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Li, Yanjie.  2021.  The Application Analysis of Artificial Intelligence in Computer Network Technology. 2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC). :1126–1129.
In the information age, computer network technology has covered different areas of social life and involved various fields, and artificial intelligence, as an emerging technology with a very rapid development momentum in recent years, is important in promoting the development of computer network systems. This article explains the concept of artificial intelligence technology, describes the problems faced by computer networks, further analyses the advantages of artificial intelligence and the inevitability of application in network technology, and then studies the application of artificial intelligence in computer network technology.
Wang, Wenhui, Han, Longxi, Ge, Guangkai, Yang, Zhenghao.  2021.  An Algorithm of Optimal Penetration Path Generation under Unknown Attacks of Electric Power WEB System Based on Knowledge Graph. 2021 2nd International Conference on Computer Communication and Network Security (CCNS). :141–144.
Aiming at the disadvantages of traditional methods such as low penetration path generation efficiency and low attack type recognition accuracy, an optimal penetration path generation algorithm based on the knowledge map power WEB system unknown attack is proposed. First, establish a minimum penetration path test model. And use the model to test the unknown attack of the penetration path under the power WEB system. Then, the ontology of the knowledge graph is designed. Finally, the design of the optimal penetration path generation algorithm based on the knowledge graph is completed. Experimental results show that the algorithm improves the efficiency of optimal penetration path generation, overcomes the shortcomings of traditional methods that can only describe known attacks, and can effectively guarantee the security of power WEB systems.
Wu, Kehe, Shi, Jin, Guo, Zhimin, Zhang, Zheng, Cai, Junfei.  2021.  Research on Security Strategy of Power Internet of Things Devices Based on Zero-Trust. 2021 International Conference on Computer Engineering and Application (ICCEA). :79–83.
In order to guarantee the normal operation of the power Internet of things devices, the zero-trust idea was used for studying the security protection strategies of devices from four aspects: user authentication, equipment trust, application integrity and flow baselines. Firstly, device trust is constructed based on device portrait; then, verification of device application integrity based on MD5 message digest algorithm to achieve device application trustworthiness. Next, the terminal network traffic baselines are mined from OpenFlow, a southbound protocol in SDN. Finally, according to the dynamic user trust degree attribute access control model, the comprehensive user trust degree was obtained by weighting the direct trust degree. It obtained from user authentication and the trust degree of user access to terminal communication traffic. And according to the comprehensive trust degree, users are assigned the minimum authority to access the terminal to realize the security protection of the terminal. According to the comprehensive trust degree, the minimum permissions for users to access the terminal were assigned to achieve the security protection of the terminal. The research shows that the zero-trust mechanism is applied to the terminal security protection of power Internet of Things, which can improve the reliability of the safe operation of terminal equipment.
Tekeoglu, Ali, Bekiroglu, Korkut, Chiang, Chen-Fu, Sengupta, Sam.  2021.  Unsupervised Time-Series Based Anomaly Detection in ICS/SCADA Networks. 2021 International Symposium on Networks, Computers and Communications (ISNCC). :1–6.
Traditionally, Industrial Control Systems (ICS) have been operated as air-gapped networks, without a necessity to connect directly to the Internet. With the introduction of the Internet of Things (IoT) paradigm, along with the cloud computing shift in traditional IT environments, ICS systems went through an adaptation period in the recent years, as the Industrial Internet of Things (IIoT) became popular. ICS systems, also called Cyber-Physical-Systems (CPS), operate on physical devices (i.e., actuators, sensors) at the lowest layer. An anomaly that effect this layer, could potentially result in physical damage. Due to the new attack surfaces that came about with IIoT movement, precise, accurate, and prompt intrusion/anomaly detection is becoming even more crucial in ICS. This paper proposes a novel method for real-time intrusion/anomaly detection based on a cyber-physical system network traffic. To evaluate the proposed anomaly detection method's efficiency, we run our implementation against a network trace taken from a Secure Water Treatment Testbed (SWAT) of iTrust Laboratory at Singapore.
Zheng, Shengbao, Shu, Shaolong, Lin, Feng.  2021.  Modeling and Control of Discrete Event Systems under Joint Sensor-Actuator Cyber Attacks. 2021 6th International Conference on Automation, Control and Robotics Engineering (CACRE). :216–220.
