博文
DIKWP as a Semantic Firewall for the Cyberspace of LLM
|
Traditional Invention and Innovation Theory 1946-TRIZ Does Not Adapt to the Digital Era
-Innovative problem-solving methods combining DIKWP model and classic TRIZ
Purpose driven Integration of data, information, knowledge, and wisdom Invention and creation methods: DIKWP-TRIZ
(Chinese people's own original invention and creation methods:DIKWP - TRIZ)
DIKWP as a Semantic Firewall for the Cyberspace of LLM
Prof. Yucong Duan
Benefactor: 
Shiming Gong
DIKWP-AC Artificial Consciousness Laboratory
AGI-AIGC-GPT Evaluation DIKWP (Global) Laboratory
(Email：duanyucong@hotmail.com)
Catalogue
3 The role of DIKWP as a semantic firewall
4 The concrete application DIKWP in network firewall
5 Specific simulation case analysis
5.3 Intelligent response mechanism
6 Challenges and Prospects of Semantic Firewall
In this paper, the application and innovation of DIKWP model in constructing network semantic firewall are discussed comprehensively. DIKWP model provides a comprehensive cognitive processing framework by integrating data (D), information (I), knowledge (K), wisdom (W) and purpose (P). This paper expounds in detail the role of DIKWP model in identifying and defending network attacks based on complex semantics and purposes, including advanced threat detection, dynamic security strategy, intelligent response mechanism and deep behavior analysis. Through the analysis of specific simulation cases, this paper shows how the DIKWP model can improve the network security level and enhance the adaptability and flexibility of the network system. At the same time, the paper also discusses the technical challenges and future prospects in realizing deep semantic analysis and purpose recognition technology.
In the digital age, network security has become an important concern. The traditional network firewall mainly focuses on data security, but with the complexity of network attacks, a deeper protection mechanism is needed. DIKWP (Data, Information, Knowledge, Wisdom, purpose) model provides innovative ideas and methods for building a network semantic firewall with its comprehensive cognitive processing framework.
DIKWP model provides a comprehensive cognitive processing framework by integrating data, information, knowledge, wisdom and purpose. In the field of network security, this model can not only identify and analyze threats at the data level, but also understand and prevent attacks based on complex semantics and purposes.
3 The role of DIKWP as a semantic firewall
Data layer (d) application:
Responsible for collecting and analyzing network traffic data and identifying potential threats and abnormal behaviors.
Screen and verify the network access data to ensure the reliability and integrity of the data source.
Information layer (I) application:
Further analyze the collected information and extract key safety warnings and patterns.
Identify abnormal behaviors and potential security vulnerabilities from massive data.
Knowledge layer (k) application:
Combine historical data and security knowledge base to provide in-depth threat analysis and prediction.
Use professional knowledge to support network security strategy.
Wisdom layer (w) application:
Use advanced analysis technology to predict complex threats and formulate response strategies.
Based on comprehensive analysis, innovative solutions and preventive measures are put forward.
purpose layer (p) application:
Predict and prevent advanced persistent threats (APT) and complex network attacks based on in-depth purpose analysis.
Clear the goal and expected effect of consensus mechanism, and formulate targeted network security strategies.
4 The concrete application DIKWP in network firewall
Advanced threat detection:
Use DIKWP model to deeply analyze network behavior and identify non-traditional and complex security threats.
Monitor network activities in real time and respond to abnormal events quickly.
Dynamic security policy:
Based on real-time data and situational analysis, the network security strategy is dynamically adjusted to deal with emerging threats.
Personalized security strategy, formulate effective defense measures for specific situations.
Intelligent response mechanism:
When a threat is detected, predetermined response measures are automatically started, such as isolating attacks and blocking malicious traffic.
Pre-set emergency plan to ensure quick and effective crisis handling.
Deep behavior analysis:
By deeply understanding the semantics and purposes of network activities, the development trend of potential attacks is predicted.
Analyze user behavior patterns, identify unusual activities, and prevent internal threats.
