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Roberts, Ciaran, Ngo, Sy-Toan, Milesi, Alexandre, Scaglione, Anna, Peisert, Sean, Arnold, Daniel.  2021.  Deep Reinforcement Learning for Mitigating Cyber-Physical DER Voltage Unbalance Attacks. 2021 American Control Conference (ACC). :2861–2867.
The deployment of DER with smart-inverter functionality is increasing the controllable assets on power distribution networks and, consequently, the cyber-physical attack surface. Within this work, we consider the use of reinforcement learning as an online controller that adjusts DER Volt/Var and Volt/Watt control logic to mitigate network voltage unbalance. We specifically focus on the case where a network-aware cyber-physical attack has compromised a subset of single-phase DER, causing a large voltage unbalance. We show how deep reinforcement learning successfully learns a policy minimizing the unbalance, both during normal operation and during a cyber-physical attack. In mitigating the attack, the learned stochastic policy operates alongside legacy equipment on the network, i.e. tap-changing transformers, adjusting optimally predefined DER control-logic.
Hu, Lei, Li, Guyue, Luo, Hongyi, Hu, Aiqun.  2021.  On the RIS Manipulating Attack and Its Countermeasures in Physical-Layer Key Generation. 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall). :1–5.
Reconfigurable Intelligent Surface (RIS) is a new paradigm that enables the reconfiguration of the wireless environment. Based on this feature, RIS can be employed to facilitate Physical-layer Key Generation (PKG). However, this technique could also be exploited by the attacker to destroy the key generation process via manipulating the channel features at the legitimate user side. Specifically, this paper proposes a new RIS-assisted Manipulating attack (RISM) that reduces the wireless channel reciprocity by rapidly changing the RIS reflection coefficient in the uplink and downlink channel probing step in orthogonal frequency division multiplexing (OFDM) systems. The vulnerability of traditional key generation technology based on channel frequency response (CFR) under this attack is analyzed. Then, we propose a slewing rate detection method based on path separation. The attacked path is removed from the time domain and a flexible quantization method is employed to maximize the Key Generation Rate (KGR). The simulation results show that under RISM attack, when the ratio of the attack path variance to the total path variance is 0.17, the Bit Disagreement Rate (BDR) of the CFR-based method is greater than 0.25, and the KGR is close to zero. In addition, the proposed detection method can successfully detect the attacked path for SNR above 0 dB in the case of 16 rounds of probing and the KGR is 35 bits/channel use at 23.04MHz bandwidth.
Foster, Rita, Priest, Zach, Cutshaw, Michael.  2021.  Infrastructure eXpression for Codified Cyber Attack Surfaces and Automated Applicability. 2021 Resilience Week (RWS). :1–4.
The internal laboratory directed research and development (LDRD) project Infrastructure eXpression (IX) at the Idaho National Laboratory (INL), is based on codifying infrastructure to support automatic applicability to emerging cyber issues, enabling automated cyber responses, codifying attack surfaces, and analysis of cyber impacts to our nation's most critical infrastructure. IX uses the Structured Threat Information eXpression (STIX) open international standard version 2.1 which supports STIX Cyber Observable (SCO) to codify infrastructure characteristics and exposures. Using these codified infrastructures, STIX Relationship Objects (SRO) connect to STIX Domain Objects (SDO) used for modeling cyber threat used to create attack surfaces integrated with specific infrastructure. This IX model creates a shareable, actionable and implementable attack surface that is updateable with emerging threat or infrastructure modifications. Enrichment of cyber threat information includes attack patterns, indicators, courses of action, malware and threat actors. Codifying infrastructure in IX enables creation of software and hardware bill of materials (SBoM/HBoM) information, analysis of emerging cyber vulnerabilities including supply chain threat to infrastructure.
Everson, Douglas, Cheng, Long.  2021.  Compressing Network Attack Surfaces for Practical Security Analysis. 2021 IEEE Secure Development Conference (SecDev). :23–29.
