Pith. sign in

REVIEW 4 major objections 5 minor 54 references

Investigating Timing-Based Information Leakage in Data Flow-Driven Real-Time Systems

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read By measuring its own response times, a low-priority observer task can predict when a high-priority victim task will execute its critical jobs.

desk verdict Real leakage mechanism, but the evaluation oversells it: weak baseline, unvalidated clustering, and no temporal holdout. read the letter →

arxiv 2506.01991 v2 pith:Y3UDVA4E submitted 2025-05-18 cs.DC cs.SYeess.SY

Mohammad Fakhruddin Babar , Zain A. H. Hammadeh , Mohammad Hamad , Monowar Hasan This is my paper · ORCID
classification cs.DCcs.SYeess.SY
keywords informationleakagereal-timesystemssidechannelfixed-priorityschedulingresponsetimeanalysisprobabilisticsuffixtreeK-meansclusteringdual-modeexecution
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper claims that in fixed-priority real-time systems where tasks alternate between short typical executions and long critical executions, a low-priority task can learn the high-priority victim's execution pattern just by timing itself. The response time of the observer carries an imprint of the interference caused by the victim's jobs, and longer critical jobs push that response time upward. The authors build a probabilistic suffix tree over the observer's response-time history and use K-means clustering to split those response times into a 'typical victim' cluster and a 'critical victim' cluster, then predict future critical arrivals from the deepest matching suffix. This matters because the result would mean an unprivileged task can detect and predict safety-critical mode switches, enabling targeted denial-of-service or actuation-blocking attacks. In synthetic tasksets, an ArduPilot-style case study, and two physical demonstrations, the inference beats random guessing, with false-positive rates below 25 percent.

What carries the argument

The load-bearing mechanism is the combination of a probabilistic suffix tree (a tree of suffixes of the observer's response-time sequence, each node storing the probability of the next response-time symbol) and K-means clustering (which partitions the observer's response times into two clusters and yields a threshold). The PST performs the prediction: given the last few observed response times, it looks up the deepest matching suffix and returns the most likely next response time. The cluster threshold performs the classification: a predicted response time above the threshold is attributed to a critical victim job, one below it to a typical victim job. The underlying enabler is the standard fixed-priority response-time recurrence, which quantifies how the victim's longer critical execution inflates the observer's response time.

What would settle it

Run a fixed-priority system with a victim whose typical and critical execution times differ by only a small amount, or with other high-priority tasks whose own typical and critical execution times vary widely, collect the observer's response times, and check whether K-means produces a threshold with false-positive rate below 25 percent; if the two response-time distributions overlap substantially, the claimed inference fails.

Watch

Extended reading notes

Core claim

The central claim is that the fixed-priority scheduler leaks information through response-time interference: the observer's measured response time $R_o^a$ varies with whether higher-priority jobs, especially the victim's, executed in typical or critical mode. The authors show this by extending standard response-time analysis $R_i = B_i + C_i + \sum_{\tau_j \in hp(\tau_i)} \lceil R_i/T_j \rceil C_j$ to max/min response-time ranges and then demonstrating that observed response times of the observer can be clustered into two groups whose boundary separates the victim's critical jobs from its typical jobs. Once the cluster threshold is learned offline, a probabilistic suffix tree converts a short history of the observer's own recent response times into a prediction of the next victim mode. On synthetic workloads the approach achieves more than 55 percent inference precision for critical arrival rates up to 30 percent and less than 25 percent false positives; on the ArduPilot taskset precision is 50 to 70 percent; and on the surveillance demonstration the false-positive rate is 16.45 percent.

Load-bearing premise

The whole inference rests on the assumption that the victim's critical jobs are long enough, and the timing variation from all other higher-priority tasks is small enough, that the observer's response times split into two cleanly separable clusters.