5 Specific simulation case analysis
Case scenario:
Abnormal data traffic was found in the internal network of a large enterprise. Through the DIKWP model, the network security team immediately started in-depth analysis.
Analysis shows that there are a series of seemingly normal but actually abnormal data requests, suggesting potential network intrusion.
DIKWP application:
Data layer (D): Collect network traffic data, including IP address, access time and data volume.
Information layer (I): Analyze data traffic patterns and identify abnormal patterns that are inconsistent with usual.
Knowledge layer (K): Compared with historical attack cases, it is confirmed that these abnormal patterns may point to network attacks.
Wisdom layer (W): considering the data sensitivity of the enterprise, start the early warning system immediately.
purpose layer (P): Based on the analysis results, the purpose layer determines the defensive measures to prevent data leakage.
Results:
Potential network attacks were discovered and prevented in time, and the data security of enterprises was protected.
Case scenario:
Financial institutions are facing persistent cyber threats. In order to deal with these threats, the organization has adopted a dynamic security strategy based on DIKWP model.
DIKWP application:
Data layer (D): Collect and analyze transaction data and network access records regularly.
Information layer (I): Identify abnormal transactions and potential fraud.
Knowledge layer (K): Use financial security knowledge to evaluate and predict threat patterns.
Wisdom layer (W): Develop defensive measures including strengthening customer verification.
purpose layer (P): The goal is to protect the network security of customer assets and institutions.
Results:
Flexible adjustment of security policies has successfully prevented a series of network frauds and attacks.
5.3 Intelligent response mechanism
Case scenario:
A university network system was attacked by ransomware. DIKWP model is used to make intelligent response plan.
DIKWP application:
Data layer (D): collect data of the affected system quickly.
Information layer (I): analyze the spread mode and influence scope of software.
Knowledge layer (K): According to the previous knowledge of similar attacks, quickly formulate response measures.
Wisdom layer (W): consider the long-term impact and make a recovery plan.
purpose layer (P): The goal is to minimize losses and restore normal operations.
Results:
Effectively isolated and controlled the spread of ransomware and minimized losses.
Case scenario:
A technology company found that its database was frequently subjected to unauthorized access. To this end, the company uses DIKWP model to conduct in-depth behavior analysis.
DIKWP application:
Data layer (D): Collect user behavior data when accessing the database.
Information layer (I): Identify unauthorized users and abnormal access patterns.
Knowledge layer (K): Analyze potential internal threats by using network security knowledge.
Wisdom layer (W): comprehensively analyze and formulate long-term security strategies.
purpose layer (P): The purpose is to prevent data leakage and improve internal security awareness.
Results:
Internal security threats are identified and prevented, and the security of the whole network system is enhanced.
Through these cases, we can see that the application of DIKWP model in network firewall not only improves the security level, but also enhances the adaptability and flexibility of network system.
6 Challenges and Prospects of Semantic Firewall
Technical challenges:
Realizing deep semantic analysis and purpose recognition technology is the main challenge.
Continuously optimize the algorithm and model to improve the prediction accuracy.
Real-time requirements:
Semantic firewall needs fast response ability, real-time processing of a large number of data and complex situation analysis.
Strengthen computing resources and network infrastructure to ensure efficient data processing.
Continuous learning and adaptation:
With the constant change of network environment and attack means, semantic firewall needs to constantly learn and adapt to new security threats.
Introduce machine learning and artificial intelligence technology to constantly update security policies.
As a network semantic firewall, DIKWP model provides a comprehensive and in-depth network security solution. It can effectively deal with increasingly complex and changeable network threats, which not only enhances the security of the network, but also improves the flexibility and adaptability of the defense mechanism. With the continuous progress of technology, the application of DIKWP model in the field of network security will continue to expand and deepen, providing more reliable and intelligent protection for the future network environment.
