Concept Drift Adaptation Using Self-Supervised and Reinforcement Learning In Android Malware Detection

• URL: http://arxiv.org/abs/2605.24294v1
• Date: Fri, 22 May 2026 23:49:30 GMT
• Title: Concept Drift Adaptation Using Self-Supervised and Reinforcement Learning In Android Malware Detection
• Abstract summary: We propose a chronological adaptive maintenance framework that models deployment-time maintenance as a sequential decision problem.<n>A policy optimization controller selects low-cost maintenance actions based on the detector state.<n>Results show that the RL controller provides a strong cost-aware adaptation strategy.
• Score: 9.493071661387596
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Android malware detectors often degrade after deployment because of concept drift, while full retraining at each maintenance step is costly. We propose a chronological adaptive maintenance framework that models deployment-time maintenance as a sequential decision problem. The framework learns a stable latent representation through self-supervised learning during initialization, freezes the encoder, measures latent drift in the fixed representation space, and performs lightweight downstream adaptation using a trainable adapter and classification head. A proximal policy optimization controller selects low-cost maintenance actions based on the detector state, including current utility, retention on a fixed memory set, latent drift indicators, and update cost. We evaluate the framework under a causal deployment-style protocol on emulator and real Android malware datasets with static and dynamic features. Results show that the RL controller provides a strong cost-aware adaptation strategy, consistently remaining among the top-performing policies while achieving a favorable balance between temporal performance, memory retention, and maintenance cost under non-stationary deployment conditions.