Editorial extensions

If this is right

  • A compromised low-priority task can act as a side-channel sensor: it needs only its own response times and a system clock, and its training phase can be done offline from public scheduling parameters.
  • Rare critical arrivals are the easiest to detect: with 10 percent critical jobs the precision is above 70 percent and false positives stay near 15 percent, which is exactly the regime an attacker would target.
  • The attacker's footprint is small: about 1 MB of memory and roughly 50 microseconds of runtime inference overhead on a Raspberry Pi 4, so the observer can stay within its timing budget and remain stealthy.
  • Longer training and longer observation histories do not improve inference, so an observer needs only a short recent window of about 10 response times to make predictions.
  • The same leakage appears on realistic platforms: a manufacturing robot can be frozen by exploiting the predicted critical timing, and a motion-triggered surveillance system leaks critical motion detections with a false-positive rate of 16.45 percent.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural defense test follows from the paper's own mechanism: if system designers shrink the gap between $C^{\mathrm{typ}}_v$ and $C^{\mathrm{cri}}_v$, or make other higher-priority tasks' execution times more variable, the K-means clusters should merge and the inference should degrade; the paper does not run this experiment.
  • The PST is essentially learning a finite-memory model of a hidden Markov process, so the same method should apply to inferring any mode switch that changes a task's execution time, such as input-dependent branches or sensor-triggered phases, not just the two-mode model considered here.
  • Because the paper compares only against random selection, a stronger baseline, such as always predicting the majority typical class, would already match the precision figures for low critical rates; the paper's real advantage is the low false-positive rate for the rare critical class, not raw accuracy.
  • The saturation of precision with observation-window length suggests the attack is robust even under memory constraints, but it also implies that short-memory schedule randomization or dummy work inserted around critical jobs could be a low-cost defense worth testing.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper studies timing-based information leakage in fixed-priority preemptive real-time systems where tasks have two execution modes (typical and critical). The authors propose that a low-priority "observer" task can infer, and even predict, the future critical-mode arrivals of a high-priority "victim" task by measuring its own response times. The technical approach combines standard response-time analysis (Eq. 1) with a probabilistic suffix tree (PST) for predicting future response times and K-means clustering to map observed response times to victim typical/critical modes (Sections III and IV). The evaluation uses synthetic UUniFast tasksets with varying utilization and critical arrival rates, an ArduPilot case study, overhead measurements on a Raspberry Pi 4, and two physical demonstrations (a robot arm and a surveillance system). The paper reports inference precision above a random coin-toss baseline and false positive rates below 25%.

Significance. If the central claim is established, the paper would be a useful contribution to real-time systems security: it extends prior side-channel and covert-channel work (e.g., FrameLeaker, ScheduLeak) to non-deterministic dual-mode data-flow tasks and provides a low-footprint inference method that works on embedded platforms. The paper has clear strengths: the response-time recurrence in Eq. (1) is standard and correctly used; the design-space exploration is systematic; the overhead measurements are concrete; and the two physical demonstrations give the work practical grounding. However, the current evaluation does not yet validate the headline claim of predicting future critical arrivals, and the reported metrics are not sufficient to demonstrate that the K-means clusters correspond to victim modes. The central idea is defensible, but the load-bearing evidence needs rework.