Testing or defending the security of a large network can be challenging because of the sheer number of potential ingress points that need to be investigated and evaluated for vulnerabilities. In short, manual security testing and analysis do not easily scale to large networks. While it has been shown that clustering can simplify the problem somewhat, the data structures and formats returned by the latest network mapping tools are not conducive to clustering algorithms. In this paper we introduce a hybrid similarity algorithm to compute the distance between two network services and then use those calculations to support a clustering algorithm designed to compress a large network attack surface by orders of magnitude. Doing so allows for new testing strategies that incorporate outlier detection and smart consolidation of test cases to improve accuracy and timeliness of testing. We conclude by presenting two case studies using an organization's network attack surface data to demonstrate the effectiveness of this approach.
Lee, Yun-kyung, Kim, Young-ho, Kim, Jeong-nyeo.  2021.  IoT Standard Platform Architecture That Provides Defense against DDoS Attacks. 2021 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia). :1–3.
IoT devices have evolved with the goal of becoming more connected. However, for security it is necessary to reduce the attack surface by allowing only necessary devices to be connected. In addition, as the number of IoT devices increases, DDoS attacks targeting IoT devices also increase. In this paper, we propose a method to apply the zero trust concept of SDP as a way to enhance security and prevent DDoS attacks in the IoT device network to which the OCF platform, one of the IoT standard platforms, is applied. The protocol proposed in this paper needs to perform additional functions in IoT devices, and the processing overhead due to the functions is 62.6ms on average. Therefore, by applying the method proposed in this paper, although there is a small amount of processing overhead, DDoS attacks targeting the IoT network can be defended and the security of the IoT network can be improved.
McCarthy, Andrew, Andriotis, Panagiotis, Ghadafi, Essam, Legg, Phil.  2021.  Feature Vulnerability and Robustness Assessment against Adversarial Machine Learning Attacks. 2021 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA). :1–8.
Whilst machine learning has been widely adopted for various domains, it is important to consider how such techniques may be susceptible to malicious users through adversarial attacks. Given a trained classifier, a malicious attack may attempt to craft a data observation whereby the data features purposefully trigger the classifier to yield incorrect responses. This has been observed in various image classification tasks, including falsifying road sign detection and facial recognition, which could have severe consequences in real-world deployment. In this work, we investigate how these attacks could impact on network traffic analysis, and how a system could perform misclassification of common network attacks such as DDoS attacks. Using the CICIDS2017 data, we examine how vulnerable the data features used for intrusion detection are to perturbation attacks using FGSM adversarial examples. As a result, our method provides a defensive approach for assessing feature robustness that seeks to balance between classification accuracy whilst minimising the attack surface of the feature space.
Li, Xiaolong, Zhao, Tengteng, Zhang, Wei, Gan, Zhiqiang, Liu, Fugang.  2021.  A Visual Analysis Framework of Attack Paths Based on Network Traffic. 2021 IEEE International Conference on Power Electronics, Computer Applications (ICPECA). :232–237.
With the rapid development of the Internet, cyberspace security has become a potentially huge problem. At the same time, the disclosure of cyberspace vulnerabilities is getting faster and faster. Traditional protection methods based on known features cannot effectively defend against new network attacks. Network attack is no more a single vulnerability exploit, but an APT attack based on multiple complicated methods. Cyberspace attacks have become ``rationalized'' on the surface. Currently, there are a lot of researches about visualization of attack paths, but there is no an overall plan to reproduce the attack path. Most researches focus on the detection and characterization individual based on single behavior cyberspace attacks, which loose it's abilities to help security personnel understand the complete attack behavior of attackers. The key factors of this paper is to collect the attackers' aggressive behavior by reverse retrospective method based on the actual shooting range environment. By finding attack nodes and dividing offensive behavior into time series, we can characterize the attacker's behavior path vividly and comprehensively.
Rahmansyah, Reyhan, Suryani, Vera, Arif Yulianto, Fazmah, Hidayah Ab Rahman, Nurul.  2021.  Reducing Docker Daemon Attack Surface Using Rootless Mode. 2021 International Conference on Software Engineering Computer Systems and 4th International Conference on Computational Science and Information Management (ICSECS-ICOCSIM). :499–502.
