The rapid advancement and wide application of the Internet of Things technology (IoT) have brought unprecedented convenience to people’s production and life. A great number of devices are connected to the IoT network to provide various services for people, which also makes the IoT more vulnerable to various cyber-attacks. This paper designs a novel IoT web attack detection architecture, which combines the powerful knowledge expression ability and high interpretability of symbolic artificial intelligence (AI) with the adaptive learning ability of connectionist AI to form a closed loop of knowledge embedding and extraction, effectively improve the detection ability of web attacks. The architecture solves the “black box” feature of deep learning models and can obtain knowledge from the trained detection model and add it to the training process of the new model to improve detection capabilities. It also uses the advantages of blockchain technology to realize intelligent sharing between different detection systems, solve the problem of difficult detection model updates and training data acquisition “bottlenecks”. To better detect web attacks, we propose a semi-supervised learning method based on an interpretable convolutional neural network (CNN) to reduce misjudgments during self-training and improve detection accuracy. Additionally, we propose a new feature method to extract the features of web logs in IoT devices, which can help the system to detect web attacks in IoT more quickly and accurately. Simulation results on two different datasets show that the proposed architecture and method can effectively detect web attacks in IoT and reduce the false positive rate.

The advancement of Internet of Things (IoT) technology has significantly transformed the dynamic between humans and devices, as well as device-to-device interactions. This paradigm shift has led to profound changes in human lifestyles and production processes. Through the interconnectedness of numerous sensors and controllers via networks, the IoT facilitates the seamless integration of humans with diverse devices, leading to substantial economic advantages. Nevertheless, the burgeoning IoT industry and the rapid proliferation of various IoT devices have also introduced a multitude of security vulnerabilities. Cyber attackers frequently exploit cyber attacks to compromise IoT devices, jeopardizing user privacy and property security, thereby posing a grave menace to the overall security of the IoT ecosystem. In this paper, we propose a novel IoT Web attack detection system based on a joint embedded prediction architecture (JEPA), which effectively alleviates the security issues faced by IoT. It can obtain high-level semantic features in IoT traffic data through non-generative self-supervised learning. These features can more effectively distinguish normal data from attack data and help improve the overall detection performance of the system. Moreover, we propose a feature interaction module based on a dual-branch network, which effectively fuses low-level features and high-level features, and comprehensively aggregates global features and local features. Simulation results on multiple datasets show that our proposed system has better detection performance and robustness.

The super data transmission capability and connectivity of wireless technologies have promoted the arrival of the Internet of Things (IoT) era. However, the distinct characteristics of IoT devices make them vulnerable to malicious attacks such as hackers and viruses. This paper designs a novel IoT intrusion detection architecture that combines knowledge extraction and sharing, which can extract human understandable knowledge from the trained deep learning model and apply it to the training process of the detection model. The obtained knowledge can also be shared with other detection systems based on the blockchain, which will effectively improve the intrusion detection capabilities of the IoT and realize collective learning. In addition, we propose a CNN-based semi-supervised learning method under the constraints of rules, which can effectively alleviate the catas-trophic interference generated during the self-training process and improve detection accuracy. Simulation results confirm the effectiveness of the proposed architecture and method.

In recent years, with the rapid development of the Internet of Things (IoT), various applications based on IoT have become more and more popular in industrial and living sectors. However, the hypertext transfer protocol (HTTP) as a popular application protocol used in various IoT applications faces a variety of security vulnerabilities. This article proposes a novel HTTP anomaly detection framework based on edge intelligence (EI) for IoT. In this framework, both clustering and classification methods are used to quickly and accurately detect anomalies in the HTTP traffic for IoT. Unlike the existing works relying on a centralized server to perform anomaly detection, with the recent advances in EI, the proposed framework distributes the entire detection process to different nodes. Moreover, a data processing method is proposed to divide the detection fields of HTTP data, which can eliminate redundant data and extract features from the fields of an HTTP header. Simulation results show that the proposed framework can significantly improve the speed and accuracy of HTTP anomaly detection, especially for unknown anomalies.

Many mobile applications rely on location information gained from location services on mobile devices. However, continuously tracking the device location with high accuracy drains the battery quickly. Furthermore, sensing the same location can be redundant when multiple devices are co-located. In this paper, we develop a crowdsourcing-based location service, E2A2 (energy efficient and accuracy aware), which places colo-cated devices into groups, and uses group location to represent individual device location. The E2A2 location service aims to reduce individual device battery consumption associated with location services while simultaneously maintaining high location accuracy for each device. Our experimental results from a prototype system show the effectiveness of our proposed solution with different mobility patterns. We also present results on the impact of different system parameters and the number of users in a group. Compared to running GPS location services on individual devices separately, our E2A2 service saves on average 33% battery consumption rate when 4 devices are co-located at walking speed and 26% battery consumption rate when 4 devices are colocated on the same bus while meeting the same accuracy requirements.