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16th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information technology, Applications and Tools, FITAT 2020

作者： Nguyen, Trong-The Yu, Jie Nguyen, Thi-Thanh-Tan Dao, Thi-Kien Ngo, Truong-Giang Fujian Provincial Key Lab of Big-Data-Mining and Applications Fujian University of Technology Fujian350118 China College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou350118 China Information Technology Faculty Electric Power University Hanoi Viet Nam Faculty of Computer Science and Engineering Thuyloi-University 175 Tay Son Dong-Da Hanoi Viet Nam University of Management and Technology Haiphong180000 Viet Nam

ISBN: (纸本)9789813367562

The sensor node localization is related to the NP-hard problem due to the complicated determination of the actual coordinate of the sensor node. The metaheuristic algorithms considered as computational intelligence approach is one of the most affected ways to assist in obtaining an optimal solution to the given NP-hard problem. This study presents a new localization solution in wireless sensor networks (WSN) by using glowworms swarm optimization (GSO) combined localizing model. The objective function is calculated mathematically based on three dimension (3D) of the localizing model and Pareto distances. The glowworms groups are created by dividing from the GSO population for their updating positions to enhance swarms for improving localization accuracy. The simulation compared results of the computational algorithm suggest that the proposed task using GSO produces better performance in terms of mean localization error computational cost and the number of localized nodes than the competitors. The proposed scheme also reduced the line of sight problem for sensor nodes with efficient use of network resources. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Sensor nodes

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Cross-Language Binary-Source Code Matching with Intermediate Representations

arXiv

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arXiv 2022年

作者： Gui, Yi Wan, Yao Zhang, Hongyu Huang, Huifang Sui, Yulei Xu, Guandong Shao, Zhiyuan Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China The University of Newcastle Australia School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan China School of Computer Science University of Technology Sydney Australia

Binary-source code matching plays an important role in many security and software engineering related tasks such as malware detection, reverse engineering and vulnerability assessment. Currently, several approaches have been proposed for binary-source code matching by jointly learning the embeddings of binary code and source code in a common vector space. Despite much effort, existing approaches target on matching the binary code and source code written in a single programming language. However, in practice, software applications are often written in different programming languages to cater for different requirements and computing platforms. Matching binary and source code across programming languages introduces additional challenges when maintaining multi-language and multi-platform applications. To this end, this paper formulates the problem of cross-language binary-source code matching, and develops a new dataset for this new problem. We present a novel approach XLIR, which is a Transformer-based neural network by learning the intermediate representations for both binary and source code. To validate the effectiveness of XLIR, comprehensive experiments are conducted on two tasks of cross-language binary-source code matching, and cross-language source-source code matching, on top of our curated dataset. Experimental results and analysis show that our proposed XLIR with intermediate representations significantly outperforms other state-of-the-art models in both of the two tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Binary codes

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Logiformer: A Two-Branch Graph Transformer Network for Interpretable Logical Reasoning

arXiv

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arXiv 2022年

作者： Xu, Fangzhi Liu, Jun Lin, Qika Pan, Yudai Zhang, Lingling School of Computer Science and Technology Xi'an Jiaotong University Xi'an China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&d National Engineering Lab for Big Data Analytics Xi'an China

Machine reading comprehension has aroused wide concerns, since it explores the potential of model for text understanding. To further equip the machine with the reasoning capability, the challenging task of logical reasoning is proposed. Previous works on logical reasoning have proposed some strategies to extract the logical units from different aspects. However, there still remains a challenge to model the long distance dependency among the logical units. Also, it is demanding to uncover the logical structures of the text and further fuse the discrete logic to the continuous text embedding. To tackle the above issues, we propose an end-to-end model Logiformer which utilizes a two-branch graph transformer network for logical reasoning of text. Firstly, we introduce different extraction strategies to split the text into two sets of logical units, and construct the logical graph and the syntax graph respectively. The logical graph models the causal relations for the logical branch while the syntax graph captures the co-occurrence relations for the syntax branch. Secondly, to model the long distance dependency, the node sequence from each graph is fed into the fully connected graph transformer structures. The two adjacent matrices are viewed as the attention biases for the graph transformer layers, which map the discrete logical structures to the continuous text embedding space. Thirdly, a dynamic gate mechanism and a question-aware self-attention module are introduced before the answer prediction to update the features. The reasoning process provides the interpretability by employing the logical units, which are consistent with human cognition. The experimental results show the superiority of our model, which outperforms the state-of-the-art single model on two logical reasoning benchmarks. © 2022, CC BY.