Containerization technology becomes one of alternatives in virtualization. Docker requires docker daemon to build, distribute and run the container and this makes the docker vulnerable to an attack surface called Docker daemon Attack Surface - an attack against docker daemon taking over the access (root). Using rootless mode is one way to prevent the attack. Therefore, this research demonstrates the attack prevention by making and running the docker container in the rootless mode. The success of the attack can be proven when the user is able to access the file /etc/shadow that is supposed to be only accessible for the rooted users. Findings of this research demonstrated that the file is inaccessible when the docker is run using the rootless mode. CPU usage is measured when the attack is being simulated using the docker run through root privileges and rootless mode, to identify whether the use of rootless mode in the docker adds the load of CPU usage and to what extent its increased. Results showed that the CPU use was 39% when using the docker with the rootless mode. Meanwhile, using the docker with the right of the root access was only 0%. The increase of 39% is commensurate with the benefit that can prevent the docker daemon attack surface.
Mehra, Ankush, Badotra, Sumit.  2021.  Artificial Intelligence Enabled Cyber Security. 2021 6th International Conference on Signal Processing, Computing and Control (ISPCC). :572–575.
In the digital era, cyber security has become a serious problem. Information penetrates, wholesale fraud, manual human test breaking, and other comparable occurrences proliferate, influencing a large number of individuals just as organizations. The hindrances have consistently been endless in creating appropriate controls and procedures and putting them in place with utmost precision in order to deal with cyber-attacks. To recent developments in artificial intelligence, the danger of cyber - attacks has increased drastically. AI has affected everything from healthcare to robots. Because malicious hackers couldn't keep this ball of fire from them, ``normal'' cyber-attacks have grown in to the ``intelligent'' cyber attacks. In this paper, The most promising artificial intelligence approaches are discussed. Researchers look at how such techniques may be used for cyber security. At last, the conversation concludes with a discussion about artificial intelligence's future and cyber security.
Al-Ameer, Ali, AL-Sunni, Fouad.  2021.  A Methodology for Securities and Cryptocurrency Trading Using Exploratory Data Analysis and Artificial Intelligence. 2021 1st International Conference on Artificial Intelligence and Data Analytics (CAIDA). :54–61.
This paper discusses securities and cryptocurrency trading using artificial intelligence (AI) in the sense that it focuses on performing Exploratory Data Analysis (EDA) on selected technical indicators before proceeding to modelling, and then to develop more practical models by introducing new reward loss function that maximizes the returns during training phase. The results of EDA reveal that the complex patterns within the data can be better captured by discriminative classification models and this was endorsed by performing back-testing on two securities using Artificial Neural Network (ANN) and Random Forests (RF) as discriminative models against their counterpart Na\"ıve Bayes as a generative model. To enhance the learning process, the new reward loss function is utilized to retrain the ANN with testing on AAPL, IBM, BRENT CRUDE and BTC using auto-trading strategy that serves as the intelligent unit, and the results indicate this loss superiorly outperforms the conventional cross-entropy used in predictive models. The overall results of this work suggest that there should be larger focus on EDA and more practical losses in the research of machine learning modelling for stock market prediction applications.
Ren, Sothearin, Kim, Jae-Sung, Cho, Wan-Sup, Soeng, Saravit, Kong, Sovanreach, Lee, Kyung-Hee.  2021.  Big Data Platform for Intelligence Industrial IoT Sensor Monitoring System Based on Edge Computing and AI. 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). :480–482.
The cutting edge of Industry 4.0 has driven everything to be converted to disruptive innovation and digitalized. This digital revolution is imprinted by modern and advanced technology that takes advantage of Big Data and Artificial Intelligence (AI) to nurture from automatic learning systems, smart city, smart energy, smart factory to the edge computing technology, and so on. To harness an appealing, noteworthy, and leading development in smart manufacturing industry, the modern industrial sciences and technologies such as Big Data, Artificial Intelligence, Internet of things, and Edge Computing have to be integrated cooperatively. Accordingly, a suggestion on the integration is presented in this paper. This proposed paper describes the design and implementation of big data platform for intelligence industrial internet of things sensor monitoring system and conveys a prediction of any upcoming errors beforehand. The architecture design is based on edge computing and artificial intelligence. To extend more precisely, industrial internet of things sensor here is about the condition monitoring sensor data - vibration, temperature, related humidity, and barometric pressure inside facility manufacturing factory.