In this paper, we investigate joint sensor-actuator cyber attacks in discrete event systems. We assume that attackers can attack some sensors and actuators at the same time by altering observations and control commands. Because of the nondeterminism in observation and control caused by cyber attacks, the behavior of the supervised systems becomes nondeterministic and deviates from the target. We define two bounds on languages, an upper-bound and a lower-bound, to describe the nondeterministic behavior. We then use the upper-bound language to investigate the safety supervisory control problem under cyber attacks. After introducing CA-controllability and CA-observability, we successfully solve the supervisory control problem under cyber attacks.
Hu, Xiaoming, Tan, Wenan, Ma, Chuang.  2020.  Comment and Improvement on Two Aggregate Signature Schemes for Smart Grid and VANET in the Learning of Network Security. 2020 International Conference on Information Science and Education (ICISE-IE). :338–341.
Smart substation and Vehicular Ad-Hoc Network (VANET) are two important applications of aggregate signature scheme. Due to the large number of data collection equipment in substation, it needs security authentication and integrity protection to transmit data. Similarly, in VANET, due to limited resources, it has the needs of privacy protection and improving computing efficiency. Aggregate signature scheme can satisfy the above these needs and realize one-time verification of signature for multi-terminal data collection which can improve the performance. Aggregate signature scheme is an important technology to solve network security problem. Recently, many aggregate signature schemes are proposed which can be applied in smart grid or VANET. In this paper, we present two security analyses on two aggregate signature schemes proposed recently. By analysis, it shows that the two aggregate signature schemes do not satisfy the security property of unforgeability. A malicious user can forge a signature on any message. We also present some improved methods to solve these security problems with better performance. From security analysis to improvement of aggregate signature scheme, it is very suitable to be an instance to exhibit the students on designing of security aggregate signature scheme for network security education or course.
Duan, Junhong, Zhao, Bo, Guo, Sensen.  2020.  The Design and Implementation of Smart Grid SOC Platform. 2020 IEEE International Conference on Information Technology,Big Data and Artificial Intelligence (ICIBA). 1:264–268.
Smart grid is the key infrastructure of the country, and its network security is an important link to ensure the national important infrastructure security. SOC as a secure operation mechanism for adaptive and continuous improvement of information security, it is practically significant to address the challenge to the network security of the smart grid. Based on the analysis of the technical characteristics and security of smart grid, and taking a grid enterprise smart grid as an example, we propose the design scheme and implementation plan of smart grid SOC platform. Experimental results show that the platform we designed can meet the performance requirements, it also meets the requirements of real-time storage of behavioral data and provides support for interactive analysis and batch analysis.
Li, Jingyi, Yi, Xiaoyin, Wei, Shi.  2020.  A Study of Network Security Situational Awareness in Internet of Things. 2020 International Wireless Communications and Mobile Computing (IWCMC). :1624–1629.
As the application of Internet of Things technology becomes more common, the security problems derived from it became more and more serious. Different from the traditional Internet, the security of the Internet of Things presented new features. This paper introduced the current situation of Internet of Things security, generalized the definitions of situation awareness and network security situation awareness, and finally discussed the methods of establishing security situational awareness of Internet of Things which provided some tentative solutions to the new DDoS attack caused by Internet of Things terminals.
Wang, Zhe, Chen, Yonghong, Wang, Linfan, Xie, Jinpu.  2020.  A Flow Correlation Scheme Based on Perceptual Hash and Time-Frequency Feature. 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). 1:2023–2027.
Flow correlation can identify attackers who use anonymous networks or stepping stones. The current flow correlation scheme based on watermark can effectively trace the network traffic. But it is difficult to balance robustness and invisibility. This paper presents an innovative flow correlation scheme that guarantees invisibility. First, the scheme generates a two-dimensional feature matrix by segmenting the network flow. Then, features of frequency and time are extracted from the matrix and mapped into perceptual hash sequences. Finally, by comparing the hash sequence similarity to correlate the network flow, the scheme reduces the complexity of the correlation while ensuring the accuracy of the flow correlation. Experimental results show that our scheme is robust to jitter, packet insertion and loss.
