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An efficient intrusion detection model based on deepFM

An efficient intrusion detection model based on deepFM

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IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Yuchen Ji Xiaoyong Li Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Beijing University of Posts and Telecommunication Beijing China

ISBN: (数字)9781728143903

ISBN: (纸本)9781728143910

With the development of the Internet, more and more network attacks are also emerging. The traditional intrusion detection system has been challenged tremendously, and it is unable to detect the new types of attacks. This paper proposes an intrusion detection model based on deepFM, which combines the advantages of FM algorithm for processing shallow features and the advantages of deep neural network for processing deep features, which can effectively extract features and classify them. We then performed experiments using the KDD CUP 99 dataset. First, the training set is deduplicated and oversampled to balance various types of samples, and then the processed data is used for training. Then optimize tuning parameters and find an optimal model. Finally, use the best model to make predictions on the test set. On the test set, we got a correct rate of 0.934 and a COST value of 0.191. By analyzing the results and comparing existing studies, we can conclude that the model proposed in this paper has a good effect on KDD 99 CUP and has certain practical value.

关键词： Feature extraction Intrusion detection Data models Encoding Frequency modulation Neural networks Training

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A High Accuracy DNS Tunnel Detection Method Without Feature Engineering

A High Accuracy DNS Tunnel Detection Method Without Feature ...

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International Conference on Computational Intelligence and Security

作者： Yang Chen XiaoYong Li Beijing Key Laboratory of Trustworthy Distributed Computing and Service (Beijing University of Posts and Telecommunications) Ministry of Education Beijing China

ISBN: (纸本)9781665404457;9781665447119

Domain Name System (DNS) is a key protocol and service used on the Internet. It is responsible for converting domain names into IP addresses. DNS tunnel is a method of encoding data of other programs or protocols in DNS query and response. Previous studies usually need to extract a large number of features manually and train the classifier of DNS tunnel detection by feature engineering. In this paper, a new framework for DNS tunnel detection is proposed, which can automatically extract features, including long short-term memory (LSTM) language model with attention mechanism and gated recurrent unit (GRU) language model with attention mechanism. Finally, a single-level classifier based on a character-level convolutional neural network (Char-CNN) is proposed. The results show that the LSTM and GRU language models based on attention mechanism and the algorithm of character-level convolution neural network achieve high accuracy and near-zero false positives.

关键词： Protocols Neural networks Training data Detectors Logic gates Feature extraction Classification algorithms

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Multi-level Directed Fuzzing for Detecting Use-after-Free Vulnerabilities

Multi-level Directed Fuzzing for Detecting Use-after-Free Vu...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Yuntao Zhang Zhongru Wang Weiqiang Yu Binxing Fang Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China Chinese Academy of Cyberspace Studies Beijing China Beijing DigApis Technology Co. Ltd Beijing China

ISBN: (纸本)9781665416597

Greybox fuzzing has been widely used in vulnerabilities detection. Most greybox fuzzing tools are coverage-based, which usually use basic block transition to gain code coverage and focus on improving it to trigger more bugs. However, only increasing code coverage is insufficient to find some heap-based vulnerabilities such as use-after-free (UAF) and double-free (DF). This is because, to trigger these vulnerabilities, one needs not only to cover more code, but also to execute special heap operations to satisfy a particular temporal constraint (i.e., allocating heap memory, free memory, and accessing the heap memory). In this paper, we propose an approach, namely MDFuzz, to detect heap-based vulnerabilities adopting multi-level directed greybox fuzzing. The key idea is identifying different targets to guide the fuzzing process to cover specific heap operations without wasting resources exploring unrelated program components. We first perform a static analysis to automatically recognize three critical targets related to heap operations and then calculate each basic block's distance to the targets. Moreover, we propose a probability-based multi-level seed queue and a novel seed selection strategy to augment the guidance of directed fuzzing. To evaluate MDFuzz, we have performed an evaluation on 7 real-world applications. The experimental results demonstrate that MDFuzz significantly outperforms the state-of-the-art fuzzers, including AFL, AFLFast and VUzzer, in terms of the time consumed to discover heap-based vulnerabilities. Moreover, MD-Fuzz found 4 previously unknown vulnerabilities in real-world programs.

关键词： Privacy Target recognition Conferences Computer bugs Static analysis Binary codes Fuzzing

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Scalable Blind Matching: An Efficient Ciphertext Matching Scheme for Content-Based Pub/Sub Cloud services

Scalable Blind Matching: An Efficient Ciphertext Matching Sc...