关键词： Embeddings

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Automatically derived stateful network functions including non-field attributes

Automatically derived stateful network functions including n...

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

作者： Bin Yuan Shengyao Sun Xianjun Deng Deqing Zou Haoyu Chen Shenghui Li Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Shenzhen Research Institute Huazhong University of Science and Technology Shenzhen China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Cluster and Grid Computing Lab

ISBN: (纸本)9781665416597

The modern network consists of thousands of network devices from different suppliers that perform distinct code-pendent functions, such as routing, switching, modifying header fields, and access control across physical and virtual networks. Because of the network complexity, the network is prone to a wide range of errors, such as false-positive configuration, software errors, or unexpected interactions across protocols. These errors can lead to loops, sub-optimal routing, path leaks, black holes, and access control violations that make services unavailable, vulnerable to exploitation, or prone to attacks (e.g., DDoS attacks). To mitigate these problems, network operators deploy many different stateful network functions, like firewalls, NATs, load balancers, and intrusion-prevention boxes. They have become an important part of networks today, so it is critical to verify that these network functions are the same as expected deployments. All static network verification tools are meant to rigorously check network software or configuration for bugs before deployment. They usually use handwritten models or limited derivation models that are error-prone and ignore the fact that even the same type of network functions (from different vendors) still have different implementation details. In this paper, we propose a tool that can automatically synthesize more realistic and high-fidelity models that include stateful network functions with non-field attributes. We design an inferring algorithm, implement the transformation between data packages and symbolic packages, and obtain a finite state machine that can accurately express the actions of black-box network functions for a given configuration.

关键词： Access control Performance evaluation Privacy Protocols Computational modeling Switches Routing

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Contrastive Learning for Robust Android Malware Familial Classification

arXiv

引用

arXiv 2021年

作者： Wu, Yueming Dou, Shihan Zou, Deqing Yang, Wei Qiang, Weizhong Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai200433 China University of Texas at Dallas Dallas United States National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Due to its open-source nature, Android operating system has been the main target of attackers to exploit. Malware creators always perform different code obfuscations on their apps to hide malicious activities. Features extracted from these obfuscated samples through program analysis contain many useless and disguised features, which leads to many false negatives. To address the issue, in this paper, we demonstrate that obfuscation-resilient malware family analysis can be achieved through contrastive learning. The key insight behind our analysis is that contrastive learning can be used to reduce the difference introduced by obfuscation while amplifying the difference between malware and other types of malware. Based on the proposed analysis, we design a system that can achieve robust and interpretable classification of Android malware. To achieve robust classification, we perform contrastive learning on malware samples to learn an encoder that can automatically extract robust features from malware samples. To achieve interpretable classification, we transform the function call graph of a sample into an image by centrality analysis. Then the corresponding heatmaps can be obtained by visualization techniques. These heatmaps can help users understand why the malware is classified as this family. We implement IFDroid and perform extensive evaluations on two datasets. Experimental results show that IFDroid is superior to state-of-the-art Android malware familial classification systems. Moreover, IFDroid is capable of maintaining a 98.4% F1 on classifying 69,421 obfuscated malware samples. © 2021, CC0.

关键词： Android (operating system)

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Intelligent thermostatic cold storage design strategies based on Monte Carlo and graph neural networks

Intelligent thermostatic cold storage design strategies base...