major comments (4)
  1. [Section V-A3, Section V-A5] The inference precision metric, IP = successful predictions / total observed jobs, is class-prior dominated. In Case I only 10% of victim jobs are critical, so a trivial classifier that always predicts "typical" attains 90% precision, well above the reported 71% for the proposed method. The random-selection baseline, which is near 50%, is not a meaningful adversary baseline under class imbalance. The paper should report balanced accuracy, per-class precision and recall (especially for the critical class), and compare against a majority-class baseline. This is load-bearing for the claim that critical invocations are actually extracted.
  2. [Section V-A4, Section I] The evaluation does not test the stated claim of predicting future critical arrivals. The workflow randomly selects a victim job from the same response-time trace used for training, and the paper does not describe a temporal holdout separating training and prediction intervals. Algorithm 1 has training and prediction durations, but the experiments do not specify that the predicted jobs lie strictly after the training window. The headline claim requires evaluating on a future held-out interval, not on jobs drawn from the training distribution.
  3. [Section IV-C, Section V-A1] The K-means step that labels observer response times as "victim critical" versus "victim typical" is not validated. In Eq. (1), the observer's response time accumulates interference from every higher-priority task, so a large response time can be caused by critical jobs of any higher-priority task, not necessarily the victim. Because the synthetic setup sets Ctyp_i = 0.7 Ccri_i for every task, other tasks' mode spreads can shift the observer's response time by as much as the victim's mode spread. The paper reports no cluster purity, no critical-class true-positive rate, and no comparison of the inferred clusters against ground-truth victim labels. This cluster-to-mode correspondence is load-bearing for the entire inference method.
  4. [Section IV-B, Section V-A1] The method has several parameters (PST probability threshold Pmin, maximum suffix length L, number of clusters K, and the critical arrival rate model) that are chosen by hand or drawn from a uniform distribution. In particular, the assumption that typical/critical markers are independent uniform draws at each job is an ad-hoc model that is not derived from the data-flow-driven execution model motivating the paper. The paper should include a sensitivity analysis over these parameters and test at least one workload model in which mode switches have temporal structure, since the PST is specifically motivated by temporal patterns.
minor comments (5)
  1. [Section III-A] The text says the observer infers future victim arrivals "by measuring the victim's own response times," but the method measures the observer's own response times; this should be corrected.
  2. [Section IV-C] In the clustering illustration, the text says "we train the PST ... and calculate the response time of τv," which appears to be a typo for the response time of the observer τo; the following sentence refers to the measured response times of the observer task.
  3. [Section V-C, Abstract] The timing overhead is reported inconsistently: the abstract states 50 ms, Section V-C reports roughly 50 us for a 50-hyperperiod training duration, and also mentions about 20 ms for a 20-hyperperiod duration. Please clarify the units and which configuration corresponds to each reported value.
  4. [References] Several references are duplicated: Völp et al. appears as [16], [22], and [48]; Son et al. appears as [17] and [47]; Ghassami et al. appears as [18] and [53]. These should be consolidated.
  5. [Example 1] In the PST example, the displayed probabilities are labeled with the continuation suffix rather than the conditioning suffix; e.g., P(r1_o | r1_o r2_o r1_o) should presumably be P(r1_o | r1_o r2_o). Please revise the notation for clarity.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the inference model is an empirically evaluated classifier, not a definitional restatement of its inputs.

full rationale

The paper's derivation chain is not circular. The response-time model in Eq. (1) is the standard fixed-priority recurrence, and the observation that a victim's longer critical execution increases the observer's response time is a direct consequence of that model rather than an assumption equivalent to the paper's conclusion. The PST and K-means components are standard statistical tools cited to external literature; the PST is fitted to observer response-time traces, and the K-means threshold is chosen unsupervised, after which the resulting classifier is evaluated against ground-truth typical/critical labels (Section V-A4). Thus the central claim—that response times can be classified to infer victim mode—is an empirical hypothesis tested on labeled data, not a quantity defined as its own prediction. The paper does invoke two prior works by the same authors ([31], [32]) but only for synthetic task-generation parameters, which is not load-bearing for the leakage claim. The paper also states a limitation: 'we cannot ensure deterministic guarantees or provide bounds on successful inference' (Section IV), which is consistent with an empirical approach rather than a circular one. Concerns about in-sample evaluation, class-prior-dominated precision, or cluster-purity validation are correctness and threat-validity issues, not circularity, and do not make the derivation equivalent to its inputs.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The paper's central demonstration rests on a standard response-time model plus several freely chosen simulation parameters. The strongest hidden dependencies are the assumed size gap between typical and critical execution times and the independent and identically distributed uniform marking of jobs, which together make the reported inference feasible or limit it. No new physical entities are introduced.

free parameters (5)
  • Ctyp/Ccri ratio = 0.7
    In synthetic tasksets, Ctyp is set to 0.7 times Ccri; this is what makes critical jobs long enough for response-time clusters to separate. The paper does not test smaller ratios.
  • Critical arrival rate = 10%-30%
    Jobs are marked critical with an independent uniform probability drawn from these values; the reported precision and false positives depend on this rate.
  • PST probability threshold Pmin = 0.001
    Suffixes are kept only if the conditional probability exceeds Pmin, per Section V-A1; this choice is not justified or swept.
  • Number of clusters K = 2
    K-means is run with K equal to 2 to separate typical from critical response times; this presumes exactly two separable response-time classes.
  • Maximum suffix length L = not reported in experiments
    Algorithm 1 takes L as an input; the example uses depth 3, but the experiment settings for L are not stated.
assumptions (5)
  • domain assumption Fixed-priority preemptive uniprocessor scheduling with implicit deadlines, using the response-time recurrence in Eq. (1).
    The entire interference and response-time model comes from standard real-time response-time analysis, assumed in Sections II-A and III.
  • domain assumption All scheduling parameters, the schedule, and clocks are public to the adversary (noninterference model).
    Section II-B assumes the adversary knows parameters and can read clocks; without this, the observer cannot compute response-time ranges or run offline training.
  • domain assumption Critical execution time is at least typical execution time for every task.
    Section II-A asserts Ccri_i is greater than or equal to Ctyp_i; this monotonicity is what makes higher response times correspond to critical victim jobs.
  • ad hoc to paper Critical and typical job markers are drawn from an independent uniform distribution at each job arrival.
    Section V-A1 marks jobs as typical or critical following a uniform distribution with a given rate; this independent and identically distributed model removes temporal structure that a PST would need for genuine future prediction.
  • ad hoc to paper Observer response times cluster into two K-means groups aligned with victim modes, irrespective of arbitrary modes of other higher-priority tasks.
    Section IV-C assumes long critical jobs shift observer response times into a separable upper cluster; this is the load-bearing separation premise.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Investigating Timing-Based Information Leakage in Data Flow-Driven Real-Time Systems." pith.science (2026). https://pith.science/paper/Y3UDVA4E

