With the improvement of intelligence and interconnection, Internet of Things (IoT) devices tend to become more vulnerable and exposed to many threats. Device identification is the foundation of many cybersecurity operations, such as asset management, vulnerability reaction, and situational awareness, which are important for enhancing the security of IoT devices. The more information sources and the more angles of view we have, the more precise identification results we obtain. This study proposes a novel and alternative method for IoT device identification, which introduces commonly available WebUI login pages with distinctive characteristics specific to vendors as the data source and uses an ensemble learning model based on a combination of Convolutional Neural Networks (CNN) and Deep Neural Networks (DNN) for device vendor identification and develops an Optical Character Recognition (OCR) based method for device type and model identification. The experimental results show that the ensemble learning model can achieve 99.1% accuracy and 99.5% F1-Score in the determination of whether a device is from a vendor that appeared in the training dataset, and if the answer is positive, 98% accuracy and 98.3% F1-Score in identifying which vendor it is from. The OCR-based method can identify fine-grained attributes of the device and achieve an accuracy of 99.46% in device model identification, which is higher than the results of the Shodan cyber search engine by a considerable margin of 11.39%.

Machine learning techniques have been increasingly applied to network intrusion detection. Class imbalance and adversarial attacks are two pivotal challenges that hinder the accuracy and robustness of machine learning-based network traffic detection models. However, current research efforts focus solely on addressing one of these issues, which limits the improvement of detection performance. To jointly tackle these challenges, we propose a dynamic ensemble learning method called ADHS-EL. Specifically, a novel sampling mechanism is integrated into the Boosting ensemble framework, providing a balanced data subset mixed with adversarial samples for each iteration to yield a strong ensemble. This sampling mechanism includes dynamic hybrid sampling for balancing benign and malicious traffic, as well as boundary malicious traffic adversarial augmentation for introducing adversarial samples to the training dataset. A boundary samples importance enhancement strategy is adopted in hybrid sampling and adversarial augmentation, which contributes to improving intrusion detection accuracy and preventing robust overfitting. Experimental evaluations on two public datasets demonstrate that the proposed method exhibits significant advantages in terms of accuracy and robustness. In the non-adversarial test, ADHS-EL obtains the highest F1 score on both datasets. In the adversarial test involving four types of adversarial attacks, ADHS-EL achieves the average detection rates of 74.35% and 80.30% on the two datasets, respectively, marking improvements of 10.63% and 13.26% over the optimal baseline methods.

Camera devices are being deployed everywhere. Cities, enterprises, and more and more smart homes are using camera devices. Fine-grained identification of devices brings an in-depth understanding of the characteristics of these devices. Identifying the device type helps secure the device safe. But, existing device identification methods have difficulty in distinguishing fine-grained types of devices. To address this challenge, we propose a fine-grained identification method based on the camera deviceso inherent features. First, feature selection is based on the coverage and differences of the inherent features type. Second, the features are classified according to their representation. A design feature similarity calculation strategy (FSCS) for each type of feature is established. Then the feature weights are determined based on feature entropy. Finally, we present a device similarity model based on the FSCS and feature weights. And we use this model to identify the fine-grained type of a target device. We have evaluated our method on Dahua and Hikvision camera devices. The experimental results show that we can identify the deviceos fine-grained type when some inherent feature values are missing. Even when the inherent feature pmissing rateq is 50%, the average accuracy still exceeds 80%.