引用

Computer Vision, Image and Deep Learning (CVIDL), International Conference on

作者： Kaichen Ouyang Chang Liu Minyu Qiu Zixin Wang TianXiang Xu University of Science and Technology of China school of mathematical science Hefei Anhui China Chinese Health Medical Information and Big Data Association-Information and Application Security Agency Beijing China China Jiliang University Information Engineering College Hangzhou Zhejiang China Beijing Normal University HoncKong Baptist University Unitednternational College Zhuhai Guangdong China University of Jinan School of Information Science and Engineering Jinan Shandong China

We first introduce four annular lattice systems with periodic boundaries as the basic model of a cold storage cooler. The four annular lattice systems represent the four subsystems of the refrigeration system and are located at the four corners of the cold storage. We introduce the energon as a self-driven particle in the system. The energy generated by the energon leaping through the subsystems ultimately serves as the source of cooling power for each subsystem, and the energon can be adsorbed into each subsystem from an external particle source, detached from each subsystem to an external particle source, moved from one subsystem to another by leapfrogging, or leapfrogged within a subsystem. At the same time, we introduce slow movers, which obey a Poisson distribution and enter each lattice point of the ystem with equal probability with parameter. When there are slow movers in the front lattice of the energetics, the leap rate of the energetics will be slowed down, thus preventing the system from being overpowered and fine-tuning the power of the system. We use Monte Carlo simulation to simulate the jumping process of energetons in the system and calculate the cooling power of the system with different parameters. By adjusting the particle adsorption rate, desorption rate, and the entry rate of slow movers, the temperature can drop to the target temperature quickly and temperature. Finally, we use CNN convolutional neural network to perform supervised learning image classification of the spatio-temporal evolution diagrams of closed system, evanescent system and absorbing system, which are three kinds of systems.

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Secondary Access Class Barring for Machine Type Communications with PUSCH Resource Constraints

Secondary Access Class Barring for Machine Type Communicatio...

引用

IEEE Conference on Vehicular technology (VTC)

作者： Yuxuan Wen Liu Yang Chaoyuan Bai Heng Liu Communication and Sensor Network for Modern Transportation International Cooperation Center(CSNMT) Southwest Jiaotong University Chengdu P.R.China Key Lab of Information Coding & Transmission Institude of Mobile Communication Southwest Jiaotong University Chengdu P.R.China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu P.R.China

ISBN: (数字)9798331517786

ISBN: (纸本)9798331517793

With the continuous increase of IoT devices, multiple devices desire to use network resources simultaneously during peak hours and overload conditions, leading to collisions and a reduction in the network’s effective throughput. The Access Class Barring (ACB) method has been proposed as the optimal potential solution in LTE communication networks to overcome this problem. However, existing ACB schemes do not fully utilize preamble resources, and most schemes do not consider the situation where PUSCH resources are limited. Therefore, this paper proposes a two-stage ACB algorithm that considers the situation of limited PUSCH resources. This algorithm recycles part of the preamble resources and sets the ACB factor based on the number of PUSCH resources. Simulation results show that our proposed algorithm significantly outperforms traditional schemes in terms of total service time, delay, throughput, and PUSCH resource efficiency.

关键词： Vehicular and wireless technologies Delay effects Computer simulation Throughput Delays Internet of Things Communication networks Long Term Evolution

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Attention in Attention: Modeling Context Correlation for Efficient Video Classification

arXiv

引用

arXiv 2022年

作者： Hao, Yanbin Wang, Shuo Cao, Pei Gao, Xinjian Xu, Tong Wu, Jinmeng He, Xiangnan The CCCD Key Lab of Ministry of Culture and Tourism School of Data Science School of Information Science and Technology University of Science and Technology of China Anhui 230026 China The Wuhan Research Institute of Posts and Telecommunications Hubei Wuhan430205 China The School of Computer Science and Information Engineering School of Artificial Intelligence Hefei University of Technology Anhui 230009 China The School of Data Science School of Computer Science and Technology University of Science and Technology of China Anhui 230026 China The Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Hubei Wuhan430070 China

