Detecting phishing webpages is an essential task that protects legitimate websites and their users from various malicious activities. To classify the suspect webpage as phishing or legitimate, robust and effective features used for classification are in demand. However, recent phishing attacks usually make phishing webpages resemble the legitimate webpages in visual and functional aspects. This poses a greater difficulty for feature extraction. We herein propose SPWalk, an unsupervised feature learning algorithm for phishing detection. In SPWalk, similar property nodes refer to a collection of phishing webpages or legitimate webpages. We first construct a weblink network with nodes representing webpages. The edges between nodes represent the reference relationships that connect webpages through hyperlinks or similar textual content. Then, SPWalk applies the network embedding technique to mapping nodes into a low-dimensional vector space. A biased random walk procedure efficiently integrates both structural information between nodes and URL information of each node. The effectiveness and robustness of SPWalk come from three points. (1). Phishing attackers do not have full control over reference relationships. (2). The structural regularities generated by diverse reference relationships can be exploited to discriminate between phishing and legitimate webpages. (3). Node URL information makes the learned node representations more suited for phishing detection. Using node as numeric features, we conduct experiments to classify webpages as legitimate or phishing. We demonstrate the superiority of SPWalk over state-of-the-art techniques on phishing detection, especially in terms of precision (over 95%). Even in the case that phishing webpages are well camouflaged by attackers for evading detection, SPwalk exhibits better classification efficacy consistently.

When it comes to the marriage of federated learning and personalized recommendation services (FedRec), characterizing user-item interaction behaviors is a long-standing and unresolved issue, highlighting the growing data privacy concerns due to the inherent openness of recommender systems. As the new interaction-level membership inference attacks on FedRecs have recently surfaced, quite possibly such adversarial attacks act as the hidden confounders lying behind the interactive recommendation, resulting in the obstruction of the causal effect disentanglement on long-term user satisfaction. As such, tailored to the specifics of private learning, we propose a counterfactual interactive recommendation system that builds a differentially private representation learning based defender (CIRDP) to capture and mitigate the adversarial threats, augmenting causal inference-based interactive recommendation of FedRecs. When characterizing interaction-level membership inference attacks of the hidden eavesdropping adversary as the primary cause of adversarial effect on user satisfaction, CIRDP incorporates causal inference-augmented offline reinforcement learning (offline RL) into FedRecs. CIRDP innovatively provides counterfactual satisfaction by optimizing a sensitivity-guided disentangled representation module with an innovative two-fold mutual information objective. As such, CIRDP introduces a differentially private representation learning based defender, guaranteeing interaction behavior-level differential privacy (DP) with a significant reduction in privacy costs. Extensive comparisons demonstrate CIRDP’s superiority over the state-of-the-art baselines in reducing inference attack threats and improving long-term success in the interactive recommendation.

Traffic anomaly detection in road networks is vital for patrol participants to adaptively optimize patrol routes for hybrid task scheduling. In most cases, the routine patrol routing problem comes down to offline task scheduling with a maximal coverage requirement of traffic violation hotspots. Meanwhile, emergent anomalies (e.g., accidents, crowded events) can happen with the unpredictable duration times and locations, leading to real-time requirements of online task scheduling. In practice, trading off coverage and emergency is usually inapplicable in hybrid scenarios, especially with decentralized routing decision makers in highly stochastic traffic environments. In this paper, we propose a Crowd-enhanced hybrid task scheduling framework with the balance of Coverage and Emergency, called CCE-patrol . CCE-patrol consists of offline violation hotspot patrolling, online emergency monitoring. For the offline subproblem, a contact-based integer linear problem modeling is proposed to adaptively find optimal patrol routes. Guided by offline routing, we identify and incorporate online emergent anomalies into traffic data completion for urban crowdsensing, with up-to-date importance estimation by multi-agent cooperative reinforcement learning. The secretary problem can be utilized to select important monitoring tasks in an online manner. Extensive experiments show the superiority of our proposals in comparison with baseline algorithms (i.e., HASH (Sun et al. 2022)) by 21.08 $\%$ improvement.

As a promising paradigm, mobile crowdsensing (MCS) takes advantage of sensing abilities and cooperates with multi-agent reinforcement learning technologies to provide services for users in large sensing areas, such as smart transportation, environment monitoring, etc. In most cases, strategy training for multi-agent reinforcement learning requires substantial interaction with the sensing environment, which results in unaffordable costs. Thus, environment reconstruction via extraction of the causal effect model from past data is an effective way to smoothly accomplish environment monitoring. However, the sensing environment is often so complex that the observable and unobservable data collected are sparse and heterogeneous, affecting the accuracy of the reconstruction. In this paper, we focus on developing a robust multi-agent environment monitoring framework, called self-interested coalitional crowdsensing for multi-agent interactive environment monitoring (SCC-MIE), including environment reconstruction and worker selection. In SCC-MIE, we start from a multi-agent generative adversarial imitation learning framework to introduce a new self-interested coalitional learning strategy, which forges cooperation between a reconstructor and a discriminator to learn the sensing environment together with the hidden confounder while providing interpretability on the results of environment monitoring. Based on this, we utilize the secretary problem to select suitable workers to collect data for accurate environment monitoring in a real-time manner. It is shown that SCC-MIE realizes a significant performance improvement in environment monitoring compared to the existing models.