Survey on network system security metrics by Chensi WU, Weiqiang XIE, Yixiao JI, Su YANG, Ziyi JIA, Song ZHAO, Yuqing ZHANG

“…With the improvement for comprehensive and objective understanding of the network system,the research and application of network system security metrics (NSSM) are noticed more.The quantitative evaluation of network system security is developing towards precision and objectification.NSSM can provide the objective and scientific basis for the confrontation of attack-defense and decision of emergency response.The global metrics of network system security is a crucial point in the field of security metrics.From the perspective of global metrics,the status and role of global metrics in security evaluation were pointed out.Three development stages of metrics (perceiving,cognizing and deepening) and their characteristics were analyzed and summarized.The process of global metrics was described.The metrics models,metrics systems and metrics tools were analyzed,and their functions,interrelations,and features in security metrics were pointed out.Then the technical challenges of global metrics of network systems were explained in detail,and ten opportunities and challenges were summarized in tabular form.Finally,the next direction and development trend of network system security metrics research were forecasted.The survey shows that NSSM has a good application prospect in network security.…”
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Overview of detection techniques for malicious social bots by Rong LIU, Bo CHEN, Ling YU, Ya-shang LIU, Si-yuan CHEN

“…The attackers use social bots to steal people’s privacy,propagate fraud messages and influent public opinions,which has brought a great threat for personal privacy security,social public security and even the security of the nation.The attackers are also introducing new techniques to carry out anti-detection.The detection of malicious social bots has become one of the most important problems in the research of online social network security and it is also a difficult problem.Firstly,development and application of social bots was reviewed and then a formulation description for the problem of detecting malicious social bots was made.Besides,main challenges in the detection of malicious social bots were analyzed.As for how to choose features for the detection,the development of choosing features that from static user features to dynamic propagation features and to relationship and evolution features were classified.As for choosing which method,approaches from the previous research based on features,machine learning,graph and crowd sourcing were summarized.Also,the limitation of these methods in detection accuracy,computation cost and so on was dissected.At last,a framework based on parallelizing machine learning methods to detect malicious social bots was proposed.…”
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Research on Spam Filters Based on NB Algorithm by Su Shengyue

“…Spam filtering is a crucial part of network security. As spam becomes more complex, traditional rule-based methods struggle to meet the needs of modern email systems. …”
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A novel approach for graph-based real-time anomaly detection from dynamic network data listened by Wireshark by Muhammet Onur Kaya, Mehmet Ozdem, Resul Das

“…This research contributes valuable insights into network security and management, highlighting the importance of integrating advanced analytical methods with effective visualization strategies to enhance the overall management of dynamic networks. …”
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Deep learning-based method for mobile social networks with strong sparsity for link prediction by HE Yadi, LIU Linfeng

“…Link prediction, the process of uncovering potential relationships between nodes in a network through the use of deep learning techniques, is commonly applied in fields such as network security and information mining. It has been utilized to identify social engineering attacks, fraudulent activities, and privacy breach risks by predicting links between nodes within a network. …”
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User authentication of industrial internet based on HHT transform of mouse behavior by Yigong ZHANG, Qian YI, Jian LI, Congbo LI, Aijun YIN, Shuping YI

“…The rapid development of the industrial internet had caused widespread concern about the network security, and the end-user authentication technology was considered a research hotspot.According to the characteristics of human-computer interaction in industrial internet, an experimental website was designed.24 users' mouse behavior data in an uncontrolled environment were collected within 2.5 years to conduct case studies.Hilbert-Huang transform (HHT) was used to extract frequency domain features of mouse behavior signals, combined with time domain features to form a time-frequency joint domain feature matrix of 163-dimensional to characterize user mouse behavior patterns.Bagged tree, support vector machine (SVM), Boost tree and K-nearest neighbor (KNN) were used to build a user authentication model， and the comparison result showed that the Bagged tree had the best internal detection effect in this case, with an average false acceptance rate (FAR) of 0.12% and an average false rejection rate (FRR) of 0.28%.In external detection, the FAR was 1.47%.Compared with the traditional mouse dynamics method, the frequency domain information of mouse behavior extracted by HHT can better realize the user authentication, and provide technical support the security of the industrial internet.…”
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Improved Security Patch on Secure Communication among Cell Phones and Sensor Networks by Ndibanje Bruce, Tae-Yong Kim, Hoon Jae Lee

