Replay Attack: Concepts and Defenses

• Replay Attack is a security threat where valid data transmissions are maliciously repeated to trick systems and undermine integrity.
• Detection and prevention strategies leverage layered authentication, statistical residual analysis, and adaptive signal processing to mitigate these threats.
• Applications span cryptographic protocols, cyber-physical systems, digital communications, and even genomic analyses, driving robust countermeasure research.

A replay attack is a security threat in which valid data transmissions are maliciously or fraudulently repeated or delayed, typically with the intent of tricking a system into executing an operation more than once or accepting a previously valid authentication step as a new, valid instance. Across domains—cryptographic authentication, cyber-physical systems, digital communications, adversarial machine learning, and network protocols—replay attacks manipulate the recurrence of legitimate events or data to subvert integrity, availability, or confidentiality. This article surveys the technical foundations, canonical attack scenarios, detection and prevention strategies, and implications of replay (repeat) attacks as reported in the literature.

1. Cryptographic Authentication Protocols

Replay attacks pose fundamental risks to systems based on encrypted challenge–response authentication, notably the Kerberos protocol. Traditional Kerberos workflows operate with a single long-term password, making them susceptible to message interception and replay, especially if an adversary captures session keys or service tickets.

To address this, a triple password extension requires the user to register three distinct passwords (Kc1, Kc2, Kc3) (Dua et al., 2013). During authentication:

• Kc1 decrypts the session key for TGS (Ticket Granting Server).
• The Authentication Server sends Kc2 and Kc3, encrypted, to TGS.
• TGS encrypts the session key for the application server with Kc2 and forwards Kc3 to the application server, which holds Kc3 for a final challenge.

This arrangement ensures that even if an attacker captures a valid ticket or session key, they cannot progress through all stages without knowing all user secrets. Further, the application server may initiate a timed challenge for Kc3, attenuating the risk of replay by requiring user liveness at the final step.

This multi-layer approach outperforms single-password schemes by ensuring replayed credentials fail unless all secrets are reproduced in real time—an infeasibility for typical adversaries given secure storage and exchange of passwords.

2. Cyber-Physical and Control Systems

In cyber-physical and industrial control contexts, replay attacks are characterized by the adversary recording valid sensor data (outputs) and/or actuator commands (inputs) during a legitimate interval and later replaying them to the controller or estimator. The typical aim is to mask plant anomalies or inject malicious commands while bypassing fault detection and standard anomaly detectors.

Advanced detection techniques rely on changes in the statistical covariance of residuals generated by parity space methods (Zhao et al., 2023). Under normal circumstances, the residual rₛ(k) = Zₛ[Yₛ(k) – Hᵤ,ₛUₛ(k)] is zero-mean Gaussian with covariance Θₛ. When a replay occurs, particularly if the replacement is partial, the covariance shifts to Θₛ^α = Θₛ + Δα, where Δα arises from transients due to mismatched initial state. Test statistics such as χ² or generalized likelihood ratio (GLR) are then compared against thresholds derived from the null distribution.

Augmenting this framework, the detector's sensitivity to replay transients can be enhanced using a cascaded marginally stable filter, which amplifies the covariance deviation caused by replay without degrading closed-loop performance. This active detection approach yields sharper "tails"—distinct, finite-length covariance signatures—that facilitate discrimination from persistent faults.

Optimally, the parity matrix Zₛ is designed (via SVD or trace/maximization indices) to maximize the statistical separation (e.g., tr(ΔαΘₛ⁻¹)) between nominal and replayed cases, allowing a tradeoff between detection probability and control impact.

3. Digital Communications and Physical-layer Replay Attacks

Replay attacks extend to the physical layer of digital communications, especially in mission-critical wireless environments such as space telemetry. Here, attackers intercept and replay entire RF transmissions at a later time, leveraging path differences and independent noise to induce destructive interference at the legitimate receiver.

Signal models for replayed transmission are expressed as

$$y_{GS1}(t) = h_{OR-GS1}(t,\tau) * x_{OR}(t) + n_{OR-GS1}(t) + h_{ADV-GS1}(t,\tau) * y_{ADV}(t) + n_{ADV-GS1}(t)$$

where h is the channel response (with multipath, Doppler), and $y_{ADV}(t)$ is the replayed signal (Benchoubane et al., 20 Jun 2025). The replay results in phase-incoherent superposition, sharply reducing SNR (up to –7.8 dB during reentry in test campaigns) and dramatically raising bit error rates (BER), particularly at critical gain thresholds.