Alamaniotis, Miltiadis.  2021.  Fuzzy Integration of Kernel-Based Gaussian Processes Applied to Anomaly Detection in Nuclear Security. 2021 12th International Conference on Information, Intelligence, Systems Applications (IISA). :1–4.
Advances in artificial intelligence (AI) have provided a variety of solutions in several real-world complex problems. One of the current trends contains the integration of various AI tools to improve the proposed solutions. The question that has to be revisited is how tools may be put together to form efficient systems suitable for the problem at hand. This paper frames itself in the area of nuclear security where an agent uses a radiation sensor to survey an area for radiological threats. The main goal of this application is to identify anomalies in the measured data that designate the presence of nuclear material that may consist of a threat. To that end, we propose the integration of two kernel modeled Gaussian processes (GP) by using a fuzzy inference system. The GP models utilize different types of information to make predictions of the background radiation contribution that will be used to identify an anomaly. The integration of the prediction of the two GP models is performed with means of fuzzy rules that provide the degree of existence of anomalous data. The proposed system is tested on a set of real-world gamma-ray spectra taken with a low-resolution portable radiation spectrometer.
Hu, Guangjun, Li, Haiwei, Li, Kun, Wang, Rui.  2021.  A Network Asset Detection Scheme Based on Website Icon Intelligent Identification. 2021 Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS). :255–257.
With the rapid development of the Internet and communication technologies, efficient management of cyberspace, safe monitoring and protection of various network assets can effectively improve the overall level of network security protection. Accurate, effective and comprehensive network asset detection is the prerequisite for effective network asset management, and it is also the basis for security monitoring and analysis. This paper proposed an artificial intelligence algorithm based scheme which accurately identify the website icon and help to determine the ownership of network assets. Through experiments based on data set collected from real network, the result demonstrate that the proposed scheme has higher accuracy and lower false alarm rate, and can effectively reduce the training cost.
Kalinin, Maxim O., Krundyshev, Vasiliy M..  2021.  Computational Intelligence Technologies Stack for Protecting the Critical Digital Infrastructures against Security Intrusions. 2021 Fifth World Conference on Smart Trends in Systems Security and Sustainability (WorldS4). :118–122.
Over the past decade, an infotelecommunication technology has made significant strides forward. With the advent of new generation wireless networks and the massive digitalization of industries, the object of protection has changed. The digital transformation has led to an increased opportunity for cybercriminals. The ability of computational intelligence to quickly process large amounts of data makes the intrusions tailored to specific environments. Polymorphic attacks that have mutations in their sequences of acts adapt to the communication environments, operating systems and service frameworks, and also try to deceive the defense tools. The poor protection of most Internet of Things devices allows the attackers to take control over them creating the megabotnets. In this regard, traditional methods of network protection become rigid and low-effective. The paper reviews a computational intelligence (CI) enabled software- defined network (SDN) for the network management, providing dynamic network reconfiguration to improve network performance and security control. Advanced machine learning and artificial neural networks are promising in detection of false data injections. Bioinformatics methods make it possible to detect polymorphic attacks. Swarm intelligence detects dynamic routing anomalies. Quantum machine learning is effective at processing the large volumes of security-relevant datasets. The CI technology stack provides a comprehensive protection against a variative cyberthreats scope.
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.
Vast, Rahul, Sawant, Shruti, Thorbole, Aishwarya, Badgujar, Vishal.  2021.  Artificial Intelligence Based Security Orchestration, Automation and Response System. 2021 6th International Conference for Convergence in Technology (I2CT). :1–5.