Attention mechanisms have significantly boosted the performance of video classification neural networks thanks to the utilization of perspective contexts. However, the current research on video attention generally focuses on adopting a specific aspect of contexts (e.g., channel, spatial/temporal, or global context) to refine the features and neglects their underlying correlation when computing attentions. This leads to incomplete context utilization and hence bears the weakness of limited performance improvement. To tackle the problem, this paper proposes an efficient attention-in-attention (AIA) method for element-wise feature refinement, which investigates the feasibility of inserting the channel context into the spatio-temporal attention learning module, referred to as CinST, and also its reverse variant, referred to as STinC. Specifically, we instantiate the video feature contexts as dynamics aggregated along a specific axis with global average and max pooling operations. The workflow of an AIA module is that the first attention block uses one kind of context information to guide the gating weights calculation of the second attention that targets at the other context. Moreover, all the computational operations in attention units act on the pooled dimension, which results in quite few computational cost increase ( Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Analysis and Improvement of Otway-Rees Based on Enhanced Authentication Tests

引用

International Journal of Network Security 2021年第3期23卷 426-435页

作者： Yu, Lei Guo, Yu-Yan Zhuo, Ze-Peng Wei, Shi-Min School of Computer Science and Technology Huaibei Normal Universtiy School of Information and Control Engineering China University of Mining and Technology School of Mathematical Sciences Huaibei Normal University Anhui Big-Data Research Center on University Management Anhui Huaibei235000 China

To improve the formalization and accuracy of enhanced authentication tests and the effectiveness of security protocol analysis and improvement, the theory and methods of authentication tests are extended and optimized from principal knowability, principal identity, parameter types, and roles challenge-response structure. Then, based on the optimized enhanced authentication tests method, the correspondence of the original Otway-Rees protocol is analyzed, and the main factors that cause principals to fail to reach consistency on the session key are found. According to the correspondence degree of principals, by the reconstruction and construction of the test components on the existing messages, three new, improved schemes of Otway-Rees protocol are given without changing the original cryptosystem, and fresh value mechanism, the order, and the number of messages exchanged among principals. The practice of protocol analysis shows that optimized enhanced authentication tests can improve the formalization of protocol analysis and reduce the complexity of protocol analysis and accurately locate protocol defects and provide correction schemes. © 2021. All Rights Reserved

关键词： Authentication

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Group-Level Cognitive Diagnosis: A Multi-Task Learning Perspective

Group-Level Cognitive Diagnosis: A Multi-Task Learning Persp...

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IEEE International Conference on data Mining (ICDM)

作者： Jie Huang Qi Liu Fei Wang Zhenya Huang Songtao Fang Runze Wu Enhong Chen Yu Su Shijin Wang Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China Fuxi AI Lab NetEase Inc. Hangzhou China IFLYTEK Research

ISBN: (纸本)9781665423991

Most cognitive diagnosis research in education has been concentrated on individual assessment, aiming at discovering the latent characteristics of students. However, in many real-world scenarios, group-level assessment is an important and meaningful task, e.g., class assessment in different regions can discover the difference of teaching level in different contexts. In this work, we consider assessing cognitive ability for a group of students, which aims to mine groups’ proficiency on specific knowledge concepts. The significant challenge in this task is the sparsity of group-exercise response data, which seriously affects the assessment performance. Existing works either do not make effective use of additional student-exercise response data or fail to reasonably model the relationship between group ability and individual ability in different learning contexts, resulting in sub-optimal diagnosis results. To this end, we propose a general Multi-Task based Group-Level Cognitive Diagnosis (MGCD) framework, which is featured with three special designs: 1) We jointly model student-exercise responses and group-exercise responses in a multi-task manner to alleviate the sparsity of group-exercise responses; 2) We design a context-aware attention network to model the relationship between student knowledge state and group knowledge state in different contexts; 3) We model an interpretable cognitive layer to obtain student ability, group ability and exercise factors (e.g., difficulty), and then we leverage neural networks to learn complex interaction functions among them. Extensive experiments on real-world datasets demonstrate the generality of MGCD and the effectiveness of our attention design and multi-task learning.

关键词： Knowledge engineering Conferences Education Neural networks Multitasking data models data mining

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