Coverage-guided Fuzz Testing (CGF) techniques have been applied to deep neural network (DNN) testing in recent years, generating a significant number of test samples to uncover inherent defects in DNN models. However,...
详细信息
Many multi-source transfer learning methods use the latent attributes between source domains to mitigate interference. However, these strategies have some shortcomings. Strategies that only use commonality may lose va...
详细信息
Using deep learning to determine whether a source code file contains defects has become an important research topic. In the past, many researchers have tended to convert code into Abstract Syntax Tree and use deep neu...
详细信息
Because of the increasing number of threats in the IoT cloud, an advanced security mechanism is needed to guard data against hacking or attacks. A user authentication mechanism is also required to authenticate the use...
详细信息
Because of the increasing number of threats in the IoT cloud, an advanced security mechanism is needed to guard data against hacking or attacks. A user authentication mechanism is also required to authenticate the user accessing the cloud services. The conventional cryptographic algorithms used to provide security mechanisms in cloud networks are often vulnerable to various cyber-attacks and inefficient against new attacks. Therefore,developing new solutions based on different mechanisms from traditional cryptography methods is required to protect data and users' privacy from attacks. Different from the conventional cryptography method, we suggest a secure mutual authentication protocol based on the visual cryptography technique in this paper. We use visual cryptography to encrypt and decrypt the secret images. The mutual authentication is based on two secret images and *** user requests the ticket from the authentication server(AS) to obtain the permission for accessing the cloud services. Three shared secret keys are used for encrypting and decrypting the authentication process. We analyze the protocol using the Barrows-Abadi-Needham(BAN)-logic method and the results show that the protocol is robust and can protect the user against various attacks. Also, it can provide a secure mutual authentication mechanism.
Natural language processing (NLP) is rapidly developing. A series of Large Language Models (LLMs) have emerged, represented by ChatGPT, which have made significant breakthroughs in natural language understanding and g...
详细信息
We designed a large language model evaluation system based on open-ended questions. The system accomplished multidimensional evaluation of LLMs using open-ended questions, and it presented evaluation results with eval...
详细信息
As an important information system of intelligent and connected vehicles, most In-Vehicle Infotainment systems are set up and manage the vehicles through user interaction. This paper presents a Fuzzing method IVIFUZZE...
详细信息
With the rapid development of software industry, a variety of new technologies are produced. Research and development in cloud computing, big data, artificial intelligence, the Internet of Things, blockchain, and othe...
详细信息
In recent years, microservice architectures have benefited from low coupling and high cohesion characteristics, allowing flexible on-demand deployment of complex applications and simplifying the difficulty of developi...
详细信息
K-nearest neighbor(KNN)is one of the most fundamental methods for unsupervised outlier detection because of its various advantages,e.g.,ease of use and relatively high ***,most data analytic tasks need to deal with hi...
详细信息
K-nearest neighbor(KNN)is one of the most fundamental methods for unsupervised outlier detection because of its various advantages,e.g.,ease of use and relatively high ***,most data analytic tasks need to deal with high-dimensional data,and the KNN-based methods often fail due to“the curse of dimensionality”.AutoEncoder-based methods have recently been introduced to use reconstruction errors for outlier detection on high-dimensional data,but the direct use of AutoEncoder typically does not preserve the data proximity relationships well for outlier *** this study,we propose to combine KNN with AutoEncoder for outlier ***,we propose the Nearest Neighbor AutoEncoder(NNAE)by persevering the original data proximity in a much lower dimension that is more suitable for performing ***,we propose the K-nearest reconstruction neighbors(K NRNs)by incorporating the reconstruction errors of NNAE with the K-distances of KNN to detect ***,we develop a method to automatically choose better parameters for optimizing the structure of ***,using five real-world datasets,we experimentally show that our proposed approach NNAE+K NRN is much better than existing methods,i.e.,KNN,Isolation Forest,a traditional AutoEncoder using reconstruction errors(AutoEncoder-RE),and Robust AutoEncoder.
暂无评论