本文全面探讨了DIKWP模型在构建网络语义防火墙方面的应用与创新。DIKWP模型通过整合数据（D）、信息（I）、知识（K）、智慧（W）和意图（P）的综合分析，提供了一种全面的认知处理框架。文章详细阐述了DIKWP模型在识别和防御基于复杂语义和意图的网络攻击中的角色，包括高级威胁检测、动态安全策略、智能响应机制和深度行为分析。通过具体的模拟案例分析，本文展示了DIKWP模型如何提高网络安全水平，增强网络系统的适应性和灵活性。同时，文章也讨论了在实现深层次语义分析和意图识别技术方面的技术挑战及未来展望。
在数字化时代，网络安全已成为重要的关注点。传统的网络防火墙主要集中在数据安全层面，但随着网络攻击的复杂化，需要一种更深层次的保护机制。DIKWP（数据、信息、知识、智慧、意图）模型以其全面的认知处理框架，为构建网络语义防火墙提供了创新的思路和方法。
DIKWP模型通过整合数据、信息、知识、智慧和意图的综合分析，提供了一种全面的认知处理框架。在网络安全领域，这种模型不仅能识别和分析数据层面的威胁，还能理解和预防基于复杂语义和意图的攻击。
数据层（D）应用：
负责收集和分析网络流量数据，识别潜在的威胁和异常行为。
筛选和验证入网数据，确保数据源的可靠性和完整性。
信息层（I）应用：
进一步分析收集的信息，提取关键安全警示和模式。
从海量数据中识别异常行为和潜在的安全漏洞。
知识层（K）应用：
结合历史数据和安全知识库，提供深入的威胁分析和预测。
利用专业知识，为网络安全策略提供支持。
智慧层（W）应用：
利用先进的分析技术，进行复杂威胁预测和响应策略制定。
基于全面的分析，提出创新的解决方案和预防措施。
意图层（P）应用：
基于深入的意图分析，预测和阻止高级持续威胁（APT）和复杂网络攻击。
明确共识机制的目标和预期效果，制定针对性的网络安全策略。
高级威胁检测：
利用DIKWP模型深入分析网络行为，识别非传统和复杂的安全威胁。
实时监控网络活动，快速响应异常事件。
动态安全策略：
基于实时数据和情境分析，动态调整网络安全策略以应对新兴威胁。
个性化安全策略，针对特定情况制定有效的防御措施。
智能响应机制：
在检测到威胁时，自动启动预定的响应措施，例如隔离攻击、阻断恶意流量。
预设应急计划，确保快速有效的危机处理。
深度行为分析：
通过深入理解网络活动的语义和意图，预测潜在攻击的发展趋势。
分析用户行为模式，识别不寻常的活动，预防内部威胁。
案例情景：
一家大型企业的内部网络发现异常数据流量。通过DIKWP模型，网络安全团队立即启动深度分析。
分析显示，存在一系列看似正常但实际异常的数据请求，暗示潜在的网络入侵。
DIKWP应用：
数据层（D）：收集网络流量数据，包括IP地址、访问时间和数据量。
信息层（I）：分析数据流量模式，识别出与平常不符的异常模式。
知识层（K）：对比历史攻击案例，确认这些异常模式可能指向网络攻击。
智慧层（W）：考虑到企业的数据敏感性，立即启动预警系统。
意图层（P）：基于分析结果，意图层确定防御措施，以防止数据泄露。
结果：
及时发现并阻止了潜在的网络攻击，保护了企业的数据安全。
案例情景：
金融机构面临着持续的网络威胁。为了应对这些威胁，机构采用了基于DIKWP模型的动态安全策略。
DIKWP应用：
数据层（D）：定期收集和分析交易数据和网络访问记录。
信息层（I）：识别交易异常和潜在的欺诈行为。
知识层（K）：利用金融安全知识，评估和预测威胁模式。
智慧层（W）：制定包括加强客户验证在内的防御措施。
意图层（P）：目标是保护客户资产和机构的网络安全。
结果：
灵活调整安全策略，成功防止了一系列网络欺诈和攻击。
案例情景：
一所大学网络系统遭受勒索软件攻击。DIKWP模型被用于制定智能响应计划。
DIKWP应用：
数据层（D）：迅速收集被影响系统的数据。
信息层（I）：分析软件的传播方式和影响范围。
知识层（K）：根据先前类似攻击的知识，快速制定响应措施。
智慧层（W）：考虑长期影响，制定恢复计划。
意图层（P）：目标是最小化损失并恢复正常运营。
结果：
有效隔离和控制了勒索软件的传播，最小化了损失。
案例情景：
一家科技公司发现其数据库频繁遭受未授权访问。为此，公司利用DIKWP模型进行深度行为分析。
DIKWP应用：
数据层（D）：收集访问数据库的用户行为数据。
信息层（I）：识别非授权用户和异常访问模式。
知识层（K）：利用网络安全知识，分析潜在的内部威胁。
智慧层（W）：综合分析和制定长期的安全策略。
