S3CDM: A secret-sharing-scheme-based cyberattack detection model and its simulation implementation
- URL: http://arxiv.org/abs/2604.09924v1
- Date: Fri, 10 Apr 2026 21:42:46 GMT
- Title: S3CDM: A secret-sharing-scheme-based cyberattack detection model and its simulation implementation
- Abstract summary: We design and develop a secret-sharing-scheme-based cyberattack detection model(S3CDM)<n>The model splits a secret among a group of legitimate participants or components for authentication, integration and detection of unauthorized activities.<n>To demonstrate the feasibility, we implement the S3CDM in three parts on Google Cloud Platform.
- Score: 5.5365702232750005
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We design and develop a secret-sharing-scheme-based cyberattack detection model(S3CDM)that can detect unauthorized or illegal activities (especially insider attacks) and protect sensitive information within complex network infrastructures of large organizations. The model splits a secret among a group of legitimate participants or components for authentication, integration and detection of unauthorized activities. Traditional Shamir's polynomial interpolation based and our own hash function based schemes are utilized in the model, they both are practical and efficient to make sure the communications between different components are secure and any unauthorized activities can be detected. The model offers a flexible multi-factor authentication method to enhance the overall system security. Probability analysis [3] shows that multiple component model is more resistant against cyberattacks than the single component one. To demonstrate the feasibility, we implement the S3CDM in three parts on Google Cloud Platform, i.e., the front end UI (User Interface) running on an HTTP server, the back end individual services written in Python, and a PostgreSQL database. Docker is used to manage the start and stop of individual services and their URLs. We demonstrate how to use the UI with a use case of simulation of broken path in details.
Related papers
- Stealing Reasoning Traces from Proprietary LLM APIs [84.58379404903394]
We develop a scalable decryption jailbreak for encrypted reasoning traces.<n>It circumvents anti-distillation mechanisms, allowing adversaries to extract a proprietary model's reasoning.<n>It inadvertently reveals hazardous information hidden within the reasoning process.
arXiv Detail & Related papers (2026-08-10T17:24:50Z) - QUACK! Making the (Rubber) Ducky Talk: A Systematic Study of Keystroke Dynamics for HID Injection Detection [60.105690843777666]
We show that robust, privacy-preserving detection is achievable using lightweight models operating solely on timing features.<n>Our analysis reveals that attacker sophistication does not monotonically translate into improved evasion.
arXiv Detail & Related papers (2026-04-17T08:53:24Z) - Decentralized Privacy-Preserving Federal Learning of Computer Vision Models on Edge Devices [0.0]
Collaborative training of a machine learning model comes with a risk of sharing sensitive or private data.<n> Federated learning offers a way of collectively training a single global model without the need to share client data.<n>We have analyzed the negative effects of gradient compression and gradient noising on the accuracy of convolutional neural networks used for classification.
arXiv Detail & Related papers (2026-01-08T13:10:33Z) - CANTXSec: A Deterministic Intrusion Detection and Prevention System for CAN Bus Monitoring ECU Activations [53.036288487863786]
We propose CANTXSec, the first deterministic Intrusion Detection and Prevention system based on physical ECU activations.<n>It detects and prevents classical attacks in the CAN bus, while detecting advanced attacks that have been less investigated in the literature.<n>We prove the effectiveness of our solution on a physical testbed, where we achieve 100% detection accuracy in both classes of attacks while preventing 100% of FIAs.
arXiv Detail & Related papers (2025-05-14T13:37:07Z) - Setup Once, Secure Always: A Single-Setup Secure Federated Learning Aggregation Protocol with Forward and Backward Secrecy for Dynamic Users [12.804623314091508]
Federated Learning (FL) enables multiple users to collaboratively train a machine learning model without sharing raw data.<n>Secure aggregation protocols mitigate this risk by ensuring that only the aggregated updates are revealed.<n>We present a novel secure aggregation protocol that requires only a single setup for the entire FL training.
arXiv Detail & Related papers (2025-02-13T06:01:09Z) - Multi-client Functional Encryption for Set Intersection with Non-monotonic Access Structures in Federated Learning [1.2891210250935148]
Federated learning (FL) based on cloud servers is a distributed machine learning framework.<n>We propose a multi-client functional encryption scheme for set intersection with non-monotonic access structures.
arXiv Detail & Related papers (2024-12-12T13:19:12Z) - Unified Physical-Digital Face Attack Detection [66.14645299430157]
Face Recognition (FR) systems can suffer from physical (i.e., print photo) and digital (i.e., DeepFake) attacks.
Previous related work rarely considers both situations at the same time.
We propose a Unified Attack Detection framework based on Vision-Language Models (VLMs)
arXiv Detail & Related papers (2024-01-31T09:38:44Z) - Client-specific Property Inference against Secure Aggregation in
Federated Learning [52.8564467292226]
Federated learning has become a widely used paradigm for collaboratively training a common model among different participants.
Many attacks have shown that it is still possible to infer sensitive information such as membership, property, or outright reconstruction of participant data.
We show that simple linear models can effectively capture client-specific properties only from the aggregated model updates.
arXiv Detail & Related papers (2023-03-07T14:11:01Z) - CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive
Learning [63.72975421109622]
CleanCLIP is a finetuning framework that weakens the learned spurious associations introduced by backdoor attacks.
CleanCLIP maintains model performance on benign examples while erasing a range of backdoor attacks on multimodal contrastive learning.
arXiv Detail & Related papers (2023-03-06T17:48:32Z) - CrowdGuard: Federated Backdoor Detection in Federated Learning [39.58317527488534]
This paper presents a novel defense mechanism, CrowdGuard, that effectively mitigates backdoor attacks in Federated Learning.
CrowdGuard employs a server-located stacked clustering scheme to enhance its resilience to rogue client feedback.
The evaluation results demonstrate that CrowdGuard achieves a 100% True-Positive-Rate and True-Negative-Rate across various scenarios.
arXiv Detail & Related papers (2022-10-14T11:27:49Z) - UnSplit: Data-Oblivious Model Inversion, Model Stealing, and Label
Inference Attacks Against Split Learning [0.0]
Split learning framework aims to split up the model among the client and the server.
We show that split learning paradigm can pose serious security risks and provide no more than a false sense of security.
arXiv Detail & Related papers (2021-08-20T07:39:16Z) - Backdoor Attacks on Federated Meta-Learning [0.225596179391365]
We analyze the effects of backdoor attacks on federated meta-learning.
We propose a defense mechanism inspired by matching networks, where the class of an input is predicted from the similarity of its features.
arXiv Detail & Related papers (2020-06-12T09:23:24Z)
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.