Dark Deceptions in DHCP: Dismantling Network Defenses
- URL: http://arxiv.org/abs/2502.10646v2
- Date: Thu, 13 Mar 2025 13:22:30 GMT
- Title: Dark Deceptions in DHCP: Dismantling Network Defenses
- Authors: Robert Dilworth,
- Abstract summary: This paper explores vulnerabilities in the Dynamic Host configuration Protocol (DHCP) and their implications on the Confidentiality, Integrity, and Availability (CIA) Triad.<n>Through an analysis of various attacks, the paper provides a taxonomic classification of threats, assesses risks, and proposes appropriate controls.<n>The discussion also highlights the dangers of VPN decloaking through DHCP exploits and underscores the importance of safeguarding network infrastructures.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper explores vulnerabilities in the Dynamic Host Configuration Protocol (DHCP) and their implications on the Confidentiality, Integrity, and Availability (CIA) Triad. Through an analysis of various attacks, including DHCP Starvation, Rogue DHCP Servers, Replay Attacks, and TunnelVision exploits, the paper provides a taxonomic classification of threats, assesses risks, and proposes appropriate controls. The discussion also highlights the dangers of VPN decloaking through DHCP exploits and underscores the importance of safeguarding network infrastructures. By bringing awareness to the TunnelVision exploit, this paper aims to mitigate risks associated with these prevalent vulnerabilities.
Related papers
- Modern DDoS Threats and Countermeasures: Insights into Emerging Attacks and Detection Strategies [49.57278643040602]
Distributed Denial of Service (DDoS) attacks persist as significant threats to online services and infrastructure.
This paper offers a comprehensive survey of emerging DDoS attacks and detection strategies over the past decade.
arXiv Detail & Related papers (2025-02-27T11:22:25Z) - PCAP-Backdoor: Backdoor Poisoning Generator for Network Traffic in CPS/IoT Environments [0.6629765271909503]
We introduce textttPCAP-Backdoor, a novel technique that facilitates backdoor poisoning attacks on PCAP datasets.<n>Experiments on real-world Cyber-Physical Systems (CPS) and Internet of Things (IoT) network traffic datasets demonstrate that attackers can effectively backdoor a model by poisoning as little as 1% or less of the entire training dataset.
arXiv Detail & Related papers (2025-01-26T15:49:34Z) - Application of Machine Learning Techniques for Secure Traffic in NoC-based Manycores [44.99833362998488]
This document explores an IDS technique using machine learning and temporal series for detecting DoS attacks in NoC-based manycore systems.<n>It is necessary to extract traffic data from a manycore NoC and execute the learning techniques in the extracted data.<n>The developed platform will have its data validated with a low-level platform.
arXiv Detail & Related papers (2025-01-21T10:58:09Z) - Securing Legacy Communication Networks via Authenticated Cyclic Redundancy Integrity Check [98.34702864029796]
We propose Authenticated Cyclic Redundancy Integrity Check (ACRIC)
ACRIC preserves backward compatibility without requiring additional hardware and is protocol agnostic.
We show that ACRIC offers robust security with minimal transmission overhead ( 1 ms)
arXiv Detail & Related papers (2024-11-21T18:26:05Z) - Enhancing Transportation Cyber-Physical Systems Security: A Shift to Post-Quantum Cryptography [6.676253819673155]
The rise of quantum computing threatens traditional cryptographic algorithms that secure Transportation Cyber-Physical Systems ( TCPS)
The objective of this paper is to underscore the urgency of transitioning to post-quantum cryptography (PQC) to mitigate these risks.
We analyzed vulnerabilities in traditional cryptography against quantum attacks and reviewed the applicability of NIST-standardized PQC schemes in TCPS.
arXiv Detail & Related papers (2024-11-20T04:11:33Z) - Exploiting Cross-Layer Vulnerabilities: Off-Path Attacks on the TCP/IP Protocol Suite [26.96330717492493]
We investigate cross-layer interactions within the TCP/IP protocol suite caused by ICMP error messages.
We uncover several significant vulnerabilities, including information leakage, desynchronization, semantic gaps, and identity spoofing.
These vulnerabilities can be exploited by off-path attackers to manipulate network traffic stealthily, affecting over 20% of popular websites and more than 89% of public Wi-Fi networks.
arXiv Detail & Related papers (2024-11-15T02:41:53Z) - CVE representation to build attack positions graphs [0.39945675027960637]
In cybersecurity, CVEs (Common Vulnerabilities and Exposures) are publicly disclosed hardware or software vulnerabilities.
This article points out that these vulnerabilities should be described in greater detail to understand how they could be chained together in a complete attack scenario.
arXiv Detail & Related papers (2023-12-05T08:57:14Z) - A Novel Supervised Deep Learning Solution to Detect Distributed Denial
of Service (DDoS) attacks on Edge Systems using Convolutional Neural Networks
(CNN) [0.41436032949434404]
This project presents a novel deep learning-based approach for detecting DDoS attacks in network traffic.
The algorithm employed in this study exploits the properties of Convolutional Neural Networks (CNN) and common deep learning algorithms.
The results of this study demonstrate the effectiveness of the proposed algorithm in detecting DDOS attacks, achieving an accuracy of.9883 on 2000 unseen flows in network traffic.
arXiv Detail & Related papers (2023-09-11T17:37:35Z) - ThreatKG: An AI-Powered System for Automated Open-Source Cyber Threat Intelligence Gathering and Management [65.0114141380651]
ThreatKG is an automated system for OSCTI gathering and management.
It efficiently collects a large number of OSCTI reports from multiple sources.
It uses specialized AI-based techniques to extract high-quality knowledge about various threat entities.
arXiv Detail & Related papers (2022-12-20T16:13:59Z) - Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial
Attacks and Training [62.77129284830945]
This paper considers a regression problem in a wireless setting and shows that adversarial attacks can break the DL-based approach.
We also analyze the effectiveness of adversarial training as a defensive technique in adversarial settings and show that the robustness of DL-based wireless system against attacks improves significantly.
arXiv Detail & Related papers (2022-06-14T04:55:11Z) - 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) - A System for Automated Open-Source Threat Intelligence Gathering and
Management [53.65687495231605]
SecurityKG is a system for automated OSCTI gathering and management.
It uses a combination of AI and NLP techniques to extract high-fidelity knowledge about threat behaviors.
arXiv Detail & Related papers (2021-01-19T18:31:35Z)
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.