意图层（P）：目的是防止数据泄露并提高内部安全意识。
结果：
识别并阻止了内部安全威胁，增强了整个网络系统的安全性。
通过这些案例，我们可以看到DIKWP模型在网络防火墙中的应用不仅提高了安全水平，还增强了网络系统的适应性和灵活性。
技术挑战：
实现深层次的语义分析和意图识别技术是主要挑战。
持续优化算法和模型，提高预测准确性。
实时性要求：
语义防火墙需要快速响应能力，实时处理大量数据和复杂的情境分析。
加强计算资源和网络基础设施，确保高效的数据处理。
持续学习与适应：
随着网络环境和攻击手段的不断变化，语义防火墙需要不断学习和适应新的安全威胁。
引入机器学习和人工智能技术，不断更新安全策略。
DIKWP模型作为网络语义防火墙，提供了一个全面和深入的网络安全解决方案。它能够有效应对日益复杂和多变的网络威胁，不仅增强了网络的安全性，也提高了防御机制的灵活性和适应性。随着技术的不断进步，DIKWP模型在网络安全领域的应用将不断扩展和深化，为未来的网络环境提供更加可靠和智能的保护。
[1] Duan Y. Which characteristic does GPT-4 belong to? An analysis through DIKWP model. DOI: 10.13140/RG.2.2.25042.53447. https://www.researchgate.net/publication/375597900_Which_characteristic_does_GPT-4_belong_to_An_analysis_through_DIKWP_model_GPT-4_shishenmexinggeDIKWP_moxingfenxibaogao. 2023.
[2] Duan Y. DIKWP Processing Report on Five Personality Traits. DOI: 10.13140/RG.2.2.35738.00965. https://www.researchgate.net/publication/375597092_wudaxinggetezhide_DIKWP_chulibaogao_duanyucongYucong_Duan. 2023.
[3] Duan Y. Research on the Application of DIKWP Model in Automatic Classification of Five Personality Traits. DOI: 10.13140/RG.2.2.15605.35047. https://www.researchgate.net/publication/375597087_DIKWP_moxingzaiwudaxinggetezhizidongfenleizhongdeyingyongyanjiu_duanyucongYucong_Duan. 2023.
[4] Duan Y, Gong S. DIKWP-TRIZ method: an innovative problem-solving method that combines the DIKWP model and classic TRIZ. DOI: 10.13140/RG.2.2.12020.53120. https://www.researchgate.net/publication/375380084_DIKWP-TRIZfangfazongheDIKWPmoxinghejingdianTRIZdechuangxinwentijiejuefangfa. 2023.
[5] Duan Y. The Technological Prospects of Natural Language Programming in Large-scale AI Models: Implementation Based on DIKWP. DOI: 10.13140/RG.2.2.19207.57762. https://www.researchgate.net/publication/374585374_The_Technological_Prospects_of_Natural_Language_Programming_in_Large-scale_AI_Models_Implementation_Based_on_DIKWP_duanyucongYucong_Duan. 2023.