@misc{pith2026250601991,
  author       = {Pith},
  title        = {Pith review of: Investigating Timing-Based Information Leakage in Data Flow-Driven Real-Time Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Y3UDVA4E}},
  note         = {Machine review of arXiv:2506.01991}
}
read the original abstract

Leaking information about the execution behavior of critical real-time tasks may lead to serious consequences, including violations of temporal constraints and even severe failures. We study information leakage for a special class of real-time tasks that have two execution modes, namely, typical execution (which invokes the majority of times) and critical execution (to tackle exceptional conditions). The data flow-driven applications inherit such a multimode execution model. In this paper, we investigate whether a low-priority "observer" task can infer the execution patterns of a high-priority "victim" task (especially the critical executions). We develop a new statistical analysis technique and show that by analyzing the response times of the low-priority task, it becomes possible to extract the execution behavior of the high-priority task. We test our approach against a random selection technique that arbitrarily classifies a job as critical. We find that correlating the observer's response times with the victim's jobs can result in higher precision in identifying critical invocations compared to a random guess. We conduct extensive evaluations with systemically generated workloads, including a case study using a UAV autopilot (ArduPilot) taskset parameters. We found that our inference algorithm can achieve relatively low false positive rates (less than 25%) with relatively low footprint (1 MB memory and 50 ms timing overhead on a Raspberry Pi 4 platform). We further demonstrate the feasibility of inference on two cyber-physical platforms: an off-the-shelf manufacturing robot and a custom-built surveillance system.

Figures

Figures reproduced from arXiv: 2506.01991 by the authors.

Figure 1
Figure 1. An example of a real-time system where a task has two execution modes. The victim task [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. A PST for a sequence of length 1200 generated with [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 4
Figure 4. Execution of τo has Co−λ unit of time for computation and λ time for inference. TABLE III: Simulation Parameters. Parameters Value Utilization, U 0%-90% Period T [100, 900] ms Hyperperiod 4500 ms Number of tasks, n [7, 20] Number of taskset for each utilization, Nu 100 Hence, it saves some budget to perform PST-based inference. We define a parameter, λ, as shown in [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figures from the paper (7 more)
Figure 3
Figure 3. Figure 3: Using K-means to isolate τo’s response times into two clusters. Instances of an observer task with response times above the threshold are caused due to critical invocation of the victim (square), while those that are below result in due to typical invocation of the vic…
Figure 5
Figure 5. Figure 5: Utilization vs precision. The statistical analysis [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 9
Figure 9. Figure 9: Inference precision for ArduPilot [35] taskset. The [PITH_FULL_IMAGE:figures/full_fig_p009_9.png]
Figure 10
Figure 10. Figure 10: False positive rates for ArduPilot [35] taskset. The [PITH_FULL_IMAGE:figures/full_fig_p009_10.png]
Figure 11
Figure 11. Figure 11: Timing overheads for building the model (left) and [PITH_FULL_IMAGE:figures/full_fig_p010_11.png]
Figure 12
Figure 12. Figure 12: Memory overheads for building the model (left) [PITH_FULL_IMAGE:figures/full_fig_p010_12.png]
Figure 14
Figure 14. Figure 14: A custom-built surveillance system used for [PITH_FULL_IMAGE:figures/full_fig_p011_14.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

54 extracted references · 53 canonical work pages

  1. [1]