Zahid, Muhammad Noaman, Jiang, Jianliang, Lu, Heng, Rizvi, Saad, Eric, Deborah, Khan, Shahrukh, Zhang, Hengli.  2020.  Security Issues and Challenges in RFID, Wireless Sensor Network and Optical Communication Networks and Solutions. 2020 IEEE 3rd International Conference of Safe Production and Informatization (IICSPI). :592–599.
Nowadays, Security is the biggest challenge in communication networks. Well defined security protocols not only solve the privacy and security issues but also help to reduce the implementation cost and simplify network's operation. Network society demands more reliable and secure network services as well as infrastructure. In communication networks, data theft, hacking, fraud, cyber warfare are serious security threats. Security as defined by experts is confirming protected communication amongst communication/computing systems and consumer applications in private and public networks, it is important for promising privacy, confidentiality, and protection of information. This paper highlights the security related issues and challenges in communication networks. We also present the holistic view for the underlaying physical layer including physical infrastructure attacks, jamming, interception, and eavesdropping. This research focused on improving the security measures and protocols in different communication networks.
Reshikeshan, Sree Subiksha M., Illindala, Mahesh S..  2020.  Systematically Encoded Polynomial Codes to Detect and Mitigate High-Status-Number Attacks in Inter-Substation GOOSE Communications. 2020 IEEE Industry Applications Society Annual Meeting. :1–7.
Inter-substation Generic Object Oriented Substation Events (GOOSE) communications that are used for critical protection functions have several cyber-security vulnerabilities. GOOSE messages are directly mapped to the Layer 2 Ethernet without network and transport layer headers that provide data encapsulation. The high-status-number attack is a malicious attack on GOOSE messages that allows hackers to completely take over intelligent electronic devices (IEDs) subscribing to GOOSE communications. The status-number parameter of GOOSE messages, stNum is tampered with in these attacks. Given the strict delivery time requirement of 3 ms for GOOSE messaging, it is infeasible to encrypt the GOOSE payload. This work proposes to secure the sensitive stNum parameter of the GOOSE payload using systematically encoded polynomial codes. Exploiting linear codes allows for the security features to be encoded in linear time, in contrast to complex hashing algorithms. At the subscribing IED, the security feature is used to verify that the stNum parameter has not been tampered with during transmission in the insecure medium. The decoding and verification using syndrome computation at the subscriber IED is also accomplished in linear time.
Zuo, Xinbin, Pang, Xue, Zhang, Pengping, Zhang, Junsan, Dong, Tao, Zhang, Peiying.  2020.  A Security-Aware Software-Defined IoT Network Architecture. 2020 IEEE Computing, Communications and IoT Applications (ComComAp). :1–5.
With the improvement of people's living standards, more and more network users access the network, including a large number of infrastructure, these devices constitute the Internet of things(IoT). With the rapid expansion of devices in the IoT, the data transmission between the IoT has become more complex, and the security issues are facing greater challenges. SDN as a mature network architecture, its security has been affirmed by the industry, it separates the data layer from the control layer, thus greatly improving the security of the network. In this paper, we apply the SDN to the IoT, and propose a IoT network architecture based on SDN. In this architecture, we not only make use of the security features of SDN, but also deploy different security modules in each layer of SDN to integrate, analyze and plan various data through the IoT, which undoubtedly improves the security performance of the network. In the end, we give a comprehensive introduction to the system and verify its performance.
Mahmoud, Loreen, Praveen, Raja.  2020.  Network Security Evaluation Using Deep Neural Network. 2020 15th International Conference for Internet Technology and Secured Transactions (ICITST). :1–4.
One of the most significant systems in computer network security assurance is the assessment of computer network security. With the goal of finding an effective method for performing the process of security evaluation in a computer network, this paper uses a deep neural network to be responsible for the task of security evaluating. The DNN will be built with python on Spyder IDE, it will be trained and tested by 17 network security indicators then the output that we get represents one of the security levels that have been already defined. The maj or purpose is to enhance the ability to determine the security level of a computer network accurately based on its selected security indicators. The method that we intend to use in this paper in order to evaluate network security is simple, reduces the human factors interferences, and can obtain the correct results of the evaluation rapidly. We will analyze the results to decide if this method will enhance the process of evaluating the security of the network in terms of accuracy.
