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Under the tendency of interconnection and interoperability in Industrial Internet, anomaly detection, which has been widely recognized, has achieved modest accomplishments in industrial cyber security. However, a significant issue is how to ...

With the rapid development of Android platform, the number of Android malwares is growing rapidly. Due to the limitations of traditional static and runtime Android malware analysis methods, machine learning based approaches are widely adopted ...

Although being isolated from the external network, the private network is still faced with some security threats, such as violations communications, malware attacks, and illegal operations. It is an attractive approach to recognize these security ...

Malware has become a serious threat to network security. Traditional static analysis methods usually cannot effectively detect packers, obfuscations, and variants. Dynamic analysis is not efficient when dealing with large amounts of malware. ...

In recent years, frequent occurrence of network security incidents indicates that host security is more and more fragile. However, current protection tools leads to reduce the efficiency of CPU or GPU. Meanwhile, they give up active defense and ...

In order to improve the real-time efficiency of expressway operation monitoring and management, the anomaly detection in intelligent monitoring network (IMN) of expressway based on edge computing and deep learning is studied. The video data ...

In this paper, a novel two-phase cycle training algorithm based on multi-objective genetic algorithm (MOGA) and modified back propagation neural network (MBPNN), namely TPC-MOGA-MBPNN, is proposed for effective intrusion detection based on ...

Wireless sensor network is regarded as one of the ten emerging technologies that will change the world in the 21st century. The related basic theories, key technologies and application models have been widely studied in the industry and academia. ...

In recent years, the increasing number of malicious software has led to more and more serious security threats to computers or network. The researches have been devoting significant efforts to solve the numerous challenges in malware detection and ...

Machine learning algorithms are widely used in malware detection where successful analysis on static and dynamic features plays a crucial role in process of detecting malicious samples. In this paper, the potential malicious features are ...

To explore the classification of malware variants, a malware variant detection method is proposed based on the code visualization method and ensemble learning model. First, malware binary data was transformed into a gray-scale image and the GIST ...

With the rise of network attack, advanced persistent threats (APT) imposes severe challenges to network security. Since APT attacker can easily hide inevitable C&C traffic in massive Web traffic, HTTP-based C&C communication has become the most ...

Distributed Denial-of-Service (DDoS) attacks disrupts the availability of essential services, which are one of the most harmful threats in today’s Internet. Many DDoS detection algorithms based on machine learning technology have emerged in recent ...

Cerebral microbleeds (CMBs) are important imaging and diagnostic biomarkers for cerebrovascular diseases and cognitive dysfunctions. Reliable detection of the location and amount of CMBs in brain tissue is crucial for the diagnosis, prevention and ...

Accurate segmentation of liver tumors is an important guarantee for the success of liver cancer surgery, where convolutional network has been a type of popular method. However, the performance of the traditional convolutional network is limited by ...

Advancements of Internet of Things (IoT) enhance the application spectrum of smart networking and demand intelligent security measurements against cyber-attacks. Recent integration of Software Defined Networking (SDN) in IoT environments provides ...

Keyword Spotting (KWS) is a significant branch of Automatic Speech Recognition (ASR), which has been widely used in edge computing devices. The goal of KWS is to provide high accuracy at a low false alarm rate (FAR) while reducing the costs of ...

Machine learning (ML) is fast becoming a powerful tool for increasing agricultural production, for instance, ML predicts weather and yield through satellite images. Such approaches, however, most applications are based on expensive cloud servers ...

In this paper, a distributed control scheme of key technologies is designed, which mainly includes QoS (quality of service) identification module and QoS routing optimization module. At the same time, a new routing method is designed to transform ...

Since the findings of the single modality data are unable to provide sufficient sensitivity and specificity for diagnostic measures, multimodality data fusion is adopted in the neuroimaging to detect the important differences between patients and ...

In this paper, based on linear quadratic theory, the optimal control problem for multi-agent systems with external disturbances is studied. Firstly, the optimal distributed controller is designed by the performance index function without the ...

Machine learning models trained on data collected from multiple parties can offer prediction services to clients. However, it raises privacy concerns for both model owners and clients. The models may disclose the details of the training data ...

With the rapid development of neural network theory, the issue of privacy has attracted much attention, especially for the prediction or classification of some sensitive information, a neural network model that can protect privacy is needed. On ...

While we enjoy high-quality social network services, image privacy is also under great privacy threats. With the development of deep learning-driven face recognition technologies, traditional protection methods are facing challenges. How to ...

The powerful storage function and convenient service function of cloud computing attract more and more users to save their digit image on the cloud server. However, the insecure data transmission process of cloud computing may lead to the ...

Machine learning is more closely linked to data privacy and has obtained rapid development in recent years. As for data privacy, searchable encryption (SE) is widely used as a ciphertext search technology, protecting the privacy of users. However, ...

Deep learning models bear the risks of privacy leakage. Attackers can obtain sensitive information contained in training data with some techniques. However, existing differentially private methods such as Differential Privacy-Stochastic Gradient ...

Covert channel is an important way to transmit covert message and implement covert communication through the network. However, the existing research on covert channel cannot meet the security requirements of covert communication in the complex ...