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.

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。

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.

Applied Science

Xuan Wu

Yafei Song

Xiaoyi Hou

Zexuan Ma

Chen Chen

软件安全

deep learning; malware classification; sequential feature; temporal convolutional network; bidirectional gated recurrent unit

Currently, malware shows an explosive growth trend. Demand for classifying malware is also increasing. The problem is the low accuracy of both malware detection and classification. From the static features of malicious families, a new deep learning method of TCN-BiGRU was proposed in this study, which combined temporal convolutional network (TCN) and bidirectional gated recurrent unit (BiGRU). First, we extracted the features of malware assembly code sequences and byte code sequences. Second, we shortened the opcode sequences by TCN to explore the features in the data and then used the BiGRU network to capture the opcode sequences in both directions to achieve deep extraction of the features of the opcode sequences. Finally, the fully connected and softmax layers were used to output predictions of the deep features. Multiple comparisons and ablation experiments demonstrated that the accuracy of malware detection and classification were effectively improved by our method. Our overall performance was 99.72% for samples comprising nine different classes, and our overall performance was 96.54% for samples comprising two different classes.
Keywords: deep learning; malware classification; sequential feature; temporal convolutional network; bidirectional gated recurrent unit