Cao, Yaofu, Li, Xiaomeng, Zhang, Shulin, Li, Yang, Chen, Liang, He, Yunrui.  2020.  Design of network security situation awareness analysis module for electric power dispatching and control system. 2020 2nd International Conference on Information Technology and Computer Application (ITCA). :716–720.
The current network security situation of the electric power dispatching and control system is becoming more and more severe. On the basis of the original network security management platform, to increase the collection of network security data information and improve the network security analysis ability, this article proposes the electric power dispatching and control system network security situation awareness analysis module. The perception layer accesses multi-source heterogeneous data sources. Upwards through the top layer, data standardization will be introduced, who realizes data support for security situation analysis, and forms an association mapping with situation awareness elements such as health situation, attack situation, behavior situation, and operation situation. The overall effect is achieving the construction goals of "full control of equipment status, source of security attacks can be traced, operational risks are identifiable, and abnormal behaviors can be found.".
Almohri, Hussain M. J., Watson, Layne T., Evans, David.  2020.  An Attack-Resilient Architecture for the Internet of Things. IEEE Transactions on Information Forensics and Security. 15:3940–3954.
With current IoT architectures, once a single device in a network is compromised, it can be used to disrupt the behavior of other devices on the same network. Even though system administrators can secure critical devices in the network using best practices and state-of-the-art technology, a single vulnerable device can undermine the security of the entire network. The goal of this work is to limit the ability of an attacker to exploit a vulnerable device on an IoT network and fabricate deceitful messages to co-opt other devices. The approach is to limit attackers by using device proxies that are used to retransmit and control network communications. We present an architecture that prevents deceitful messages generated by compromised devices from affecting the rest of the network. The design assumes a centralized and trustworthy machine that can observe the behavior of all devices on the network. The central machine collects application layer data, as opposed to low-level network traffic, from each IoT device. The collected data is used to train models that capture the normal behavior of each individual IoT device. The normal behavioral data is then used to monitor the IoT devices and detect anomalous behavior. This paper reports on our experiments using both a binary classifier and a density-based clustering algorithm to model benign IoT device behavior with a realistic test-bed, designed to capture normal behavior in an IoT-monitored environment. Results from the IoT testbed show that both the classifier and the clustering algorithms are promising and encourage the use of application-level data for detecting compromised IoT devices.
Conference Name: IEEE Transactions on Information Forensics and Security
Bhati, Akhilesh, Bouras, Abdelaziz, Ahmed Qidwai, Uvais, Belhi, Abdelhak.  2020.  Deep Learning Based Identification of DDoS Attacks in Industrial Application. 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). :190–196.
Denial of Service (DoS) attacks are very common type of computer attack in the world of internet today. Automatically detecting such type of DDoS attack packets & dropping them before passing through is the best prevention method. Conventional solution only monitors and provide the feedforward solution instead of the feedback machine-based learning. A Design of Deep neural network has been suggested in this paper. In this approach, high level features are extracted for representation and inference of the dataset. Experiment has been conducted based on the ISCX dataset for year 2017, 2018 and CICDDoS2019 and program has been developed in Matlab R17b using Wireshark.
Kong, Tong, Wang, Liming, Ma, Duohe, Chen, Kai, Xu, Zhen, Lu, Yijun.  2020.  ConfigRand: A Moving Target Defense Framework against the Shared Kernel Information Leakages for Container-based Cloud. 2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS). :794—801.
Lightweight virtualization represented by container technology provides a virtual environment for cloud services with more flexibility and efficiency due to the kernel-sharing property. However, the shared kernel also means that the system isolation mechanisms are incomplete. Attackers can scan the shared system configuration files to explore vulnerabilities for launching attacks. Previous works mainly eliminate the problem by fixing operating systems or using access control policies, but these methods require significant modifications and cannot meet the security needs of individual containers accurately. In this paper, we present ConfigRand, a moving target defense framework to prevent the information leakages due to the shared kernel in the container-based cloud. The ConfigRand deploys deceptive system configurations for each container, bounding the scan of attackers aimed at the shared kernel. In design of ConfigRand, we (1) propose a framework applying the moving target defense philosophy to periodically generate, distribute, and deploy the deceptive system configurations in the container-based cloud; (2) establish a model to formalize these configurations and quantify their heterogeneity; (3) present a configuration movement strategy to evaluate and optimize the variation of configurations. The results show that ConfigRand can effectively prevent the information leakages due to the shared kernel and apply to typical container applications with minimal system modification and performance degradation.