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IEEE International Conference on Big Data

作者： Qingling Feng Ruisheng Shi Qifeng Luo Lina Lan Jinqiao Shi Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Content-based publish/subscribe cloud services are prevailing recently. Confidentiality in publish/subscribe cloud services has become a major concern, especially for applications with sensitive data. Many methods have been proposed to achieve the confidentiality of events and subscriptions. Unfortunately, all these approaches suffer significant performance deterioration while matching on large-scale subscription set. In this paper, we propose an efficient ciphertext matching scheme called SBM. To the best of our knowledge, SBM is the first approach which can preserve confidentiality while at the same time provide scalable event matching. Furthermore, we have integrated SBM with an open source publish/subscribe middleware, PADRES and conducted extensive experiments to evaluate our scheme. The experimental results demonstrate that the matching speed of our solution is faster by two orders of magnitude than its counterparts.

关键词： Degradation Protocols Scalability Big Data Security Indexes Middleware

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EML-Based IM/DD Transmission over Dispersion-Uncompensated Links Using Low-Complexity Optimized Detection

EML-Based IM/DD Transmission over Dispersion-Uncompensated L...

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Asia Communications and Photonics Conference and Exhibition (ACP)

作者： Shuangyue Liu Han Cui Xizi Tang Yueming Lu Yaojun Qiao State Key Laboratory of Information Photonics and Optical Communications School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

We experimentally demonstrate an EML-based 40-Gbit/s OOK IM/DD trans-mission over 120-km SSMF using a low-complexity optimized detection algorithm, which requires no multiplication during the update of survival path, ... 详细信息

ISBN: (纸本)9781957171005

关键词： Chromatic dispersion Asia Optical fiber communication Complexity theory Detection algorithms Photonics

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Fast and Efficient Layer-Aware Container Vulnerability Patching in Edge computing 4th

Fast and Efficient Layer-Aware Container Vulnerability Patc...

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4th International Conference on Ubiquitous Security, UbiSec 2024

作者： Xie, Xuan Hu, Yuting Chen, Xinpu Lin, Yumin Tang, Zhiqing Guo, Jianxiong Zhang, Tianle Wang, Tian Faculty of Arts and Sciences Beijing Normal University Zhuhai China Institute of AI and Future Networks Beijing Normal University Zhuhai China BNU-HKBU United International College Zhuhai China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9789819648351

With the rise of Edge computing (EC), more applications are being deployed on edge servers using containers to serve users. These containers rely on image files composed of multiple layers, which often harbor vulnerabilities that can typically be resolved through upgrades. However, due to resource limitations, geographical distribution, and latency sensitivity in EC, traditional cloud upgrade strategies are not directly applicable. Therefore, efficiently updating containers to address vulnerabilities is a pressing challenge. In this paper, we present a joint cluster upgrade and task scheduling algorithm using layer sharing to quickly and efficiently address container vulnerabilities. By modeling the edge cluster upgrade problem, we establish the upgrade sequence based on node load, thereby reducing the impact on ongoing tasks. Our layer-sharing task scheduling mechanism takes into account node resource status, layer distribution, and task characteristics. Based on this, we present the Layer-aware Cluster upgrade and Task scheduling (LCT) algorithm that optimizes layer transmission, download delays, and task computation delays to minimize the disruption caused by patching. Experiments conducted with both simulated and real datasets demonstrate that our algorithm significantly outperforms baseline methods. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Scheduling algorithms

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An iterative sinogram metal artifact reducdion based on UNet

An iterative sinogram metal artifact reducdion based on UNet

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IEEE International Conference on Big Data

作者： Zichong An Xuemei Zhu Xiangling Fu Junqi Ma Chenyi Guo Zheng Zhang School of Computer Science(National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing China The University Hospital of Beijing University of Posts and Telecommunications Beijing China Yofo Medical Technology Co. Ltd Beijing China Department of Electronic Enginerring Tsinghua University Beijing China School of Modern Post(School of Automation) Beijing University of Posts and Telecommunications Beijing China

In the practice of dentistry, oral dental CT images are frequently used to assist doctors in diagnosis. Filtered back projection (FBP) technique is widely employed in practice for the reconstruction of CT images obtained from X-ray calculations. However, when metal objects occur in a patient’s oral cavity, the CT images would show density discontinuities due to the metals’ “X-ray absorption coefficient is much larger than human tissues. When the FBP algorithm is applied to CT images with metals, severe metal artifacts would be obtained, which significantly reconstructed images. Therefore, metal artifact reduction (MAR) work is becoming an important problem in dentistry image processing. In this paper, we propose a novel iterative sinogram metal artifact reduction model (IS-MARM) to solve the problem. Inspired by the Diffusion model, we propose a new method to reduce metal artifacts and interpolate new data in sinogram of dentistry images iteratively. This approach reduces the difficulty of model learning and achieves good results. Secondly, we proposed a new simple method of iterative data generating to simulate real-world metals in CT sinogram images. Finally, we have demonstrated the effectiveness of our method through experiments on dental CT MAR work.