    Real-time indoor autonomous vehicle test environment,

    J. P. How, B. Behihke, A. Frank, D. Dale, and J. Vian, “Real-time indoor autonomous vehicle test environment,” IEEE Control Systems Magazine, vol. 28, no. 2, pp. 51–64, 2008

    work page 2008

  2. [2]

    Real-time scheduling of an automated manufacturing center,

    N. Raman, R. V . Rachamadugu, and F. B. Talbot, “Real-time scheduling of an automated manufacturing center,” European Journal of Operational Research , vol. 40, no. 2, pp. 222–242, 1989

    work page 1989

  3. [3]

    A resilient real-time system design for a secure and reconfigurable power grid,

    H. Qi, X. Wang, L. M. Tolbert, F. Li, F. Z. Peng, P. Ning, and M. Amin, “A resilient real-time system design for a secure and reconfigurable power grid,” IEEE Transactions on Smart Grid , vol. 2, no. 4, pp. 770–781, 2011

    work page 2011

  4. [4]

    Real-time locating systems to improve healthcare delivery: A systematic review,

    K. M. Overmann, D. T. Wu, C. T. Xu, S. S. Bindhu, and L. Barrick, “Real-time locating systems to improve healthcare delivery: A systematic review,” Journal of the American Medical Informatics Association, vol. 28, no. 6, pp. 1308–1317, 2021

    work page 2021

  5. [5]

    Real-time optimal resource allocation for embedded uav communication systems,

    M.-N. Nguyen, L. D. Nguyen, T. Q. Duong, and H. D. Tuan, “Real-time optimal resource allocation for embedded uav communication systems,” IEEE Wireless Communications Letters, vol. 8, no. 1, pp. 225–228, 2018

    work page 2018

  6. [6]

    The bird mission is completed for launch with the pslv-c3 in 2001,

    K. Brieß, W. Bärwald, F. Lura, S. Montenegro, D. Oertel, H. Studemund, and G. Schlotzhauer, “The bird mission is completed for launch with the pslv-c3 in 2001,” in Small Satellites for Earth Observation, Digest of the 3rd International Symposium of the International Academy of Astronautics, Berlin, April 2-6, 2001, (H. P. Röser, R. Sandau, and A. Valenzue...

    work page 2001

  7. [7]

    Event-driven multithreading execution platform for real-time on-board software systems,

    Z. A. H. Hammadeh, T. Franz, O. Maibaum, A. Gerndt, and D. Lüdtke, “Event-driven multithreading execution platform for real-time on-board software systems,” in Proceedings of the 15th annual workshop on Operating Systems Platforms for Embedded Real-time Applications , pp. 29–34, July 2019

    work page 2019

  8. [8]

    Budgeting Under-Specified Tasks for Weakly-Hard Real-Time Systems,

    Z. A. H. Hammadeh, S. Quinton, M. Panunzio, R. Henia, L. Rioux, and R. Ernst, “Budgeting Under-Specified Tasks for Weakly-Hard Real-Time Systems,” in 29th Euromicro Conference on Real-Time Systems (ECRTS

    work page

Show all 54 references
  1. [9]

    Analysis of the cyber attack on the ukrainian power grid,

    D. U. Case, “Analysis of the cyber attack on the ukrainian power grid,” Electricity information sharing and analysis center (E-ISAC) , vol. 388, no. 1-29, p. 3, 2016

    2016

  2. [10]

    Experimental security analysis of a modern automobile,

    K. Koscher, A. Czeskis, F. Roesner, S. Patel, T. Kohno, S. Checkoway, D. McCoy, B. Kantor, D. Anderson, H. Shacham, et al., “Experimental security analysis of a modern automobile,” in 2010 IEEE symposium on security and privacy , pp. 447–462, IEEE, 2010

    2010

  3. [11]

    Drone hack,

    D. P. Shepard, J. A. Bhatti, and T. E. Humphreys, “Drone hack,” Gps World, vol. 23, no. 8, pp. 30–33, 2012

    2012

  4. [12]

    Virtualdrone: virtual sensing, actuation, and communication for attack-resilient unmanned aerial systems,

    M.-K. Yoon, B. Liu, N. Hovakimyan, and L. Sha, “Virtualdrone: virtual sensing, actuation, and communication for attack-resilient unmanned aerial systems,” in Proceedings of the 8th international conference on cyber-physical systems, pp. 143–154, 2017

