arXiv subjects

İpek Abasıkeleş Turgut

Publications and source records attributed to İpek Abasıkeleş Turgut.

4 recordsLinked to original sources

arXiv2026

IPEK: Event-Criticality-Aware Evidential Trust Management against Strategic Insider Attacks in Vehicular Ad Hoc Networks

Event-based trust management in vehicular ad hoc networks is vulnerable to strategic insider attackers who report trivial events correctly to build reputation, falsify reports on safety-critical events, and corrupt the trust feedback they provide about other vehicles. This paper proposes IPEK, an event-criticality-aware evidential trust management framework. IPEK combines an asymmetric local trust that scales rewards and penalties with event severity and location criticality, a reporter-credibility test that discounts feedback deviating systematically from the consensus of witnesses, and a Dempster-Shafer global trust that fuses feedback with Yager's rule, historical combination and risk accentuation. Closed-form properties quantify how quickly trust is lost and regained and show why Yager's rule keeps the global state revisable. IPEK is evaluated in simulation against a trust cascading-based dissemination scheme and the beta reputation system under eight attacks, with severity-, location-, on-off- and continuously triggered attackers falsifying their reports alone or also their feedback, at attacker ratios from 10% to 40%. Threshold-free separation measures are complemented by closed-loop revocation with frozen thresholds. With up to 25% attackers, IPEK achieves a significantly higher F1-score than both baselines under all attacks, with false positive rates of 0.9-15.5% against 7.8-49.7% and 26.0-41.4%, and it remains robust up to 40% attackers when only reports are falsified. Under fully colluding attackers that also corrupt feedback, all methods break down beyond about one third of attackers; this envelope and a longer detection delay are reported as limitations.

cs.NI↗

arXiv2026

Evaluating a Layered Prompt-Injection Defence for the Model Context Protocol: A Record-Level Audit of Decision Conventions, Corpus Provenance and Reproducibility

The Model Context Protocol (MCP) extends the prompt-injection attack surface of large language model applications to tool descriptions, parameter schemas and tool outputs. Defences for it report detection figures that are not comparable, because each is measured on its authors' own corpus under a decision convention that is rarely stated. This paper audits one such evaluation at the level of individual decision records. Its object is CASCADE, a fully local layered defence (rule matching, embedding similarity and an optional local language-model review), run in three configurations on a frozen 5,000-sample corpus under a pinned code revision; the corpus, the per-sample records and the analysis scripts are public. Four findings result. First, the convention that collapses allow, review and block onto a binary label sets the headline: the pipeline without review reports an 11.70% false-positive rate when referrals count as positives and 1.51% when only denials do, while referring 68.5% of all traffic to a human. Second, the construction of the corpus shapes the aggregate: 65.6% of records are texts placed in one of five fixed wrappers, the wrapper alone identifies the label, and for the same text wrapping lowers the false-positive rate from 21.2% to 3.2% and raises detection from 86.0% to 98.0%. Third, the published description does not identify what ran: the rule module inside the pipeline is not the rule engine evaluated alone; the block threshold produced denials only among the first 48 requests; and, without review, every later denial, including all 23 denials of benign requests, came from an output guard run on an echo of the input. Fourth, the local review model, invoked on 32.6% of requests at 2.5 s each, changes no binary outcome but converts 1,492 referrals into denials. A ten-item reporting checklist is derived from these findings.

cs.CR↗

arXiv2026

SAFE: Spatially-Aware Feedback Enhancement for Fault-Tolerant Trust Management in Event-Based VANETs

In event-based trust management for vehicular ad hoc networks (VANETs), vehicles that witness a road event broadcast its state, and vehicles that later witness the same event score the earlier broadcasters in feedback reports sent to a central decision unit (CDU). When the event state changes, an honest vehicle that reported the previous state receives negative feedback, as the evaluator compares an out-of-date message with the new state. This stale-witness problem is hypothesised to be a major source of unfair penalisation of honest vehicles. To address it, SAFE (Spatially-Aware Feedback Enhancement) is proposed. In SAFE, vehicles continue to record event messages after their decision and throughout the witness area, and send an updated feedback report when they leave it. SAFE was compared with the trust cascading-based emergency message dissemination model (TCEMD) in attack-free highway scenarios simulated with OMNeT++, Veins and Simulation of Urban MObility (SUMO). In the single-event scenario, the negative-feedback rate in the two update rounds after the state change decreased from 53.8% to 24.7% and from 55.6% to 8.3%, and the number of distinct honest vehicles blacklisted decreased from 20 to 6. In the multi-event scenario, the negative-feedback rate remained at or below 1.3% in SAFE, compared with up to 77.6% in TCEMD, and the share of trust evaluations ending in an untrusted label decreased from 22.7% to at most 1.7%. These gains required 2.3 to 5.0 times more feedback entries. Experiments with two decision distances showed that a shorter gap between the decision and witness distances reduced stale feedback in both schemes, supporting the stale-witness hypothesis

cs.NI↗

arXiv2017

Performance Evaluation of Mobile Base Station under different Network Sizes on Cluster-Based Wireless Sensor Networks

The position of the base station (BS) in wireless sensor networks (WSNs) has a significant impact on network lifetime. This paper suggests a mobile BS positioning algorithm for cluster-based WSNs, which considers both the location and the remaining energy level of the cluster heads in the network and evaluate the performance of the algorithm under different values of network sizes, including 100m x 100m, 200m x 200m and 300m x 300m. Simulations are conducted by using OMNeT++ and proposed method is compared with two different static BS positions, including central and external, on HEED protocol. The results show that mobile BS performs better than both central and external BS positions under all network sizes. Besides, the performance difference between the proposed method and the others increases as the size of the network increases, which demonstrates that the proposed mobile BS positioning also provides scalability.

cs.NI↗