In response to the substantial threat that Internet attacks pose to data center network security, researchers have proposed several deep learning-based methods for detecting network intrusions. However, while algorith...
详细信息
ISBN:
(数字)9798350385557
ISBN:
(纸本)9798350385564
In response to the substantial threat that Internet attacks pose to data center network security, researchers have proposed several deep learning-based methods for detecting network intrusions. However, while algorithms are constantly improving in terms of accuracy, their stability in the face of insufficient attack samples is a major obstacle. To solve the issues of insufficient attack samples and low detection accuracy in network intrusion detection, this paper proposes a deep confidence network intrusion detection method G-DBN based on GAN. The model is based on the malicious sample extension of the generative adversarial network, and it can produce adversarial samples using malicious network flows as original samples. Furthermore, this paper uses deep belief network technology to create and assess the efficacy of the G-DBN model in detecting network attacks, comparing it to standard DBN models and other network intrusion detection techniques. Experimental results show that compared to the standard three-layer DBN method, the G-DBN method described in this paper improves the detection accuracy of attack samples by 6.46% and better meets the performance requirements of current practical applications.
The sixth generation(6G)mobile networks will reshape the world by offering instant,efficient,and intelligent hyper-connectivity,as envisioned by the previously proposed Ubiquitous-X 6G *** hyper-massive and global con...
详细信息
The sixth generation(6G)mobile networks will reshape the world by offering instant,efficient,and intelligent hyper-connectivity,as envisioned by the previously proposed Ubiquitous-X 6G *** hyper-massive and global connectivity will introduce tremendous challenges into the operation and management of 6G networks,calling for revolutionary theories and technological *** this end,we propose a new route to boost network capabilities toward a wisdom-evolutionary and primitive-concise network(WePCN)vision for the Ubiquitous-X 6G *** particular,we aim to concretize the evolution path toward the WePCN by first conceiving a new semantic representation framework,namely semantic base,and then establishing an intelligent and efficient semantic communication(IE-SC)network *** the IE-SC architecture,a semantic intelligence plane is employed to interconnect the semantic-empowered physical-bearing layer,network protocol layer,and application-intent layer via semantic information *** proposed architecture integrates artificial intelligence and network technologies to enable intelligent interactions among various communication objects in *** features a lower bandwidth requirement,less redundancy,and more accurate intent *** also present a brief review of recent advances in semantic communications and highlight potential use cases,complemented by a range of open challenges for 6G.
The advent of the Bitcoin system has brought another boom in the Internet era. In a very short time, many Blockchain systems come into being successively, whose decentration, consensus mechanisms, intelligent contract...
详细信息
In recent years, the integration of communication and sensing has become a hot research field, among which human behavior sensing (HBS) based on Channel State Information (CSI) is particularly active. However, current...
详细信息
In recent years, the integration of communication and sensing has become a hot research field, among which human behavior sensing (HBS) based on Channel State Information (CSI) is particularly active. However, current methods often simplify the sensing task as a prediction problem for fixed category labels, that is, establishing a direct mapping relationship between input features and preset labels through training. Although this paradigm performs well in specific single task scenarios, the model’s generality and task generalization ability are limited. Inspired by Contrastive Learning and the multi-modal Contrastive Language-Image Pretraining model (CLIP), this paper proposes a novel intelligent behavior sensing system (IBSS), based on improved Contrastive Language-CSI Learning. IBSS converts behavior labels into supervised text in natural language form, and uses Transformer model to construct text encoder and CSI encoder respectively. Among them, the text encoder extracts task related semantic features from the supervised text generated by discrete labels, while the CSI encoder extracts feature representations related to sensing tasks from CSI data. By contrastive learning and training, the features of the two modalities are mapped into a joint feature space, and human behavior sensing is achieved by capturing the similarity between CSI features and text features. In addition, thanks to the advantages of the Transformer model in capturing global contextual information between tasks, the proposed method is not only suitable for single-task sensing, but can also be extended to multi-task sensing scenarios with correlation. To verify the effectiveness of the proposed method, we conducted experimental evaluations on public dataset and self-built dataset. The experimental results show that under the same training conditions, the average recognition accuracy of our method in gesture sensing and position sensing tasks reaches over 95%. Compared with the existing state-of
The Node Package Manager ( npm) registry contains millions of JavaScript packages widely shared between worldwide developers. However, npm has also been abused by attackers to spread malicious packages, highlighting t...
详细信息
The Node Package Manager ( npm) registry contains millions of JavaScript packages widely shared between worldwide developers. However, npm has also been abused by attackers to spread malicious packages, highlighting the importance of detecting malicious npm packages. Existing malicious npm package detectors suffer from, among other things, high false positives and/or high false negatives. In this paper, we propose a novel Malicious npm Package Detector (MalPacDetector), which leverages Large Language Model (LLM) to automatically and dynamically generate features (rather than asking experts to manually define them). To evaluate the effectiveness of MalPacDetector and existing detectors, we construct a new npm package dataset, which overcomes the weaknesses of existing datasets (e.g., a small number of examples and a high repetition rate of malicious fragments). The experimental results show that MalPacDetector outperforms existing detectors by achieving a false positive rate of 1.3% and a false negative rate of 7.5%. In particular, MalPacDetector detects 39 previously unknown malicious packages, which are confirmed by the npm security team.
Among the plethora of IoT(Internet of Things)applications,the smart home is one of the ***,the rapid development of the smart home has also made smart home systems a target for ***,researchers have made many efforts t...
详细信息
Among the plethora of IoT(Internet of Things)applications,the smart home is one of the ***,the rapid development of the smart home has also made smart home systems a target for ***,researchers have made many efforts to investigate and enhance the security of smart home *** a more secure smart home ecosystem,we present a detailed literature review on the security of smart home ***,we categorize smart home systems’security issues into the platform,device,and communication *** exploring the research and specific issues in each of these security areas,we summarize the root causes of the security flaws in today's smart home systems,which include the heterogeneity of internal components of the systems,vendors'customization,the lack of clear responsibility boundaries and the absence of standard security ***,to better understand the security of smart home systems and potentially provide better protection for smart home systems,we propose research directions,including automated vulnerability mining,vigorous security checking,and data-driven security analysis.
With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreove...
With serverless computing offering more efficient and cost-effective application deployment, the diversity of serverless platforms presents challenges to users, including platform lock-in and costly migration. Moreover, due to the black box nature of function computing, traditional performance benchmarking methods are not applicable, necessitating new studies. This article presents a detailed comparison of six major public cloud function computing platforms and introduces a benchmarking framework for function computing performance. This framework aims to help users make comprehensive comparisons and select the most suitable platform for their specific needs.
Many nonlinear differential equations arising from practical problems may permit nontrivial multiple solutions relevant to applications, and these multiple solutions are helpful to deeply understand these practical pr...
详细信息
In the field of named entity recognition of diabetes literature, due to the lack of large-scale and high-quality annotation data sets, the traditional named entity recognition technology has the drawback of insufficie...
详细信息
Time series anomaly detection is instrumental in maintaining system availability in various domains. Current work in this research line mainly focuses on learning data normality deeply and comprehensively by devising ...
详细信息
暂无评论