:19:20Z)
• Federated Dynamic Sparse Training: Computing Less, Communicating Less, Yet Learning Better [88.28293442298015]
  Federated learning (FL) enables distribution of machine learning workloads from the cloud to resource-limited edge devices. We develop, implement, and experimentally validate a novel FL framework termed Federated Dynamic Sparse Training (FedDST) FedDST is a dynamic process that extracts and trains sparse sub-networks from the target full network.
  arXiv  Detail & Related papers  (2021-12-18T02:26:38Z)
• A Comparative Analysis of Machine Learning Algorithms for Intrusion Detection in Edge-Enabled IoT Networks [0.0]
  Intrusion detection is one of the challenging issues in the area of network security. In this paper, a comparative analysis of conventional machine learning classification algorithms has been performed. It can be observed that Multi-Layer Perception (MLP) has dependencies between input and output and relies more on network configuration for intrusion detection.
  arXiv  Detail & Related papers  (2021-11-02T05:58:07Z)
• Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming [97.40955121478716]
  We propose a first-order dual SDP algorithm that requires memory only linear in the total number of network activations. We significantly improve L-inf verified robust accuracy from 1% to 88% and 6% to 40% respectively. We also demonstrate tight verification of a quadratic stability specification for the decoder of a variational autoencoder.
  arXiv  Detail & Related papers  (2020-10-22T12:32:29Z)

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.