    2017

  5. [13]

    Design and analysis of security attacks against critical smart grid infrastructures,

    B. Min and V . Varadharajan, “Design and analysis of security attacks against critical smart grid infrastructures,” in 2014 19th International Conference on Engineering of Complex Computer Systems , pp. 59–68, IEEE, 2014

    2014

  6. [14]

    A novel side-channel in real-time schedulers,

    C.-Y . Chen, S. Mohan, R. Pellizzoni, R. B. Bobba, and N. Kiyavash, “A novel side-channel in real-time schedulers,” in2019 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), pp. 90–102, IEEE, 2019

    2019

  7. [15]

    Taskshuffler: A schedule randomization protocol for obfuscation against timing inference attacks in real-time systems,

    M.-K. Yoon, S. Mohan, C.-Y . Chen, and L. Sha, “Taskshuffler: A schedule randomization protocol for obfuscation against timing inference attacks in real-time systems,” in 2016 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS) , pp. 1–12, IEEE, 2016

    2016

  8. [16]

    Avoiding timing channels in fixed-priority schedulers,

    M. Völp, C.-J. Hamann, and H. Härtig, “Avoiding timing channels in fixed-priority schedulers,” in Proceedings of the 2008 ACM Symposium on Information, Computer and Communications Security (ASIACCS) , pp. 44–55, 2008

    2008

  9. [17]

    Covert timing channel analysis of rate monotonic real-time scheduling algorithm in mls systems,

    J. Son and J. Alves-Foss, “Covert timing channel analysis of rate monotonic real-time scheduling algorithm in mls systems,” in2006 IEEE Information Assurance Workshop, pp. 361–368, June 2006

    2006

  10. [18]

    Capacity limit of queueing timing channel in shared fcfs schedulers,

    A. Ghassami, X. Gong, and N. Kiyavash, “Capacity limit of queueing timing channel in shared fcfs schedulers,” in 2015 IEEE International Symposium on Information Theory (ISIT) , pp. 789–793, June 2015

    2015

  11. [19]

    Richardson and S

    M. Richardson and S. Wallace, Getting started with Raspberry Pi . O’Reilly Media, Inc., 2012

    2012

  12. [20]

    Formal analysis of sporadic overload in real-time systems,

    S. Quinton, M. Hanke, and R. Ernst, “Formal analysis of sporadic overload in real-time systems,” in 2012 Design, Automation & Test in Europe Conference & Exhibition (DATE) , pp. 515–520, IEEE, 2012

    2012

  13. [21]

    A survey of research into mixed criticality systems,

    A. Burns and R. I. Davis, “A survey of research into mixed criticality systems,” ACM Comput. Surv., vol. 50, Nov. 2017

    2017

  14. [23]

    Learning execution contexts from system call distribution for anomaly detection in smart embedded system,

    M.-K. Yoon, S. Mohan, J. Choi, M. Christodorescu, and L. Sha, “Learning execution contexts from system call distribution for anomaly detection in smart embedded system,” in Proceedings of the Second International Conference on Internet-of-Things Design and Implementation, pp. ...

    2017

  15. [24]

    You can’t always check what you wanted: : Selective checking and trusted execution to prevent false actuations in real-time internet-of-things,

    M. Hasan and S. Mohan, “You can’t always check what you wanted: : Selective checking and trusted execution to prevent false actuations in real-time internet-of-things,” in 2023 IEEE 26th International Symposium on Real-Time Distributed Computing (ISORC) , pp. 42–53, 2023

    2023

  16. [25]

    Contego: An Adaptive Framework for Integrating Security Tasks in Real-Time Systems,

    M. Hasan, S. Mohan, R. Pellizzoni, and R. B. Bobba, “Contego: An Adaptive Framework for Integrating Security Tasks in Real-Time Systems,” in 29th Euromicro Conference on Real-Time Systems (ECRTS 2017), pp. 23:1–23:22, 2017

    2017

  17. [26]

    Beyond just safety: Delay-aware security monitoring for real-time control systems,

    M. Hasan, S. Mohan, R. B. Bobba, and R. Pellizzoni, “Beyond just safety: Delay-aware security monitoring for real-time control systems,” ACM Trans. Cyber-Phys. Syst. , vol. 6, Sept. 2022