Classic equalizers (CMA) fail to maintain synchronization in such an environment, as phase ambiguity caused by the replay signal misleads the demodulation process. The recommended mitigation couples a phase-coherency-dependent decision-directed (DD) equalizer (operating on amplitude/power, not phase) with a narrowed phase-locked loop (PLL) bandwidth. This architectural change restores synchronization resilience, reducing BER by up to 89% under the tested threat model without sacrificing benign-operating performance.

4. Adversarial Machine Learning and Sequential Perturbations

In deep RL (reinforcement learning) systems, sequential replay-like attacks are operationalized by training an adversarial transformer network (ATN) to apply bounded, quasi-imperceptible perturbations frame by frame (Tretschk et al., 2018). While each perturbation remains within a small $\ell_2$-norm ball, the cumulative effect of a repeated sequence can reprogram the agent towards an adversarial reward objective distinct from its original task. In the transfer learned system DQN(sₜ + gθ(sₜ)), the ATN is trained to maximize $r^A$ instead of $r^O$ while strictly enforcing a norm constraint via

$$\operatorname{clip}_p(x) = \begin{cases} p \cdot x / \|x\|_2 &\text{if } \|x\|_2 \ge p \ x &\text{otherwise} \end{cases}$$

Experiments show that, under this sequential perturbation regime, safety-critical RL controllers (drones, vehicles) can be surreptitiously redirected, emphasizing that periodic or repeated small attacks can be as harmful as direct policy tampering.

5. Machine Learning Defenses, Detection, and Generalization

Replay detection is a central concern in fields such as automatic speaker verification (ASV) where attackers replay genuine voice recordings. Discriminative features—extracted from high-frequency bands via inverted Mel warping—are more robust to device variability and enable better generalization than standard MFCC features, as confirmed on the ASV-Spoof 2017 database (Li et al., 2017). Multi-task learning architectures, such as Siamese neural networks with joint cross-entropy and distance losses, further improve replay detection equal error rates (EER), achieving reductions of approximately 26.8% relative to strong baselines (Platen et al., 2020).

Spatially-aware detection (e.g., using learnable beamformers with regularization for channel orthogonality and sparsity) enhances the robustness of replay speech detection systems, achieving substantial EER reductions and preserving performance in previously unseen acoustic environments (Neri et al., 19 Feb 2025).

In adversarial ML, repeated (statistically rare) queries flag traditional black-box attacks. By limiting adversarial queries to at most two per image and employing algorithms such as CMA-ES and finite difference gradient estimation in a tiled (low-dimensional) perturbation space, attackers can mount universal attacks while virtually eliminating repeated query signatures (Willmott et al., 2021).

6. System-level, Social, and Genomic Phenomena

Beyond core IT and communications, repeat attacks are structurally inseparable from phenomena such as:

• Spatiotemporally correlated terror incidents, where a prior attack increases event probability in nearby locations and times, forming a near-repeat process. This is statistically modeled via observed versus random-event hypotheses, often with latency distributions following attack-response connections between groups (Chuang et al., 2019).
• Genomic repeats, where accurate classification of repeat DNA sequences is central to understanding evolution and instability. Deep learning classifiers—such as Terrier, using convolutional/hierarchical networks—enable the high-resolution repeat "attack" mapping that is often missed by motif- or rule-based systems (Turnbull et al., 12 Mar 2025).

7. Synthesis and Future Directions

Replay (repeat) attacks, whether in cryptographic protocols, networked control systems, ML pipelines, or large-scale physical/social networks, exploit the system’s reliance on time or event uniqueness, undermining integrity or masking adversarial manipulations via repeated, time-shifted, or context-shifted data.

Key technical countermeasures are:

• Layered authentication with non-repeating, session-bound secrets (e.g., multiple passwords in distributed systems).
• Statistical and dynamic residual analysis to identify finite-duration covariance "ripples" unique to replay as opposed to persistent faults.
• Architectural defense at the physical and link layers exploiting amplitude-based decision processes and phase-tracking constraints.
• Feature-engineered and multi-task learning–based discrimination for application-layer pattern recognition.
• Tiled, low-dimension black-box attack strategies minimizing repeated observable artifacts in adversarial ML attacks.

The prevailing research trend favours robust, context-adaptive, and multi-modal detection, as well as architectural changes that increase the observability of replay patterns without imposing unsustainable overhead or degrading legitimate performance. In domains where replay cannot be fully prevented, detection and timely containment, enabled by statistical analysis and hardware/software coordination, remain fundamental.

The technical evolution of replay attack countermeasures is aligned with advances in privacy-preserving protocols, resilient control theory, signal processing, and adversarial machine learning. These areas will continue to dictate the resilience of distributed, safety-critical, and data-driven systems against repeat-based adversarial strategies.