Can attacks reduce Age of Information?

We study a monitoring system in which a single source sends status updates to a monitor through a communication channel. The communication channel is modeled as a queueing system, and we assume that attacks occur following a random process. When an attack occurs, all packets in the queueing system are discarded. While one might expect attacks to always negatively impact system performance, we demonstrate in this paper that, from the perspective of Age of Information (AoI), attacks can in some cases reduce the AoI. Our objective is to identify the conditions under which AoI is reduced and to determine the attack rate that minimizes or reduces AoI. First, we analyze single and tandem M/M/1/1 queues with preemption and show that attacks cannot reduce AoI in these cases. Next, we examine a single M/M/1/1 queue without preemption and establish necessary and sufficient conditions for the existence of an attack rate that minimizes AoI. For this scenario, we also derive an upper bound for the optimal attack rate and prove that it becomes tight when the arrival rate of updates is very high. Through numerical experiments, we observe that attacks can reduce AoI in tandem M/M/1/1 queues without preemption, as well as in preemptive M/M/1/2 and M/M/1/3 queues. Furthermore, we show that the benefit of attacks on AoI increases with the buffer size.

Authors

Institutions

Publication Details

Journal
Performance Evaluation
Published
2025-06-02
DOI
https://doi.org/10.1016/j.peva.2025.102498
Citations
1
Primary Topic
Age of Information Optimization
Type
article
Field-Weighted Citation Impact
1.49

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Can attacks reduce Age of Information?

Mohamad Assaad, Josu Doncel
1 citations
Performance Evaluation
Age of Information Optimization
1.49
article

Can attacks reduce Age of Information?

Mohamad Assaad, Josu Doncel
article en
1 citations

Abstract

We study a monitoring system in which a single source sends status updates to a monitor through a communication channel. The communication channel is modeled as a queueing system, and we assume that attacks occur following a random process. When an attack occurs, all packets in the queueing system are discarded. While one might expect attacks to always negatively impact system performance, we demonstrate in this paper that, from the perspective of Age of Information (AoI), attacks can in some cases reduce the AoI. Our objective is to identify the conditions under which AoI is reduced and to determine the attack rate that minimizes or reduces AoI. First, we analyze single and tandem M/M/1/1 queues with preemption and show that attacks cannot reduce AoI in these cases. Next, we examine a single M/M/1/1 queue without preemption and establish necessary and sufficient conditions for the existence of an attack rate that minimizes AoI. For this scenario, we also derive an upper bound for the optimal attack rate and prove that it becomes tight when the arrival rate of updates is very high. Through numerical experiments, we observe that attacks can reduce AoI in tandem M/M/1/1 queues without preemption, as well as in preemptive M/M/1/2 and M/M/1/3 queues. Furthermore, we show that the benefit of attacks on AoI increases with the buffer size.

Performance EvaluationVol. 169
University of the Basque Country (ES), CentraleSupélec (FR)
Hezkuntza, Hizkuntza Politika Eta Kultura Saila, Eusko Jaurlaritza
Openalex Percentile: Top 17%
Age of Information Optimization
1.49
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Can attacks reduce Age of Information? — Mohamad Assaad, Josu Doncel · Performance Evaluation (2025) | TGRS Research Map | TGRS