    2022

  18. [27]

    Finding response times in a real-time system,

    M. Joseph and P. Pandya, “Finding response times in a real-time system,” The Computer Journal , vol. 29, no. 5, pp. 390–395, 1986

    1986

  19. [28]

    Variations on probabilistic suffix trees: statistical modeling and prediction of protein families,

    G. Bejerano and G. Yona, “Variations on probabilistic suffix trees: statistical modeling and prediction of protein families,” Bioinformatics, vol. 17, no. 1, pp. 23–43, 2001

    2001

  20. [29]

    Unsupervised k-means clustering algorithm,

    K. P. Sinaga and M.-S. Yang, “Unsupervised k-means clustering algorithm,” IEEE access, vol. 8, pp. 80716–80727, 2020

    2020

  21. [30]

    A comparison of heuristic procedures for minimum within-cluster sums of squares partitioning,

    M. J. Brusco and D. Steinley, “A comparison of heuristic procedures for minimum within-cluster sums of squares partitioning,” Psychometrika, vol. 72, no. 4, pp. 583–600, 2007

    2007

  22. [31]

    A new covert channel in fixed-priority real-time multiframe tasks,

    M. F. Babar and M. Hasan, “A new covert channel in fixed-priority real-time multiframe tasks,” in 2024 IEEE 27th International Symposium on Real-Time Distributed Computing (ISORC) , pp. 1–6, 2024

    2024

  23. [32]

    Deeptrust RT : Confidential deep neural inference meets real-time!,

    M. F. Babar and M. Hasan, “Deeptrust RT : Confidential deep neural inference meets real-time!,” in 36th Euromicro Conference on Real-Time Systems (ECRTS 2024) , pp. 13–1, Schloss Dagstuhl–Leibniz-Zentrum für Informatik, 2024

    2024

  24. [33]

    Priority assignment for global fixed priority pre-emptive scheduling in multiprocessor real-time systems,

    R. I. Davis and A. Burns, “Priority assignment for global fixed priority pre-emptive scheduling in multiprocessor real-time systems,” in 2009 30th IEEE Real-Time Systems Symposium , pp. 398–409, IEEE, 2009

    2009

  25. [34]

    The rate monotonic scheduling algorithm: Exact characterization and average case behavior,

    J. Lehoczky, L. Sha, and Y . Ding, “The rate monotonic scheduling algorithm: Exact characterization and average case behavior,” in RTSS, vol. 89, pp. 166–171, 1989

    1989

  26. [35]

    https://github.com/ardupilot/ardupilot

    “https://github.com/ardupilot/ardupilot.”

  27. [36]

    Leaking your engine speed by spectrum analysis of real-time scheduling sequences,

    S. Liu, N. Guan, D. Ji, W. Liu, X. Liu, and W. Yi, “Leaking your engine speed by spectrum analysis of real-time scheduling sequences,” Journal of Systems Architecture, vol. 97, pp. 455–466, 2019

    2019

  28. [37]

    Piarm documentation,

    SunFounder, “Piarm documentation,” 2023. Accessed: 2023-09-22

    2023

  29. [38]

    Adafruit Motor Shield

    “Adafruit Motor Shield.” https://learn.adafruit.com/ adafruit-motor-shield-v2-for-arduino/overview. [Accessed 01-03-2025]

    2025

  30. [39]

    Robot hat

    SunFounder, “Robot hat.” https://github.com/sunfounder/robot-hat, n.d. Accessed: 2025-01-22

    2025

  31. [40]

    Retrieved September 30, 2023

    Motion Project, “Motion.” https://motion-project.github.io/, 2021. Retrieved September 30, 2023

    2021

  32. [41]

    SoK: Security in real-time systems,

    M. Hasan, A. Kashinath, C.-Y . Chen, and S. Mohan, “SoK: Security in real-time systems,” ACM Computing Surveys , vol. 56, no. 9, pp. 1–31, 2024

    2024

  33. [42]

    Real-time systems security through scheduler constraints,

    S. Mohan, M. K. Yoon, R. Pellizzoni, and R. Bobba, “Real-time systems security through scheduler constraints,” in 2014 26th Euromicro Conference on Real-Time Systems , pp. 129–140, IEEE, 2014. 12

    2014

  34. [43]

    A generalized model for preventing information leakage in hard real-time systems,

