As smart homes become increasingly prevalent, intelligent models are widely used for tasks such as anomaly detection and behavior prediction. These models are typically trained on static datasets, making them brittle to behavioral drift caused by seasonal changes, lifestyle shifts, or evolving routines. However, collecting new behavior data for retraining is often impractical due to its slow pace, high cost, and privacy concerns. In this paper, we propose SmartGen, an LLM-based framework that synthesizes context-aware user behavior data to support continual adaptation of downstream smart home models. SmartGen consists of four key components. First, we design a Time and Semantic-aware Split module to divide long behavior sequences into manageable, semantically coherent subsequences under dual time-span constraints. Second, we propose Semantic-aware Sequence Compression to reduce input length while preserving representative semantics by clustering behavior mapping in latent space. Third, we introduce Graph-guided Sequence Synthesis, which constructs a behavior relationship graph and encodes frequent transitions into prompts, guiding the LLM to generate data aligned with contextual changes while retaining core behavior patterns. Finally, we design a Two-stage Outlier Filter to identify and remove implausible or semantically inconsistent outputs, aiming to improve the factual coherence and behavioral validity of the generated sequences. Experiments on three real-world datasets demonstrate that SmartGen significantly enhances model performance on anomaly detection and behavior prediction tasks under behavioral drift, with anomaly detection improving by 85.43% and behavior prediction by 70.51% on average. The code is available at https://github.com/xzyvoid/SmartGen.

Network Intrusion Detection Systems (NIDS) are critical for web security by identifying and blocking malicious traffic. In-network NIDS leverage programmable switches for high-speed traffic processing. However, they are unable to reconcile the fine-grained classification of known classes and the identification of unseen attacks. Moreover, they lack support for incremental updates. In this paper, we propose Helios, an in-network malicious traffic detection system, for continual adaptation in attack-incremental scenarios. First, we design a novel Supervised Mixture Prototypical Learning (SMPL) method combined with clustering initialization to learn prototypes that encapsulate the knowledge, based on the weighted infinity norm distance. SMPL enables known class classification and unseen attack identification through similarity comparison between prototypes and samples. Then, we design boundary calibration and overlap refinement to transform learned prototypes into priority-guided matching rules, ensuring precise and efficient in-network deployment. Additionally, Helios supports incremental prototype learning and rule updates, achieving low-cost hardware reconfiguration. We implement Helios on a Tofino switch and evaluation on three datasets shows that Helios achieves superior performance in classifying known classes (92%+ in ACC and F1) as well as identifying unseen attacks (62% - 98% in TPR). Helios has also reduced resource consumption and reconfiguration time, demonstrating its scalability and efficiency for real-world deployment.

Smart homes, powered by the Internet of Things, offer great convenience but also pose security concerns due to abnormal behaviors, such as improper operations of users and potential attacks from malicious attackers. Several behavior modeling methods have been proposed to identify abnormal behaviors and mitigate potential risks. However, their performance often falls short because they do not effectively learn less frequent behaviors, consider temporal context, or account for the impact of noise in human behaviors. In this paper, we propose SmartGuard, an autoencoder-based unsupervised user behavior anomaly detection framework. First, we design a Loss-guided Dynamic Mask Strategy (LDMS) to encourage the model to learn less frequent behaviors, which are often overlooked during learning. Second, we propose a Three-level Time-aware Position Embedding (TTPE) to incorporate temporal information into positional embedding to detect temporal context anomaly. Third, we propose a Noise-aware Weighted Reconstruction Loss (NWRL) that assigns different weights for routine behaviors and noise behaviors to mitigate the interference of noise behaviors during inference. Comprehensive experiments on three datasets with ten types of anomaly behaviors demonstrates that SmartGuard consistently outperforms state-of-the-art baselines and also offers highly interpretable results.

Duration bias widely exists in video recommendations, where models tend to recommend short videos for the higher ratio of finish playing and thus possibly fail to capture users' true interests. In this paper, we eliminate the duration bias from both data and model. First, based on the extensive data analysis, we observe that play completion rate of videos with the same duration presents a bimodal distribution. Hence, we propose to perform threshold division to construct binary labels as training labels for alleviating the drawback of finish playing labels overly biased towards short videos. Algorithmically, we resort to causal inference, which enables us to inspect causal relationships of video recommendations with a causal graph. We identify that duration has two kinds of effect on prediction: direct and indirect. Duration bias lies in the direct effect, while the indirect effect benefits prediction. To this end, we design a model-agnostic Counterfactual Video Recommendation for Duration Debiasing (CVRDD) framework, which incorporates multi-task learning to estimate different causal effect during training. In the inference phase, we perform counterfactual inference to remove the direct effect of duration for unbiased prediction. We conduct experiments on two industrial datasets, and in addition to achieving highly promising results on traditional top-k recommendation metrics, CVRDD also improves the user watch time.

The bumps and dips in data streams are valuable patterns for data mining and networking scenarios such as online advertising and botnet detection. In this paper, we define the wave, a data stream pattern with a serious deviation from the stable arrival rate for a period of time. We then propose Pontus, an efficient framework for wave detection and estimation. In Pontus, a lightweight data structure is utilized for the preliminary processing of incoming packets in the data plane to take advantage of its high processing speed; then, the powerful control plane carries out computationally intensive wave detection and estimation. In particular, we propose the Multi-Stage Progressive Tracking strategy which detects waves in stages and removes any disqualified items promptly to save memory. Hash collisions are addressed by a Stage Variance Maximization technique to reduce estimation error. Moreover, we prove the theoretical error bound and establish upper bounds of false positive and false negative. Experiment results show that the software version of Pontus can achieve around 97% F1-Score even under scarce memory when baselines fail. Furthermore, the implemented prototype of Pontus based on P4 achieves 842x higher throughput than the baseline strawman solution.