Security and Communication Networks（期刊）

ecurity and Communication Networks is an international journal publishing original research and review papers on all security areas including network security, cryptography, cyber security, etc. The emphasis is on security protocols, approaches and techniques applied to all types of information and communication networks, including wired, wireless and optical transmission platforms.

The journal provides a prestigious forum for the R&D community in academia and industry working at the inter-disciplinary nexus of next generation communications technologies for security implementations in all network layers.

Answering the highly practical and commercial importance of network security R&D, submissions of applications-oriented papers describing case studies and simulations are encouraged as well as research analysis-type papers.

USENIX Security 

The USENIX Association is a 501(c)(3) nonprofit organization, dedicated to supporting the advanced computing systems communities and furthering the reach of innovative research. We are known for organizing conferences and publishing research, but our greatest strength lies in building communities in computing systems. We represent the interests of our communities in a variety of ways, including our professional affiliation with the Computing Research Association.

Since 1975, USENIX has brought together the community of engineers, researchers, and practitioners working on the cutting edge of the computing world. USENIX conferences have become the essential meeting grounds for the presentation and discussion of the most advanced information on the development of all aspects of computing systems.

Since 1980, the IEEE Symposium on Security and Privacy has been the premier forum for presenting developments in computer security and electronic privacy, and for bringing together researchers and practitioners in the field.

ACM CCS

The ACM Conference on Computer and Communications Security (CCS) is the flagship annual conference of the Special Interest Group on Security, Audit and Control (SIGSAC) of the Association for Computing Machinery (ACM). The conference brings together information security researchers, practitioners, developers, and users from all over the world to explore cutting-edge ideas and results. 

NDSS

The Network and Distributed System Security (NDSS) Symposium fosters information exchange among researchers and practitioners of network and distributed system security.

Originally the Workshop on Network and Distributed System Security, the NDSS Symposium was first held in 1993 in San Diego, California. It was organized as a workshop of the Privacy and Security Research Group of the Internet Research Task Force (IRTF) and was hosted by the Internet Society and Lawrence Livermore National Laboratory.

The Internet Society has proudly hosted the NDSS Symposium ever since to foster the next generation of Internet security experts and to support open, accessible, and collaborative security to research to help strengthen the Internet.

The symposium is now a five-day hybrid event including three days of the main NDSS Symposium and two days of co-located workshops and symposia. Each event brings together hundreds of security educators, researchers and practitioners from all over the world. The event is designed to encourage and enable the Internet community to apply, deploy, and advance the state of practical security technologies.

ACM ASIACCS

Building on the success of ACM Conference on Computer and Communications Security (CCS), the ACM Special Interest Group on Security, Audit, and Control (SIGSAC) formally established the annual ACM Asia Conference on Computer and Communications Security (ASIACCS). The inaugural ASIACCS was held in Taipei (2006). Since then ASIACCS has been held in Singapore (2007), Tokyo (2008), Sydney (2009), Beijing (2010), Hong Kong (2011), Seoul (2012), Hangzhou (2013), Kyoto (2014), Singapore (2015), Xi’an (2016), Abu Dhabi (2017), Songdo (2018), Auckland (2019), Taipei (2020, virtual), Hong Kong (2021, virtual) and Nagasaki (2022, hybrid).

年度 IEEE/IFIP 可靠系统和网络国际会议 - (DSN)

The Passive and Active Measurement (PAM) conference brings together researchers and operators to discuss novel and emerging work in the area of network measurement and analysis.

SIGCOMM

ACM SIGCOMM 是 ACM 数据通信特别兴趣小组 (SIGCOMM) 的旗舰年度会议

ACM IMC

The Internet Measurement Conference (IMC) is a three-day event focusing on Internet measurement and analysis

ACSAC

The IEEE/ACM Automated Software Engineering (ASE) Conference series is the premier research forum for automated software engineering. Each year, it brings together researchers and practitioners from academia and industry to discuss foundations, techniques, and tools for automating the analysis, design, implementation, testing, and maintenance of large software systems.

信息工程大学

ICCC

ICCC会议发起于2015年，由四川省电子学会主办，西南交通大学, 电子科技大学，四川大学，成都理工大学，成都信息工程大学等协办。

Applied Sciences（期刊）

Applied Sciences is an international, peer-reviewed, open access journal on all aspects of applied natural sciences published semimonthly online by MDPI.

Journal

Hao Yang

Qin He

Zhenyan Liu

Qian Zhang

The development of Internet and network applications has brought the development of encrypted communication technology. But on this basis, malicious traffic also uses encryption to avoid traditional security protection and detection. Traditional security protection and detection methods cannot accurately detect encrypted malicious traffic. In recent years, the rise of artificial intelligence allows us to use machine learning and deep learning methods to detect encrypted malicious traffic without decryption, and the detection results are very accurate. At present, the research on malicious encrypted traffic detection mainly focuses on the characteristics’ analysis of encrypted traffic and the selection of machine learning algorithms. In this paper, a method combining natural language processing and machine learning is proposed; that is, a detection method based on TF-IDF is proposed to build a detection model. In the process of data preprocessing, this method introduces the natural language processing method, namely, the TF-IDF model, to extract data information, obtain the importance of keywords, and then reconstruct the characteristics of data. The detection method based on the TF-IDF model does not need to analyze each field of the data set. Compared with the general machine learning data preprocessing method, that is, data encoding processing, the experimental results show that using natural language processing technology to preprocess data can effectively improve the accuracy of detection. Gradient boosting classifier, random forest classifier, AdaBoost classifier, and the ensemble model based on these three classifiers are, respectively, used in the construction of the later models. At the same time, CNN neural network in deep learning is also used for training, and CNN can effectively extract data information. Under the condition that the input data of the classifier and neural network are consistent, through the comparison and analysis of various methods, the accuracy of the one-dimensional convolutional network based on CNN is slightly higher than that of the classifier based on machine learning.