    R. Pellizzoni, N. Paryab, M.-K. Yoon, S. Bak, S. Mohan, and R. B. Bobba, “A generalized model for preventing information leakage in hard real-time systems,” in 21st IEEE Real-Time and Embedded Technology and Applications Symposium , pp. 271–282, 2015

    2015

  35. [44]

    Integrating security constraints into fixed priority real-time schedulers,

    S. Mohan, M.-K. Yoon, R. Pellizzoni, and R. B. Bobba, “Integrating security constraints into fixed priority real-time schedulers,” Real-Time Systems, vol. 52, pp. 644–674, 2016

    2016

  36. [45]

    Partitioned real-time scheduling for preventing information leakage,

    D. Kang, I. Jung, K. Lee, and H. Baek, “Partitioned real-time scheduling for preventing information leakage,” IEEE Access, vol. 10, pp. 22712– 22723, 2022

    2022

  37. [46]

    Prediction of abnormal temporal behavior in real-time systems,

    M. Hamad, Z. A. H. Hammadeh, S. Saidi, V . Prevelakis, and R. Ernst, “Prediction of abnormal temporal behavior in real-time systems,” in Proceedings of the 33rd Annual ACM Symposium on Applied Computing, SAC ’18, (New York, NY , USA), p. 359–367, Association for Computing Mach...

    2018

  38. [47]

    Covert timing channel analysis of rate monotonic real- time scheduling algorithm in mls systems,

    J. Son et al. , “Covert timing channel analysis of rate monotonic real- time scheduling algorithm in mls systems,” in 2006 IEEE Information Assurance Workshop, pp. 361–368, IEEE, 2006

    2006

  39. [48]

    Avoiding timing channels in fixed-priority schedulers,

    M. Völp, C.-J. Hamann, and H. Härtig, “Avoiding timing channels in fixed-priority schedulers,” in Proceedings of the 2008 ACM symposium on Information, computer and communications security , pp. 44–55, 2008

    2008

  40. [49]

    On confidentiality- preserving real-time locking protocols,

    M. Völp, B. Engel, C.-J. Hamann, and H. Härtig, “On confidentiality- preserving real-time locking protocols,” in 2013 IEEE 19th Real-Time and Embedded Technology and Applications Symposium (RTAS) , pp. 153–162, IEEE, 2013

    2013

  41. [50]

    Mitigating timing side channel in shared schedulers,

    S. Kadloor, N. Kiyavash, and P. Venkitasubramaniam, “Mitigating timing side channel in shared schedulers,” IEEE/ACM transactions on networking, vol. 24, no. 3, pp. 1562–1573, 2015

    2015

  42. [51]

    Quantifying the information leakage in timing side channels in deterministic work-conserving schedulers,

    X. Gong and N. Kiyavash, “Quantifying the information leakage in timing side channels in deterministic work-conserving schedulers,” IEEE/ACM Transactions on Networking, vol. 24, no. 3, pp. 1841–1852, 2015

    2015

  43. [52]

    Covert timing channel design for uniprocessor real-time systems,

    J. Kwak and J. Lee, “Covert timing channel design for uniprocessor real-time systems,” in Parallel and Distributed Computing, Applications and Technologies: 19th International Conference, PDCAT 2018, Jeju Island, South Korea, August 20-22, 2018, Revised Selected Papers 19 , pp...

    2018

  44. [53]

    Capacity limit of queueing timing channel in shared fcfs schedulers,

    A. Ghassami, X. Gong, and N. Kiyavash, “Capacity limit of queueing timing channel in shared fcfs schedulers,” in 2015 IEEE International Symposium on Information Theory (ISIT) , pp. 789–793, 2015

    2015

  45. [54]

    Work in progress: Exploring schedule-based side-channels in trustzone-enabled real-time systems,

    M. A. Aguida and M. Hasan, “Work in progress: Exploring schedule-based side-channels in trustzone-enabled real-time systems,” in 2022 IEEE 28th Real-Time and Embedded Technology and Applications Symposium (RTAS), pp. 301–304, IEEE, 2022. 13

    2022

  46. [2017]

    Bertogna, ed.), vol

    (M. Bertogna, ed.), vol. 76 of Leibniz International Proceedings in Informatics (LIPIcs) , (Dagstuhl, Germany), pp. 17:1–17:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik, 2017

    2017

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.