With the development of Internet of Things (IoT) technology, various types of information, such as social resources and physical resources, are deeply integrated for different comprehensive applications. Social networking, car networking, medical services, video surveillance and other forms of the IoT information service model gradually change people’s daily lives. Facing the vast amounts of IoT information data, IoT search technology is used to quickly find accurate information to meet the real-time search needs of users. However, IoT search requires using a large amount of user private information, such as personal health information, location information and social relations information, to provide personalized services. Employing private information from users will encounter security problems if an effective access control mechanism is missing during the IoT search process. An access control mechanism can effectively monitor the access activities of resources and ensure that authorized users access information resources under legitimate conditions. This survey examines the growing literature on access control for an IoT search. Problems and challenges of access control mechanisms are analyzed to facilitate the adoption of access control solutions in real-life settings. This paper aims to provide theoretical, methodological and technical guidance for IoT search access control mechanisms in large-scale dynamic heterogeneous environments. Based on a literature review, we also analyzed the future development direction of access control in the age of IoT.

With the development of Internet of Things (IoT) and cloud technologies, numerous IoT devices and sensors transmit huge amounts of data to cloud data centers for further processing. While providing us considerable convenience, cloud-based computing and storage also bring us many security problems, such as the abuse of information collection and concentrated web servers in the cloud. Traditional intrusion detection systems (IDS) and web application firewalls (WAF) are becoming incompatible with the new network environment, and related systems with machine learning or deep learning are emerging. However, cloud-IoT systems increase attacks against web servers, since data centralization carries a more attractive reward. In this paper, based on distributed deep learning, we propose a web attack detection system that takes advantage of analyzing URLs. The system is designed to detect web attacks and is deployed on edge devices. The cloud handles the above challenges in the paradigm of the Edge of Things (EoT). Multiple concurrent deep models are used to enhance the stability of the system and the convenience in updating. We implemented experiments on the system with two concurrent deep models and compared the system with existing systems by using several datasets. The experimental results with 99.410% in accuracy, 98.91% in TPR and 99.55% in DRN demonstrate the system is competitive in detecting web attacks.

With the development of the Internet of Things (IoT) and big data technology, the intelligent transportation system is becoming the main development direction of future transportation systems. The time required for a given path in a transportation system can be accurately estimated using the trajectory data of the taxis in a city. This is a very challenging task. Although historical data has been used in existing research, excessive use of trace information in historical data or inaccurate neighbor trajectory information does not allow for better prediction accuracy of the query path. We propose a deep learning method based on neighbors for travel time estimation (TTE), called the Nei-TTE method. We divide the entire path into multiple disjoint segments and use the historical trajectory data approximated at the time level. Our model captures the characteristics of each segment and utilizes the trajectory characteristics of adjacent segments as the road network topology and speed interact. We use velocity features to effectively represent adjacent segment structures. The experiments on the Porto dataset show that the experimental results of our model are significantly better than those of the existing models.

The continuous increase and spread of malware have caused immeasurable losses to social enterprises and even the country, especially unknown malware. Most existing methods use predefined class samples to train models, which cannot handle unknown malware detection. In this paper, we formalize unknown malware detection as a Few-Shot Learning problem. However, the existing model cannot dynamically adjust the model parameters according to the samples and does not deeply consider the influence of the correlation between samples, so it achieves sub-optimal performance. We propose a Dynamic Prototype Network based on Sample Adaptation for few-shot malware detection (DPNSA). Specifically, we use dynamic convolution to realize dynamic feature extraction based on sample adaptation. Secondly, we define the class feature (prototype) as the mean of the dynamic embedding of all malware samples of each class in the support set. Then, a dual-sample dynamic activation function is proposed, which uses the correlation of the dual-sample to reduce the impact of unrelated features between samples on the metric. Finally, we use the metric-based method to calculate the distance between the query sample and the prototype to realize malware detection. Experiments show that our method outperforms the existing few-shot malware detection models and achieves significant improvement.

The complexity and ongoing evolution of Advanced Persistent Threats (APTs) compromise the efficacy of conventional cybersecurity measures. Firewalls, intrusion detection systems, and antivirus software, which are dependent on static rules and predefined signatures, are increasingly ineffective against these sophisticated threats. Moreover, the use of system audit logs for threat hunting involves a retrospective review of cybersecurity incidents to reconstruct attack paths for attribution, which affects the timeliness and effectiveness of threat detection and response. Even when the attacker is identified, this method does not prevent cyber attacks. To address these challenges, we introduce ThreatInsight, a novel early-stage threat detection solution that minimizes reliance on system audit logs. ThreatInsight detects potential threats by analyzing IPs captured from HoneyPoints. These IPs are processed through threat data mining and threat feature modeling. By employing fact-based and semantic reasoning techniques based on the APT Threat Intelligence Knowledge Graph (APT-TI-KG), ThreatInsight identifies and attributes attackers. The system generates analysis reports detailing the threat knowledge concerning IPs and attributed attackers, equipping analysts with actionable insights and defense strategies. The system architecture includes modules for HoneyPoint IP extraction, Threat Intelligence (TI) data analysis, attacker attribution, and analysis report generation. ThreatInsight facilitates real-time analysis and the identification of potential threats at early stages, thereby enhancing the early detection capabilities of cybersecurity defense systems and improving overall threat detection and proactive defense effectiveness.

With the rapid development of smart cities, various types of sensors can rapidly collect a large amount of data, and it becomes increasingly important to discover effective knowledge and process information from massive amounts of data. Currently, in the field of knowledge engineering, knowledge graphs, especially domain knowledge graphs, play important roles and become the infrastructure of Internet knowledge-driven intelligent applications. Domain concept extraction is critical to the construction of domain knowledge graphs. Although there have been some works that have extracted concepts, semantic information has not been fully used. However, the excellent concept extraction results can be obtained by making full use of semantic information. In this article, a novel concept extraction method, Semantic Graph-Based Concept Extraction (SGCCE), is proposed. First, the similarities between terms are calculated using the word co-occurrence, the LDA topic model and Word2Vec. Then, a semantic graph of terms is constructed based on the similarities between the terms. Finally, according to the semantic graph of the terms, community detection algorithms are used to divide the terms into different communities where each community acts as a concept. In the experiments, we compare the concept extraction results that are obtained by different community detection algorithms to analyze the different semantic graphs. The experimental results show the effectiveness of our proposed method. This method can effectively use semantic information, and the results of the concept extraction are better from domain big data in smart cities.