Billah, Mohammad Masum, Khan, Niaz Ahmed, Ullah, Mohammad Woli, Shahriar, Faisal, Rashid, Syed Zahidur, Ahmed, Md Razu.  2020.  Developing a Secured and Reliable Vehicular Communication System and Its Performance Evaluation. 2020 IEEE Region 10 Symposium (TENSYMP). :60–65.
The Ad-hoc Vehicular networks (VANET) was developed through the implementation of the concepts of ad-hoc mobile networks(MANET), which is swiftly maturing, promising, emerging wireless communication technology nowadays. Vehicular communication enables us to communicate with other vehicles and Roadside Infrastructure Units (RSU) to share information pertaining to the safety system, traffic analysis, Authentication, privacy, etc. As VANETs operate in an open wireless connectivity system, it increases permeable of variant type's security issues. Security concerns, however, which are either generally seen in ad-hoc networks or utterly unique to VANET, present significant challenges. Access Control List (ACL) can be an efficient feature to solve such security issues by permitting statements to access registered specific IP addresses in the network and deny statement unregistered IP addresses in the system. To establish such secured VANETs, the License number of the vehicle will be the Identity Number, which will be assigned via a DNS server by the Traffic Certification Authority (TCA). TCA allows registered vehicles to access the nearest two or more regions. For special vehicles, public access should be restricted by configuring ACL on a specific IP. Smart-card given by TCA can be used to authenticate a subscriber by checking previous records during entry to a new network area. After in-depth analysis of Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), Average Delay, and Handover Delay, this research offers more secure and reliable communication in VANETs.
Qi, Xiaoxia, Shen, Shuai, Wang, Qijin.  2020.  A Moving Target Defense Technology Based on SCIT. 2020 International Conference on Computer Engineering and Application (ICCEA). :454—457.
Moving target defense technology is one of the revolutionary techniques that is “changing the rules of the game” in the field of network technology, according to recent propositions from the US Science and Technology Commission. Building upon a recently-developed approach called Self Cleansing Intrusion Tolerance (SCIT), this paper proposes a moving target defense system that is based on server switching and cleaning. A protected object is maneuvered to improve its safety by exploiting software diversity and thereby introducing randomness and unpredictability into the system. Experimental results show that the improved system increases the difficulty of attack and significantly reduces the likelihood of a system being invaded, thus serving to enhance system security.
Idhom, M., Wahanani, H. E., Fauzi, A..  2020.  Network Security System on Multiple Servers Against Brute Force Attacks. 2020 6th Information Technology International Seminar (ITIS). :258—262.
Network security is critical to be able to maintain the information, especially on servers that store a lot of information; several types of attacks can occur on servers, including brute force and DDoS attacks; in the case study in this research, there are four servers used so that a network security system that can synchronize with each other so that when one server detects an attack, another server can take precautions before the same attack occurs on another server.fail2ban is a network security tool that uses the IDPS (Intrusion Detection and Prevention System) method which is an extension of the IDS (Intrusion Detection System) combined with IP tables so that it can detect and prevent suspicious activities on a network, fail2ban automatically default can only run on one server without being able to synchronize on other servers. With a network security system that can run on multiple servers, the attack prevention process can be done faster because when one server detects an attack, another server will take precautions by retrieving the information that has entered the collector database synchronizing all servers other servers can prevent attacks before an attack occurs on that server.
Behrens, Hans Walter, Candan, K. Selçuk.  2020.  Practical Security for Cooperative Ad Hoc Systems. 2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops). :1–2.
Existing consumer devices represent the most pervasive computational platform available, but their inherently decentralized nature poses significant challenges for distributed computing adoption. In particular, device owners must willingly cooperate in collective deployments even while others may intentionally work to maliciously disrupt that cooperation. Public, cooperative systems benefit from low barriers to entry improving scalability and adoption, but simultaneously increase risk exposure to adversarial threats via promiscuous participant adoption. In this work, I aim to facilitate widespread adoption of cooperative systems by discussing the unique security and operational challenges of these systems, and highlighting several novel approaches that mitigate these disadvantages.