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An Efficient Trust Inference Algorithm with Local Weighted Centrality for Social Recommendation

An Efficient Trust Inference Algorithm with Local Weighted C...

引用

IEEE International Conference on Communications (ICC)

作者： Binsi Cai Xiaoyong Li Yali Gao Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing P.R.China

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

The integration of trust system and recommendation system is a new hot spot in current research. Trust relationship has be exploited in social recommendation, which can effectively solve the problems of low recommendation quality, sparse data and cold start in the traditional recommendation system. Meanwhile, trust inference in social relations is necessary in completing trust information and expanding social recommendation knowledge base. In this work, we propose a new trust inference algorithm LWCTrust to improve the efficiency and accuracy of social recommendation. Firstly, we construct a local weighted centrality (LWC) metric based on the user's degree centrality and trust information, and propose a new adaptive breadth-first search algorithm. Then, based on the property of path decay, we compare two different trust decay strategies. In addition, considering inconsistencies and conflicts in trust opinion, we apply LWC metric to multi-path aggregation step and present a OWA dynamic aggregation strategy. A number of experiments are conducted on the real social network dataset Advogato, and the results validate the great performance of LWCTrust. Our work is the first to construct an efficient LWC metric using social graph trust information, and we explore the effect of attenuation functions on accuracy in path propagation.

关键词： Measurement Inference algorithms Reliability Heuristic algorithms Dictionaries Social network services Mathematical model

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distributed File System Multilevel Fault-Tolerant High Availability Mechanism 2020

Distributed File System Multilevel Fault-Tolerant High Avail...

引用

Proceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies

作者： Yufeng Shi Jinxin Zuo Yihong Guo Yueming Lu Key Laboratory of Trustworthy Distributed Computing and Servi-ce(BUPT) Ministry of Education Beijing University of Posts and Telecommunications Beijing China Nanjing University of Science & Technology Nanjing Jiangsu China

ISBN: (纸本)9781450387828

To solve the single point of failure (SPoF), we designed a distributed file system multilevel fault-tolerant high availability mechanism and implemented the multilevel fault-tolerant file system (MFTFS). At the same time, we proposed a multilevel-raft election algorithm to automatically solve the split-brain and master state switching problems. By introducing a hot-standby master and a cold-standby master cluster, it is possible to quickly recover from failures to achieve high system availability when the primary master fails. The hot-standby master maintains a strict consistency of metadata in memory with the primary master, which facilitates rapid failover and replaces the master node to provide external services. The cold-standby master cluster backs up the metadata in the hard disk of the hot-standby master. At the same time, it acts as a third party to judge the status of the primary/hot-standby master, when the primary/hot-standby master is missing, it elects a new master, to avoid split-brain problems. Experimental results show that our mechanism can maintain the strict consistency of metadata in the primary/hot-standby memory, while making the mean failure recovery time less than 2s, and has an excellent performance in solving single-point failure and split-brain problems.

关键词： data consistency

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MDPPC: Efficient Scalable Multiparty Delegated PSI and PSI Cardinality

MDPPC: Efficient Scalable Multiparty Delegated PSI and PSI C...

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Annual Conference on Privacy, Security and Trust, PST

作者： Yihao Yang Xiaolei Dong Jiachen Shen Zhenfu Cao Yunbo Yang Jun Zhou Liming Fang Zhe Liu Chunpeng Ge Chunhua Su Zongyang Hou Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Research Center for Basic Theories of Intelligent Computing Research Institute of Basic Theories Zhejiang Lab Hangzhou China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) Changsha China Shandong University Jinan China University of Aizu Fukushima Japan Print-Rite Technology Development Co. Ltd. Of Zhuhai China

Private Set Intersection (PSI) is one of the most important functions in secure multiparty computation (MPC). PSI protocols have been a practical cryptographic primitive and there are many privacy-preserving applications based on PSI protocols such as computing conversion of advertising and distributed computation. Private Set Intersection Cardinality (PSI-CA) is a useful variant of PSI protocol. PSI and PSI-CA allow several parties, each holding a private set, to jointly compute the intersection and cardinality, respectively without leaking any additional information. Nowadays, most PSI protocols mainly focus on two-party settings, while in multiparty settings, parties are able to share more valuable information and thus more desirable. On the other hand, with the advent of cloud computing, delegating computation to an untrusted server becomes an interesting problem. However, most existing delegated PSI protocols are unable to efficiently scale to multiple clients. In order to solve these problems, this paper proposes MDPPC, an efficient PSI protocol which supports scalable multiparty delegated PSI and PSI-CA operations. Security analysis shows that MDPPC is secure against semi-honest adversaries and it allows any number of colluding clients. For 15 parties with set size of 2 20 on server side and 2 16 on clients side, MDPPC costs only 81 seconds in PSI and 80 seconds in PSI-CA, respectively. The experimental results show that MDPPC has high scalability.

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