Cybersecurity is becoming very crucial in the today's world where technology is now not limited to just computers, smartphones, etc. It is slowly entering into things that are used on daily basis like home appliances, automobiles, etc. Thus, opening a new door for people with wrong intent. With the increase in speed of technology dealing with such issues also requires quick response from security people. Thus, dealing with huge variety of devices quickly will require some extent of automation in this field. Generating threat intelligence automatically and also including those which are multilingual will also add plus point to prevent well known major attacks. Here we are proposing an AI based SOAR system in which the data from various sources like firewalls, IDS, etc. is collected with individual event profiling using a deep-learning detection method. For this the very first step is that the collected data from different sources will be converted into a standardized format i.e. to categorize the data collected from different sources. For standardized format Here our system finds out about the true positive alert for which the appropriate/ needful steps will be taken such as the generation of Indicators of Compromise report and the additional evidences with the help of Security Information and Event Management system. The security alerts will be notified to the security teams with the degree of threat.
He, Zewei.  2021.  Communication Engineering Application System Based on Artificial Intelligence Technology. 2021 6th International Conference on Smart Grid and Electrical Automation (ICSGEA). :366–369.
In order to overcome a series of problems in the application process of traditional communication engineering in the new era, such as information security, this paper proposes a novel communication engineering application system based on artificial intelligence technology. The application system fully combines the artificial intelligence technology, and applies the artificial intelligence thinking to the reform of traditional communication engineering. Based on this, the application strategy also fully combines the application and development of 5g technology, and strengthens the security of communication engineering in the application process from many aspects. The results show that the application system can give full play to the role of artificial intelligence technology and improve the security of communication process as much as possible, which lays a good foundation for the further development of 5g technology.
Xu, Ling.  2021.  Application of Artificial Intelligence and Big Data in the Security of Regulatory Places. 2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA). :210–213.
This paper analyzes the necessity of artificial intelligence and big data in the security application of regulatory places. The author studies the specific application of artificial intelligence and big data in ideological dynamics management, access control system, video surveillance system, emergency alarm system, perimeter control system, police inspection system, daily behavior management, and system implementation management. The author puts forward how to do technical integration, improve information sharing, strengthen the construction of talents, and increase management fund expenditure. The purpose of this paper is to enhance the security management level of regulatory places and optimize the management environment of regulatory places.
Schrenk, Bernhard.  2021.  Simplified Synaptic Receptor for Coherent Optical Neural Networks. 2021 IEEE Photonics Society Summer Topicals Meeting Series (SUM). :1–2.
Advancing artificial neural networks to the coherent optical domain offers several advantages, such as a filterless synaptic interconnect with increased routing flexibility. Towards this direction, a coherent synaptic receptor with integrated multiplication function will be experimentally evaluated for a 1-GHz train of 130-ps spikes.
Viktoriia, Hrechko, Hnatienko, Hrygorii, Babenko, Tetiana.  2021.  An Intelligent Model to Assess Information Systems Security Level. 2021 Fifth World Conference on Smart Trends in Systems Security and Sustainability (WorldS4). :128–133.
This research presents a model for assessing information systems cybersecurity maturity level. The main purpose of the model is to provide comprehensive support for information security specialists and auditors in checking information systems security level, checking security policy implementation, and compliance with security standards. The model synthesized based on controls and practices present in ISO 27001 and ISO 27002 and the neural network of direct signal propagation. The methodology described in this paper can also be extended to synthesis a model for different security control sets and, consequently, to verify compliance with another security standard or policy. The resulting model describes a real non-automated process of assessing the maturity of an IS at an acceptable level and it can be recommended to be used in the process of real audit of Information Security Management Systems.
Paul, Avishek, Islam, Md Rabiul.  2021.  An Artificial Neural Network Based Anomaly Detection Method in CAN Bus Messages in Vehicles. 2021 International Conference on Automation, Control and Mechatronics for Industry 4.0 (ACMI). :1–5.
Controller Area Network is the bus standard that works as a central system inside the vehicles for communicating in-vehicle messages. Despite having many advantages, attackers may hack into a car system through CAN bus, take control of it and cause serious damage. For, CAN bus lacks security services like authentication, encryption etc. Therefore, an anomaly detection system must be integrated with CAN bus in vehicles. In this paper, we proposed an Artificial Neural Network based anomaly detection method to identify illicit messages in CAN bus. We trained our model with two types of attacks so that it can efficiently identify the attacks. When tested, the proposed algorithm showed high performance in detecting Denial of Service attacks (with accuracy 100%) and Fuzzy attacks (with accuracy 99.98%).