[6] Duan Y. The Technological Prospects of Natural Language Programming in Large-scale AI Models: Implementation Based on DIKWP. DOI: 10.13140/RG.2.2.19207.57762. https://www.researchgate.net/publication/374585374_The_Technological_Prospects_of_Natural_Language_Programming_in_Large-scale_AI_Models_Implementation_Based_on_DIKWP_duanyucongYucong_Duan. 2023.
[7] Duan Y. Exploring GPT-4, Bias, and its Association with the DIKWP Model. DOI: 10.13140/RG.2.2.11687.32161. https://www.researchgate.net/publication/374420003_tantaoGPT-4pianjianjiqiyuDIKWPmoxingdeguanlian_Exploring_GPT-4_Bias_and_its_Association_with_the_DIKWP_Model. 2023.
[8] Duan Y. DIKWP language: a semantic bridge connecting humans and AI. DOI: 10.13140/RG.2.2.16464.89602. https://www.researchgate.net/publication/374385889_DIKWP_yuyanlianjierenleiyu_AI_deyuyiqiaoliang. 2023.
[9] Duan Y. The DIKWP artificial consciousness of the DIKWP automaton method displays the corresponding processing process at the level of word and word granularity. DOI: 10.13140/RG.2.2.13773.00483. https://www.researchgate.net/publication/374267176_DIKWP_rengongyishide_DIKWP_zidongjifangshiyiziciliducengjizhanxianduiyingdechuliguocheng. 2023.
[10] Duan Y. Implementation and Application of Artificial wisdom in DIKWP Model: Exploring a Deep Framework from Data to Decision Making. DOI: 10.13140/RG.2.2.33276.51847. https://www.researchgate.net/publication/374266065_rengongzhinengzai_DIKWP_moxingzhongdeshixianyuyingyongtansuocongshujudaojuecedeshendukuangjia_duanyucongYucong_Duan. 2023.
Data can be regarded as a concrete manifestation of the same semantics in our cognition. Often, Data represents the semantic confirmation of the existence of a specific fact or observation, and is recognised as the same object or concept by corresponding to some of the same semantic correspondences contained in the existential nature of the cognitive subject's pre-existing cognitive objects. When dealing with data, we often seek and extract the particular identical semantics that labels that data, and then unify them as an identical concept based on the corresponding identical semantics. For example, when we see a flock of sheep, although each sheep may be slightly different in terms of size, colour, gender, etc., we will classify them into the concept of "sheep" because they share our semantic understanding of the concept of "sheep". The same semantics can be specific, for example, when identifying an arm, we can confirm that a silicone arm is an arm based on the same semantics as a human arm, such as the same number of fingers, the same colour, the same arm shape, etc., or we can determine that the silicone arm is not an arm because it doesn't have the same semantics as a real arm, which is defined by the definition of "can be rotated". It is also possible to determine that the silicone arm is not an arm because it does not have the same semantics as a real arm, such as "rotatable".
Information, on the other hand, corresponds to the expression of different semantics in cognition. Typically, Information refers to the creation of new semantic associations by linking cognitive DIKWP objects with data, information, knowledge, wisdom, or purposes already cognised by the cognising subject through a specific purpose. When processing information, we identify the differences in the DIKWP objects they are cognised with, corresponding to different semantics, and classify the information according to the input data, information, knowledge, wisdom or purpose. For example, in a car park, although all cars can be classified under the notion of 'car', each car's parking location, time of parking, wear and tear, owner, functionality, payment history and experience all represent different semantics in the information. The different semantics of the information are often present in the cognition of the cognitive subject and are often not explicitly expressed. For example, a depressed person may use the term "depressed" to express the decline of his current mood relative to his previous mood, but this "depressed" is not the same as the corresponding information because its contrasting state is not the same as the corresponding information. However, the corresponding information cannot be objectively perceived by the listener because the contrasting state is not known to the listener, and thus becomes the patient's own subjective cognitive information.
