DCBA: Detection of Collaborative Black-Hole Attacks in Connected Dominated Set using Baiting Process

Lata B T; Venugopal K R

Home/Archive/arXiv

arXiv · 2608.12347

DCBA: Detection of Collaborative Black-Hole Attacks in Connected Dominated Set using Baiting Process

Lata B T·Venugopal K R

View original source ↗Read source document ↗Export metadata ↓

Abstract

Mobile Ad-hoc Network (MANET) is temporary and dynamic network topology, wherein nodes are mobile in nature and distributed randomly in a network area. In MANET, nodes cooperate with each other to operate and forward data through multihop communication between source and destination. MANET is exposed to different types of attacks due to absence of central administration. However, some nodes decline to cooperate, misbehaves and appears to be malicious affecting network functionality and connectivity. Providing security and identifying malicious node has become one of the challenging research topics in MANET. Black-hole attack is considered to be most popular attack that degrades the overall network performance. Black-hole node falsely advertises the shortest path to destination intentionally to disrupt the network communication resulting in packet drop. In collaborative black-hole attacks, multiple black-hole nodes cooperate and launch attacks in order to degrade network reliability. In this article we propose a Lightweight technique to detect and isolate Collaborative Black-Hole attacks (LW-CBH) by enhancing existing AODV routing protocol. In this scheme a timer based baiting process and reverse tracing setup is used to detect malicious node through control status message in MAC layer which are Reply Sequence (R-SEQ) and Code Sequence (C-SEQ) message of connected dominated set of nodes. However existing AODV routing protocol fails to detect malicious node during dynamic topology changing in MANET. Simulation of proposed technique is performed using discrete event simulator tool NS-2.35. The simulation results are evaluated for throughput, packet delivery ratio, average end-to-end delay and normalized routing overhead.

Explore related subjects

cs.CR

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Lata B T, Venugopal K R. 2026-06-13. DCBA: Detection of Collaborative Black-Hole Attacks in Connected Dominated Set using Baiting Process. https://arxiv.org/abs/2608.12347

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Starfish: Rebalancing Multi-Party Off-Chain Payment Channels

Blockchain technology has revolutionized the way transactions are executed, but scalability remains a major challenge. Payment Channel Network (PCN), as a Layer-2 scaling solution, has been proposed to address this issue. However, skewed payments can deplete the balance of one party within a channel, restricting the ability of PCNs to transact through a path and subsequently reducing the transaction success rate. To address this issue, the technology of rebalancing has been proposed. However, existing rebalancing strategies in PCNs are limited in their efficiency and applicability under realistic network topologies. Cycle-based approaches only enable rebalancing among nodes that form cycles, while non-cycle-based approaches incur high on-chain operation costs. In this study, we propose Starfish, a rebalancing approach designed for the locally star-like structures formed by high-degree routing hubs and their incident channels in real-world PCNs. By leveraging a hub-centric structure, Starfish reduces the on-chain overhead of rebalancing from quadratic to linear in the number of channels, achieving high rebalancing efficiency. We formally prove the security of Starfish and conduct comparative experiments against existing rebalancing techniques to demonstrate its effectiveness.

cs.CR↗

arXiv2026

Proof-of-Authorship for Diffusion-based AI Generated Content

Recent advancements in AI-generated content (AIGC) have introduced new challenges in intellectual property protection and the authentication of generated objects. We focus on scenarios in which an author seeks to assert authorship of an object generated using latent diffusion models (LDMs), in the presence of adversaries who attempt to falsely claim authorship of objects they did not create. While proof-of-ownership has been studied in the context of multimedia content through techniques such as time-stamping and watermarking, these approaches face notable limitations. In contrast to traditional content creation sources (e.g., cameras), the LDM generation process offers greater control to the author. Specifically, the random seed used during generation can be deliberately chosen. By binding the seed to the author's identity using cryptographic functions, the author can assert to be the creator of the object. We refer to this stronger guarantee as proof-of-authorship, since only the creator of the object can legitimately claim the object. This contrasts with proof-of-ownership via time-stamping or watermarking, where any entity could potentially claim ownership of an object by being the first to timestamp or embed the watermark. We propose a proof-of-authorship framework involving a probabilistic adjudicator who quantifies the probability that a claim is false. Furthermore, unlike prior approaches, the proposed framework does not involve any secret. We explore various attack scenarios and analyze design choices using Stable Diffusion 2.1 and XL as representative case studies.

cs.CR↗

arXiv2026

Impact Analysis of Speech Representation Learning Models for Acoustic Side-Channel Attack

Acoustic side-channel attacks (ASCA) on keyboards have gained increasing attention, yet impact of speech representation learning models in ASCA remains unexplored. Addressing this, we introduce KEYAC, a dataset designed to analyze representation generalization for ASCA under both standard and VoIP codec settings. On KEYAC, we evaluate six representation learning models under zero-shot and partial fine-tuning settings using fully connected and convolutional networks. Results show that while partial fine-tuning improves performance, models struggle to generalize across VoIP codecs. We hypothesize this limitation stems from inadequate modeling of nonlinear feature interactions in conventional fine-tuning architectures. To address this, we employ Kolmogorov-Arnold Networks (KAN) for fine-tuning. Empirical results show that KAN-based fine-tuning consistently outperforms the baselines and establishes a new state-of-the-art on KEYAC.

cs.CR↗