Sallam, Youssef F., Ahmed, Hossam El-din H., Saleeb, Adel, El-Bahnasawy, Nirmeen A., El-Samie, Fathi E. Abd.  2021.  Implementation of Network Attack Detection Using Convolutional Neural Network. 2021 International Conference on Electronic Engineering (ICEEM). :1–6.
The Internet obviously has a major impact on the global economy and human life every day. This boundless use pushes the attack programmers to attack the data frameworks on the Internet. Web attacks influence the reliability of the Internet and its administrations. These attacks are classified as User-to-Root (U2R), Remote-to-Local (R2L), Denial-of-Service (DoS) and Probing (Probe). Subsequently, making sure about web framework security and protecting data are pivotal. The conventional layers of safeguards like antivirus scanners, firewalls and proxies, which are applied to treat the security weaknesses are insufficient. So, Intrusion Detection Systems (IDSs) are utilized to screen PC and data frameworks for security shortcomings. IDS adds more effectiveness in securing networks against attacks. This paper presents an IDS model based on Deep Learning (DL) with Convolutional Neural Network (CNN) hypothesis. The model has been evaluated on the NSLKDD dataset. It has been trained by Kddtrain+ and tested twice, once using kddtrain+ and the other using kddtest+. The achieved test accuracies are 99.7% and 98.43% with 0.002 and 0.02 wrong alert rates for the two test scenarios, respectively.
Jianhua, Xing, Jing, Si, Yongjing, Zhang, Wei, Li, Yuning, Zheng.  2021.  Research on Malware Variant Detection Method Based on Deep Neural Network. 2021 IEEE 5th International Conference on Cryptography, Security and Privacy (CSP). :144–147.
To deal with the increasingly serious threat of industrial information malicious code, the simulations and characteristics of the domestic security and controllable operating system and office software were implemented in the virtual sandbox environment based on virtualization technology in this study. Firstly, the serialization detection scheme based on the convolution neural network algorithm was improved. Then, the API sequence was modeled and analyzed by the improved convolution neural network algorithm to excavate more local related information of variant sequences. Finally the variant detection of malicious code was realized. Results showed that this improved method had higher efficiency and accuracy for a large number of malicious code detection, and could be applied to the malicious code detection in security and controllable operating system.
Abdullah, Rezhna M., Abdullah, Syamnd M., Abdullah, Saman M..  2021.  Neighborhood Component Analysis and Artificial Neural Network for DDoS Attack Detection over IoT Networks. 2021 7th International Engineering Conference ``Research Innovation amid Global Pandemic" (IEC). :1–6.
Recently, modern networks have been made up of connections of small devices that have less memory, small CPU capability, and limited resources. Such networks apparently known as Internet of Things networks. Devices in such network promising high standards of live for human, however, they increase the size of threats lead to bring more risks to network security. One of the most popular threats against such networks is known as Distributed Denial of Service (DDoS). Reports from security solution providers show that number of such attacks are in increase considerably. Therefore, more researches on detecting the DDoS attacks are necessary. Such works need monitoring network packets that move over Internet and networks and, through some intelligent techniques, monitored packets could be classified as benign or as DDoS attack. This work focuses on combining Neighborhood Component Analysis and Artificial Neural Network-Backpropagation to classify and identify packets as forward by attackers or as come from authorized and illegible users. This work utilized the activities of four type of the network protocols to distinguish five types of attacks from benign packets. The proposed model shows the ability of classifying packets to normal or to attack classes with an accuracy of 99.4%.
Gong, Jianhu.  2021.  Network Information Security Pipeline Based on Grey Relational Cluster and Neural Networks. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). :971–975.
Network information security pipeline based on the grey relational cluster and neural networks is designed and implemented in this paper. This method is based on the principle that the optimal selected feature set must contain the feature with the highest information entropy gain to the data set category. First, the feature with the largest information gain is selected from all features as the search starting point, and then the sample data set classification mark is fully considered. For the better performance, the neural networks are considered. The network learning ability is directly determined by its complexity. The learning of general complex problems and large sample data will bring about a core dramatic increase in network scale. The proposed model is validated through the simulation.