Indoor intrusion detection (IID) is an essential technology to enable various important applications. Recently, an extensive amount of research has been carried out to develop device-free intrusion detection systems based on the WiFi signal due to its ubiquitous existence and minimum privacy disclosure. However, existing WiFi-based intrusion detection systems typically suffer from false alarms caused by pets, limiting their usage in practice. In this article, we propose PetFree to revisit the IID problem with careful consideration of pet interference. PetFree uses fine-grained channel state information (CSI) of WiFi signals to detect whether there is a human or a pet in the monitoring area. The basic idea of PetFree is to use the effective interference height (EIH) differences between humans and pets. We propose a novel CSI-EIH model to characterize the relationship between CSI measurements and the EIH of the target. Based on this model, PetFree manages to achieve accurate pet identification using only a single WiFi link. We implement and evaluate PetFree extensively with commodity WiFi devices in three different indoor scenarios. Results show that PetFree achieves an overall 93.7% intrusion detection rate and decreases the false alarm rate caused by home pets to 6.5%, significantly outperforming the state-of-the-art approach.

Trigger-Action Program (TAP) is a simple but powerful format to realize intelligent IoT applications, especially in home automation scenarios. Existing trace-driven approaches and in-situ programming approaches depend on either customized interaction commands or well-labeled datasets, resulting in limited applicable scenarios. In this paper, we propose ChatIoT, a zero-code TAP generation system based on large language models (LLMs). With a novel context-aware compressive prompting scheme, ChatIoT is able to automatically generate TAPs from user requests in a token-efficient manner and deploy them to the TAP runtime. Further, for those TAP requests including unknown sensing abilities, ChatIoT can also generate new AI models with knowledge distillation by multimodal LLMs, with a novel model customization method based on deep reinforcement learning. We implemented ChatIoT and evaluated its performance extensively. Results show that ChatIoT can reduce token consumption by 26.1-84.9% and improve TAP generation accuracy by 4.2-65.5% compared to state-of-the-art approaches in multiple settings. We also conducted a real user study, and ChatIoT can achieve 91.57% TAP generation accuracy.

Inferring fine-grained link metrics by using aggregated path measurements, known as network tomography, is an effective and efficient way to facilitate various network operations, such as network monitoring, load balancing, and failure diagnosis. Given the network topology and a set of interesting links, we study the problem of calculating the link metrics of these links by end-to-end cycle-free path measurements among selected monitors, i.e., preferential link tomography. Since assigning nodes as monitors usually requires non-negligible operational cost, we focus on assigning a minimum number of monitors to identify these interesting links. We propose an optimal monitor assignment (OMA) algorithm for preferential link tomography in communication networks. OMA first partitions the graph representing the network topology into multiple graph components. Then, OMA carefully assigns monitors inside each graph component and at the boundaries of multiple graph components. We theoretically prove the optimality of OMA by proving: 1) the monitors assigned by OMA are able to identify all interesting links and 2) the number of monitors assigned by OMA is minimal. We also implement OMA and evaluate it through extensive simulations based on both real topologies and synthetic topologies. Compared with two baseline approaches, OMA reduces the number of monitors assigned significantly in various network settings.

Multihop low-power wireless communication is a key feature of Wireless Sensor Networks (WSNs). Although with a lot of efforts, the performance of multihop WSNs in practical deployment could still be unpredictable and unsatisfactory, such as high packet transmission latency and short network lifetime. Narrowband Internet of Things (NB-IoT) is an emerging cellular technology for providing wide-area coverage. In this paper, we investigate the possibilities of deploying 802.15.4 and NB-IoT equipped sink nodes (i.e., gateways) into existing WSNs to achieve the predictable and improved performance in terms of end-to-end reliability, end-to-end latency, and network lifetime. To calculate these key network-wide metrics, we propose a hybrid analytical model for 802.15.4 and NB-IoT traffic, taking the new features of NB-IoT into account. Based on the established model, we formally consider two types of fundamental problems, i.e., the minimum gateway deployment and the optimal κ-gateway deployment. We implement our framework and extensively evaluate it in realistic as well as synthetic WSNs. Results demonstrate that our approach can provide significant performance gains, e.g., the network lifetime can be extended by 37.4%-80.3% by deploying only one sink node. Compared with a state-of-the-art method, our approach improves the network lifetime by 12.6%-31.8%.

Mobile advertising plays a vital role in the mobile app ecosystem. A major threat to the sustainability of this ecosystem is click fraud , i.e., ad clicks performed by malicious code or automatic bot problems. Existing click fraud detection approaches focus on analyzing the ad requests at the server side. However, such approaches may suffer from high false negatives since the detection can be easily circumvented, e.g., when the clicks are behind proxies or globally distributed. In this paper, we present AdSherlock, an efficient and deployable click fraud detection approach at the client side (inside the application) for mobile apps. AdSherlock splits the computation-intensive operations of click request identification into an offline procedure and an online procedure. In the offline procedure, AdSherlock generates both exact patterns and probabilistic patterns based on URL (Uniform Resource Locator) tokenization. These patterns are used in the online procedure for click request identification and further used for click fraud detection together with an ad request tree model. We implement a prototype of AdSherlock and evaluate its performance using real apps. The online detector is injected into the app executable archive through binary instrumentation. Results show that AdSherlock achieves higher click fraud detection accuracy compared with state of the art, with negligible runtime overhead.

Calculating fine-grained link metrics by using aggregated path measurements, known as network tomography, is an effective and efficient way to facilitate various network operations, such as network monitoring, load balancing, and fault diagnosis. Recently, there is a growing interest in the monitor placement problem that ensures link identifiability in a network with link failures. Unfortunately, existing work either assumes an ideal failure prediction model where all failures can be predicted perfectly or makes pessimistic assumptions that all failures are unpredictable. In this paper, we study the problem of placing a minimum number of monitors to identify additive link metrics [or additive by using the log(·) function, e.g., loss rates] from end-to-end measurements among monitors with considering both predictable and unpredictable link failures. We propose a set of robust monitor placement algorithms with different performance-complexity tradeoffs to solve this tomography problem. In particular, we show that the optimal (i.e., minimum) monitor placement is the solution to a hitting set problem, for which, we provide a polynomial-time algorithm to construct the input. We formally prove that the proposed algorithms can guarantee network identifiability against failures based on the graph theory. Trace-driven evaluation results show the effectiveness and the robustness of our algorithms.