“…The communication between cell phones and sensor networks involves strong user authentication protocols to ensure the data and network security. Generally, in order to obtain the relevant information, cell phones interact with sensor networks via gateways. …”
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Efficient Pairing-Free Privacy-Preserving Auditing Scheme for Cloud Storage in Distributed Sensor Networks by Xinpeng Zhang, Chunxiang Xu, Xiaojun Zhang

“…The previous research of distributed sensor network security has focused on secure information in communication; however the research of secure data storage has been overlooked. …”
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EFFICIENT MALICIOUS NODE DETECTION IN WIRELESS SENSOR NETWORKS USING RABIN-KARP ALGORITHM by T Devapriya, V Ganesan, S Velmurugan

“…A scalable, lightweight algorithm that can detect and mitigate harmful behavior is the goal of this effort to improve network security. The Rabin-Karp method, well-known for its pattern-matching efficiency, is modified to verify transmitted data packets using hashes. …”
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Deep Learning Based DDoS Attack Detection by Xu Ziyi

“…Nowadays, one of the biggest risks to network security is Distributed Denial of Service (DDoS) assaults, which cause disruptions to services by flooding systems with malicious traffic. …”
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Network Packet Sniffer: A Case Study of Kabale University. by Mugarurebye, Shawn, Gumoshabe, Rebecca

“…As a fundamental procedure of network security measurement, network data collection executes real-time network monitoring, supports network performance evaluation, assists network billing, and helps traffic testing and filtering. …”
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DETECTING WEB-BASED BOTNETS USING A WEB PROXY AND A CONVOLUTIONAL NEURAL NETWORK by Trần Đắc Tốt, Phạm Tuấn Khiêm, Phạm Nguyễn Huy Phương

“…Botnets are increasingly becoming the most dangerous threats in the field of network security, and many different approaches to detecting attacks from botnets have been studied. …”
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Encrypted traffic identification scheme based on sliding window and randomness features by LIU Jiachi, KUANG Boyu, SU Mang, XU Yaqian, FU Anmin

“…With the development of information technology, network security has increasingly become a focal point for users and organizations, and encrypted data transmission has gradually become mainstream. …”
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Experimental Study and Theoretical Verification of Explosion-Proof Performance of Insulated Glass by Jinju Zhang, Xinjian Wang, Liqiong Wang, Pengchong Su

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Multi-feature fusion malware detection method based on attention and gating mechanisms by Zhongyuan CHEN, Jianbiao ZHANG

“…With the rapid development of network technology, the number and variety of malware have been increasing, posing a significant challenge in the field of network security.However, existing single-feature malware detection methods have proven inadequate in representing sample information effectively.Moreover, multi-feature detection approaches also face limitations in feature fusion, resulting in an inability to learn and comprehend the complex relationships within and between features.These limitations ultimately lead to subpar detection results.To address these issues, a malware detection method called MFAGM was proposed, which focused on multimodal feature fusion.By processing the .asm and .bytes files of the dataset, three key features belonging to two types (opcode statistics sequences, API sequences, and grey-scale image features) were successfully extracted.This comprehensive characterization of sample information from multiple perspectives aimed to improve detection accuracy.In order to enhance the fusion of these multimodal features, a feature fusion module called SA-JGmu was designed.This module utilized the self-attention mechanism to capture internal dependencies between features.It also leveraged the gating mechanism to enhance interactivity among different features.Additionally, weight-jumping links were introduced to further optimize the representational capabilities of the model.Experimental results on the Microsoft malware classification challenge dataset demonstrate that MFAGM achieves higher accuracy and F1 scores compared to other methods in the task of malware detection.…”
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Optimal Design of One-Sided Exponential Adaptive EWMA Scheme Based on Median Run Length by Yulong Qiao, Zixing Wu, Qian Zhang, Qin Xu, Ge Jin

