Given the massive difficulty in emulating IoT firmware, blackbox fuzzing of IoT devices for vulnerability discovery has become an attractive option. However, existing blackbox IoT fuzzers need much time and tedious effort to reverse engineer the IoT companion app (or manually collect test scripts) of each IoT device, which is unscalable when analyzing many devices. Moreover, fuzzing through a companion app is impeded by the input sanitization inside the app and limited to the manually revealed functions. We notice that IoT devices are typically able to connect a hub using standard wireless protocols (such as ZigBee, Z-Wave, and WiFi). We thus propose a uniform hub-based architecture for fuzzing various IoT devices, without reverse engineering any companion apps. It exploits the messages exchanged between a hub and an IoT device to automatically discover all the functions, and then launches systematic function-oriented message-semantics-guided fuzzing. It avoids sanitization imposed by a companion app. In addition, it conducts device state-sensitive fuzzing, which we find very effective in finding IoT bugs. We implement the system named HubFuzzer. The evaluation shows that HubFuzzer leads to much higher coverage than prior state of the art. We test 21 IoT devices and find 23 zero-day vulnerabilities. Four CVEs have been assigned.

Machine learning has the potential to revolutionize medical image classification. However, machine learning requires large medical datasets to improve accuracy, which will compromise patient privacy. Federated learning is a promising technique that protects patient privacy and improves the accuracy of medical image classification. Unfortunately, current research shows that federated learning faces the risk of privacy leakage. In this paper, we propose a privacy-preserving federal learning scheme via hybrid differential privacy for medical image classification (FHDM). Specifically, we construct a local hybrid differential privacy algorithm (LHDP) against patients' privacy leakage. This hybrid algorithm combines Gaussian and Laplace differential privacy without enlarging the privacy budget. We prove that the algorithm applies to the model parameter. Moreover, we design loss optimization and global optimization strategies on the algorithm to achieve higher accuracy in medical image classification. Finally, we validate FHDM in terms of privacy-preserving and model accuracy on real datasets. Experiments show that FHDM effectively protects privacy and improves the average accuracy by 9.60% compared to previous differential privacy schemes with the same medical image dataset and privacy budget.

In the field of network traffic anomaly detection, unsupervised learning plays a critical role yet encounters significant challenges, including accurately determining anomaly thresholds and modeling the intricate temporal dynamics of network traffic. To address these challenges, we present a novel approach, termed Convolutional Autoencoder-Isolation Forest (CAE-IF). By leveraging packet-level reconstruction errors with contextual information, our approach obviates the need for manual threshold setting and effectively captures temporal dynamics. The process commences with the application of the damped incremental statistics algorithm to extract statistical features from network traffic with temporal information. Subsequently, the Convolutional Autoencoder (CAE) is employed to compute the Root Mean Square Error (RMSE), offering detailed insights into the temporal correlations in network traffic. This RMSE is then refined through an aggregation mechanism based on source IP addresses, yielding a fine-grained temporal representation. Finally, the Isolation Forest (IF) algorithm is applied to establish an anomaly detection framework. Our comprehensive experimental evaluation, using three datasets: Mirai, Operating System Scan (OS Scan), and Simple Service Discovery Protocol (SSDP) Flood, demonstrates the superior efficacy of the CAE-IF method. It achieves remarkable F1 scores of 96.14%, 99.81%, and 99.98% on these datasets, respectively. These results not only signify substantial improvements over existing methods for the Mirai and OS Scan datasets but also match the highest F1 score obtained on the SSDP Flood dataset.

The increasing prevalence of IoT technology in smart homes has significantly enhanced convenience but also introduced new security and safety challenges. Traditional security solutions, reliant on sequences of IoT-generated event data (e.g., notifications of device status changes and sensor readings), are vulnerable to cyberattacks, such as message forgery and interception and delaying attacks, and fail to monitor non-smart devices. Moreover, fragmented smart home ecosystems require vendor cooperation or system modifications for comprehensive monitoring, limiting the practicality of the existing approaches. To address these issues, we propose IoTBystander, a non-intrusive dual-channel smart home security monitoring framework that utilizes two ubiquitous platform-agnostic signals, i.e., audio and network, to monitor user and device activities. We introduce a novel dual-channel aggregation mechanism that integrates insights from both channels and cross-verifies the integrity of monitoring results. This approach expands the monitoring scope to include non-smart devices and provides richer context for anomaly detection, failure diagnosis, and configuration debugging. Empirical evaluations on a real-world testbed with nine smart and eleven non-smart devices demonstrate the high accuracy of IoTBystander in event recognition: 92.86% for recognizing events of smart devices, 95.09% for non-smart devices, and 94.27% for all devices. A case study on five anomaly scenarios further shows significant improvements in anomaly detection performance by combining the strengths of both channels.

Conditional anonymous authentication can provide anonymity and traceability to Vehicular Ad-Hoc Networks (VANETs), which protects users’ privacy while resisting malicious users and false messages. However, existing schemes suffer from various disadvantages, such as unavailable batch verification, unrenewable user public keys/certificates, and untimely revocation. In this paper, we propose an efficient conditional anonymous authentication scheme with on-chain key management (ECAKM) in VANETs. To achieve lightweight authentication, we design an efficient Signature of Knowledge (SoK) and a batch verification algorithm. We also employ a Bloom filter on the chain to manage the information about revoked anonymous public keys to further improve the efficiency of our scheme. Moreover, we adopt hash chain technology to update users’ anonymous public keys and protect vehicles against linkage attacks. In addition, based on the blockchain and smart contract (SC), we can manage anonymous public keys of users efficiently and transparently. Security analysis and experimental results demonstrate that our scheme ensures conditional privacy with a reduced authentication overhead.