Knowledge corresponds to the complete semantics in cognition. Knowledge is the understanding and explanation of the world acquired through observation and learning. In processing knowledge, we abstract at least one concept or schema that corresponds to a complete semantics through observation and learning. For example, we learn that all swans are white through observation, which is a complete knowledge of the concept "all swans are white" that we have gathered through a large amount of information.
Wisdom corresponds to information in the perspective of ethics, social morality, human nature, etc., a kind of extreme values from the culture, human social groups relative to the current era fixed or individual cognitive values. When dealing with Wisdom, we integrate this data, information, knowledge, and wisdom and use them to guide decision-making. For example, when faced with a decision-making problem, we integrate various perspectives such as ethics, morality, and feasibility, not just technology or efficiency.
Purpose can be viewed as a dichotomy (input, output), where both input and output are elements of data, information, knowledge, wisdom, or purpose. Purpose represents our understanding of a phenomenon or problem (input) and the goal we wish to achieve by processing and solving that phenomenon or problem (output). When processing purposes, the AI system processes the inputs according to its predefined goals (outputs), and gradually brings the outputs closer to the predefined goals by learning and adapting.
Yucong Duan, male, currently serves as a member of the Academic Committee of the School of Computer Science and Technology at Hainan University. He is a professor and doctoral supervisor and is one of the first batch of talents selected into the South China Sea Masters Program of Hainan Province and the leading talents in Hainan Province. He graduated from the Software Research Institute of the Chinese Academy of Sciences in 2006, and has successively worked and visited Tsinghua University, Capital Medical University, POSCO University of Technology in South Korea, National Academy of Sciences of France, Charles University in Prague, Czech Republic, Milan Bicka University in Italy, Missouri State University in the United States, etc. He is currently a member of the Academic Committee of the School of Computer Science and Technology at Hainan University and he is the leader of the DIKWP (Data, Information, Knowledge, Wisdom, Purpose) Innovation Team at Hainan University, Distinguished Researcher at Chongqing Police College, Leader of Hainan Provincial Committee's "Double Hundred Talent" Team, Vice President of Hainan Invention Association, Vice President of Hainan Intellectual Property Association, Vice President of Hainan Low Carbon Economy Development Promotion Association, Vice President of Hainan Agricultural Products Processing Enterprises Association, Visiting Fellow, Central Michigan University, Member of the Doctoral Steering Committee of the University of Modena. Since being introduced to Hainan University as a D-class talent in 2012, He has published over 260 papers, included more than 120 SCI citations, and 11 ESI citations, with a citation count of over 4300. He has designed 241 serialized Chinese national and international invention patents (including 15 PCT invention patents) for multiple industries and fields and has been granted 85 Chinese national and international invention patents as the first inventor. Received the third prize for Wu Wenjun's artificial intelligence technology invention in 2020; In 2021, as the Chairman of the Program Committee, independently initiated the first International Conference on Data, Information, Knowledge and Wisdom - IEEE DIKW 2021; Served as the Chairman of the IEEE DIKW 2022 Conference Steering Committee in 2022; Served as the Chairman of the IEEE DIKW 2023 Conference in 2023. He was named the most beautiful technology worker in Hainan Province in 2022 (and was promoted nationwide); In 2022 and 2023, he was consecutively selected for the "Lifetime Scientific Influence Ranking" of the top 2% of global scientists released by Stanford University in the United States. Participated in the development of 2 international standards for IEEE financial knowledge graph and 4 industry knowledge graph standards. Initiated and co hosted the first International Congress on Artificial Consciousness (AC2023) in 2023.
Prof. Yucong Duan
DIKWP-AC Artificial Consciousness Laboratory
AGI-AIGC-GPT Evaluation DIKWP (Global) Laboratory
DIKWP research group, Hainan University
duanyucong@hotmail.com
https://blog.sciencenet.cn/blog-3429562-1414195.html
上一篇：语义数学在科学和技术中的应用扩展
下一篇：量子计算的基础及新语义数学的应用