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Review of malware detection and classification visualization techniques by Jinwei WANG, Zhengjia CHEN, Xue XIE, Xiangyang LUO, Bin MA

“…With the rapid advancement of technology, network security faces a significant challenge due to the proliferation of malicious software and its variants.These malicious software use various technical tactics to deceive or bypass traditional detection methods, rendering conventional non-visual detection techniques inadequate.In recent years, data visualization has gained considerable attention in the academic community as a powerful approach for detecting and classifying malicious software.By visually representing the key features of malicious software, these methods greatly enhance the accuracy of malware detection and classification, opening up extensive research opportunities in the field of cyber security.An overview of traditional non-visual detection techniques and visualization-based methods were provided in the realm of malicious software detection.Traditional non-visual approaches for malicious software detection, including static analysis, dynamic analysis, and hybrid techniques, were introduced.Subsequently, a comprehensive survey and evaluation of prominent contemporary visualization-based methods for detecting malicious software were undertaken.This primarily encompasses encompassed the integration of visualization with machine learning and visualization combined with deep learning, each of which exhibits distinct advantages and characteristics within the domain of malware detection and classification.Consequently, the holistic consideration of several factors, such as dataset size, computational resources, time constraints, model accuracy, and implementation complexity, is necessary for the selection of detection and classification methods.In conclusion, the challenges currently faced by detection technologies are summarized, and a forward-looking perspective on future research directions in the field is provided.…”
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Research on industrial Internet security detection and response based on digital twin by MA Jiali, GUO Yuanbo, FANG Chen, CHEN Qingli, ZHANG Qi

“…Considering that traditional network security defense methods cannot meet the strict requirements of industrial Internet for reliability and stability, a method for anomaly detection and response in digital space was studied based on the idea of digital twins by collecting on-site data and using twin model security cognition. …”
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Unbalanced protocol recognition method based on improved residual U-Net by Jisheng WU, Zheng HONG, Tiantian MA

“…An unbalanced protocol recognition method based on the improved Residual U-Net was proposed to solve the challenge of network security posed by the increasing network attacks with the continuous development of the Internet.In the captured network traffic, a small proportion is constituted by malicious traffic, typically utilizing minority protocols.However, existing protocol recognition methods struggle to accurately identify these minority protocols when the class distribution of the protocol data is imbalanced.To address this issue, an unbalanced protocol recognition method was proposed, which utilized the improved Residual U-Net, incorporating a novel activation function and the Squeeze-and-Excitation Networks (SE-Net) to enhance the feature extraction capability.The loss function employed in the proposed model was the weighted Dice loss function.In cases where the recognition accuracies of the minority protocols were low, the loss function value would be high.Consequently, the optimization direction of the model would be dominated by the minority protocols, resulting in improved recognition accuracies for them.During the protocol recognition process, the network flow was extracted from the network traffic and preprocessed to convert it into a one-dimensional matrix.Subsequently, the protocol recognition model extracted the features of the protocol data, and the Softmax classifier predicted the protocol types.Experimental results demonstrate that the proposed protocol recognition model achieves more accurate recognition of the minority protocols compared to the comparison model, while also improving the recognition accuracies of the majority protocols.…”
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IMPROVING INTRUSION DETECTION USING TREE ADJOINING GRAMMAR GUIDED GENETIC PROGRAMMING by Vũ Văn Cảnh, Hoàng Tuấn Hảo, Nguyễn Văn Hoàn

“…Nowadays, the problem of network security has become urgent and affect the performance of modern computer networks greatly. …”
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