Xu, Hui, Zhang, Wei, Gao, Man, Chen, Hongwei.  2020.  Clustering Analysis for Big Data in Network Security Domain Using a Spark-Based Method. 2020 IEEE 5th International Symposium on Smart and Wireless Systems within the Conferences on Intelligent Data Acquisition and Advanced Computing Systems (IDAACS-SWS). :1—4.
Considering the problem of network security under the background of big data, the clustering analysis algorithms can be utilized to improve the correctness of network intrusion detection models for security management. As a kind of iterative clustering analysis algorithm, K-means algorithm is not only simple but also efficient, so it is widely used. However, the traditional K-means algorithm cannot well solve the network security problem when facing big data due to its high complexity and limited processing ability. In this case, this paper proposes to optimize the traditional K-means algorithm based on the Spark platform and deploy the optimized clustering analysis algorithm in the distributed architecture, so as to improve the efficiency of clustering algorithm for network intrusion detection in big data environment. The experimental result shows that, compared with the traditional K-means algorithm, the efficiency of the optimized K-means algorithm using a Spark-based method is significantly improved in the running time.
Xu, Yizheng.  2020.  Application Research Based on Machine Learning in Network Privacy Security. 2020 International Conference on Computer Information and Big Data Applications (CIBDA). :237—240.
As the hottest frontier technology in the field of artificial intelligence, machine learning is subverting various industries step by step. In the future, it will penetrate all aspects of our lives and become an indispensable technology around us. Among them, network security is an area where machine learning can show off its strengths. Among many network security problems, privacy protection is a more difficult problem, so it needs more introduction of new technologies, new methods and new ideas such as machine learning to help solve some problems. The research contents for this include four parts: an overview of machine learning, the significance of machine learning in network security, the application process of machine learning in network security research, and the application of machine learning in privacy protection. It focuses on the issues related to privacy protection and proposes to combine the most advanced matching algorithm in deep learning methods with information theory data protection technology, so as to introduce it into biometric authentication. While ensuring that the loss of matching accuracy is minimal, a high-standard privacy protection algorithm is concluded, which enables businesses, government entities, and end users to more widely accept privacy protection technology.
Zhu, Pengfei, Cui, Jiabin, Ji, Yuefeng.  2020.  A Built-in Hash Permutation Assisted Cross-layer Secure Transport in End-to-End FlexE over WDM Networks. GLOBECOM 2020 - 2020 IEEE Global Communications Conference. :1—5.

With the traffic growth with different deterministic transport and isolation requirements in radio access networks (RAN), Flexible Ethernet (FlexE) over wavelength division multiplexing (WDM) network is as a candidate for next generation RAN transport, and the security issue in RAN transport is much more obvious, especially the eavesdropping attack in physical layer. Therefore, in this work, we put forward a cross-layer design for security enhancement through leveraging universal Hashing based FlexE data block permutation and multiple parallel fibre transmission for anti-eavesdropping in end-to-end FlexE over WDM network. Different levels of attack ability are considered for measuring the impact on network security and resource utilization. Furthermore, the trade-off problem between efficient resource utilization and guarantee of higher level of security is also explored. Numerical results demonstrate the cross-layer defense strategies are effective to struggle against intruders with different levels of attack ability.

Liu, Shuyong, Jiang, Hongrui, Li, Sizhao, Yang, Yang, Shen, Linshan.  2020.  A Feature Compression Technique for Anomaly Detection Using Convolutional Neural Networks. 2020 IEEE 14th International Conference on Anti-counterfeiting, Security, and Identification (ASID). :39–42.
Anomaly detection classification technology based on deep learning is one of the crucial technologies supporting network security. However, as the data increasing, this traditional model cannot guarantee that the false alarm rate is minimized while meeting the high detection rate. Additionally, distribution of imbalanced abnormal samples will lead to an increase in the error rate of the classification results. In this work, since CNN is effective in network intrusion classification, we embed a compressed feature layer in CNN (Convolutional Neural Networks). The purpose is to improve the efficiency of network intrusion detection. After our model was trained for 55 epochs and we set the learning rate of the model to 0.01, the detection rate reaches over 98%.