Related papers

• PILOT: Privileged Imitation Learning for End-to-End Motion Planning of Autonomous UAVs under Partial Observability [7.550075542886951]
  This paper presents PILOT, a constraint-aware privileged imitation learning framework for vision-based end-to-end UAV planning under partial observability.
  arXiv  Detail & Related papers  (2026-08-14T08:43:59Z)
• SVR: Self-Verifying Refinement via Joint Verdict-Confidence Reinforcement Learning for Adaptive Test-Time Compute [62.3458279176813]
  Self-Verifying Refinement is an oracle-free multi-turn reinforcement learning framework.<n>It learns to use self-verification as a compute-control policy.<n>It achieves a macro-average accuracy of 0.563 with only 2.99 inference turns on average.
  arXiv  Detail & Related papers  (2026-07-30T16:20:58Z)
• AdaptiveSD A Stability-Aware, Runtime-Adaptive Speculative Decoding Framework with Multi-Policy Orchestration for CPU-Constrained LLM Inference [0.0]
  Fixed-depth speculative decoding has emerged as one promising technique, but in practice, it often leads to performance degradation due to bandwidth saturation, instability, or even catastrophic resource exhaustion resulting in system failure.<n>Our solution consists of four tightly-coupled components working together in a continuous feedback loop: an Adaptive Draft Controller, a Dynamic Policy Engine, a KV Cache Coordination Layer, and a Monitoring Engine.<n>While conventional approaches focus solely on maximizing throughput, we instead assess the effectiveness of our approach based on several key metrics including wasted drafted compute and inter-token latency dispersion alongside standard measures of speculative efficiency.
  arXiv  Detail & Related papers  (2026-07-04T13:39:40Z)
• Decoupled Delay Compensation: Enhancing Pre-trained MARL Policies via Learned Dynamics Filtering [2.0223202343873226]
  Real-world multi-agent reinforcement learning (MARL) systems must often operate under stale observations, communication delays, and intermittent packet loss.<n>We propose a modular execution-stage state-estimation layer that replaces delayed communicated observations with current belief-state estimates.
  arXiv  Detail & Related papers  (2026-05-25T19:19:46Z)
• Multimodal Learning on Low-Quality Data with Conformal Predictive Self-Calibration [72.0672328514289]
  Multimodal learning often grapples with the challenge of low-quality data.<n>We propose a unified framework, termed Conformal Predictive Self-Calibration.<n>Our framework consistently outperforms existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-05-05T14:48:52Z)
• Generative Control as Optimization: Time Unconditional Flow Matching for Adaptive and Robust Robotic Control [33.03635235189535]
  We introduce Generative Control as Optimization (GeCO), a time-unconditional framework that transforms action synthesis into iterative optimization.<n>Test-time inference becomes an adaptive process that allocates computation based on convergence-exiting early for simple states while refining longer for difficult ones.<n>We validate GeCO on standard simulation benchmarks and demonstrate seamless scaling to pi0-series Vision-Language-Action (VLA) models.
  arXiv  Detail & Related papers  (2026-03-18T15:27:17Z)
• Modeling and Controlling Deployment Reliability under Temporal Distribution Shift [1.4323566945483497]
  We propose a deployment-centric framework that treats reliability as a dynamic state composed of discrimination and calibration.<n>We show that selective, drift-triggered interventions can achieve smoother reliability trajectories than continuous rolling retraining.
  arXiv  Detail & Related papers  (2026-03-01T17:18:44Z)
• TeleBoost: A Systematic Alignment Framework for High-Fidelity, Controllable, and Robust Video Generation [45.864084191741135]
  Post-training is the decisive step for converting a pretrained video generator into a production-oriented model.<n>This report presents a systematical post-training framework that organizes supervised policy shaping, reward-driven reinforcement learning, and preference-based refinement.
  arXiv  Detail & Related papers  (2026-02-07T15:49:25Z)
• Not All Preferences Are Created Equal: Stability-Aware and Gradient-Efficient Alignment for Reasoning Models [52.48582333951919]
  We propose a dynamic framework designed to enhance alignment reliability by maximizing the Signal-to-Noise Ratio of policy updates.<n>SAGE (Stability-Aware Gradient Efficiency) integrates a coarse-grained curriculum mechanism that refreshes candidate pools based on model competence.<n> Experiments on multiple mathematical reasoning benchmarks demonstrate that SAGE significantly accelerates convergence and outperforms static baselines.
  arXiv  Detail & Related papers  (2026-02-01T12:56:10Z)
• Centaur: Robust End-to-End Autonomous Driving with Test-Time Training [84.78837437133234]
  We propose Centaur, which updates a planner's behavior via test-time training without relying on hand-engineered rules or cost functions.<n>We develop a novel uncertainty measure, called Cluster Entropy, which is simple, interpretable, and compatible with state-of-the-art planning algorithms.
  arXiv  Detail & Related papers  (2025-03-14T17:59:41Z)
• AdaShadow: Responsive Test-time Model Adaptation in Non-stationary Mobile Environments [24.606016498430407]
  This paper presents AdaShadow, a responsive test-time adaptation framework for non-stationary mobile data distribution and resource dynamics. AdaShadow addresses challenges in estimating layer importance and latency, as well as scheduling the optimal layer update plan. Results show that AdaShadow achieves the best accuracy-latency balance under continual shifts.
  arXiv  Detail & Related papers  (2024-10-10T16:41:39Z)
• Improving the Performance of Robust Control through Event-Triggered Learning [74.57758188038375]
  We propose an event-triggered learning algorithm that decides when to learn in the face of uncertainty in the LQR problem. We demonstrate improved performance over a robust controller baseline in a numerical example.
  arXiv  Detail & Related papers  (2022-07-28T17:36:37Z)
• Non-stationary Online Learning with Memory and Non-stochastic Control [71.14503310914799]
  We study the problem of Online Convex Optimization (OCO) with memory, which allows loss functions to depend on past decisions. In this paper, we introduce dynamic policy regret as the performance measure to design algorithms robust to non-stationary environments. We propose a novel algorithm for OCO with memory that provably enjoys an optimal dynamic policy regret in terms of time horizon, non-stationarity measure, and memory length.
  arXiv  Detail & Related papers  (2021-02-07T09:45:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.