我国商用密码管理现状与发展对策建议

随着信息技术日新月异的发展,网络环境日益复杂、安全形势瞬息万变,网络安全威胁的范围和内涵不断扩大和演化,我国面临的网络安全挑战日趋严峻。“没有网络安全就没有国家安全”,商用密码技术作为实现网络安全的核心技术,在网络安全防护工作中发挥着重要的基础支撑作用。聚焦我国商用密码管理现状,围绕政策法规、标准规范、技术实力、产业发展等内容进行研究,分析我国商用密码管理工作中的挑战,并提出相应的建议与举措,为商用密码管理工作提供了有益参考。     

洛克希德·马丁公司网络军工产业研究

洛克希德·马丁公司(以下简称"洛马")是美国一家拥有百年历史的国防承包商,在百年发展历程中,洛马以美国国家安全需求为牵引,逐渐成长为世界最大军工集团.伴随网络时代的来临和新安全威胁的涌现,洛马加大网络空间作战技术的研发力度和资金投入,为美国加强网络国防能力建设铸剑护航.研究以洛马网络军工产业发展为脉络,对其网络安全全谱...     

面向拜登政府的过渡期网络安全建议

美国网络空间日光浴委员会为新上任的拜登政府提出的网络安全建议。重点建议拜登政府在上任头100天内组建国家网络总监办公室,制定和颁布国家网络战略,改善当前政府网络安全工作的连贯性和影响力,并进一步加强与私营部门的合作关系。同时阐述了拜登政府此后应优先开展的七项工作,以及白宫和国会应围绕网络安全展开立法合作的六个领域。     

网络安全等级保护在大型水电站的实践

按照《信息安全技术网络安全等级保护基本要求》要求,对某大型水电站电力监控系统安全防护现状做出详细分析后,提出电力监控系统安全防护整改方案。整改方案严格遵守《信息安全技术网络安全等级保护基本要求》《电力监控系统安全防护规定》等重要文件要求,涵盖了水电站主要的电力监控系统。在采取了有针对性的技术措施加强安全防护的同时,完善了水电站电力监控系统安全防护管理的建设。最后,对部分遗留问题进行了初步探讨,以便今后进一步完善电力监控系统安全防护体系建设。     

基于贝叶斯知识追踪的网安人才能力智能化评估方法

近年来,网络空间安全形势日益严峻,导致网络空间安全人才(以下简称网安人才)缺口巨大,国家加快网安人才评估的需求愈加强烈.针对当前网安人才能力评估精准度不足的问题,本文提出了一种改进的贝叶斯知识追踪CT-BKT(Cybersecurity Talents Bayesian Knowledge Tracing)模型,通过网...     

Artificial Algae Optimization with Deep Belief Network Enabled Ransomware Detection in IoT Environment

The Internet of Things (IoT) has gained more popularity in research because of its large-scale challenges and implementation. But security was the main concern when witnessing the fast development in its applications and size. It was a dreary task to independently set security systems in every IoT gadget and upgrade them according to the newer threats. Additionally, machine learning (ML) techniques optimally use a colossal volume of data generated by IoT devices. Deep Learning (DL) related systems were modelled for attack detection in IoT. But the current security systems address restricted attacks and can be utilized outdated datasets for evaluations. This study develops an Artificial Algae Optimization Algorithm with Optimal Deep Belief Network (AAA-ODBN) Enabled Ransomware Detection in an IoT environment. The presented AAA-ODBN technique mainly intends to recognize and categorize ransomware in the IoT environment. The presented AAA-ODBN technique follows a three-stage process: feature selection, classification, and parameter tuning. In the first stage, the AAA-ODBN technique uses AAA based feature selection (AAA-FS) technique to elect feature subsets. Secondly, the AAA-ODBN technique employs the DBN model for ransomware detection. At last, the dragonfly algorithm (DFA) is utilized for the hyperparameter tuning of the DBN technique. A sequence of simulations is implemented to demonstrate the improved performance of the AAA-ODBN algorithm. The experimental values indicate the significant outcome of the AAA-ODBN model over other models.     

内禀安全：网络安全能力体系化构建方法

目前主流的网络安全防护体系是外嵌的,安全体系与业务体系分离,安全产品相互孤立,在防护能力上难以高效应对越来越复杂的网络安全挑战。网络安全从外向内进行强基,势在必行。将网络安全的业务场景归纳为组织、厂商、监管和威胁四方视角,各视角具有不同的业务目标。从四方视角的共性和个性出发,系统性归纳网络安全生态的能力需求,提出内禀安全方法论。内禀安全能力是指ICT组件原生支撑监测、防护和溯源等安全功能的能力。内禀安全能力对网络安全具有基础支撑作用,本身不是最终的安全功能实现,与现有的“内生安全”“内设安全”等方法论所针对的问题不同。内禀安全强调网络组件内在的安全赋能禀赋,有两种方式可以发掘这种禀赋,一是通过先天安全能力激活,二是外嵌能力内化,对外在逻辑上表现出自体免疫。此类组件的优势之一在于业务与安全的内聚,能够透明化感知安全态势、定制化配置安全策略、贴身化执行安全保护;优势之二在于将业务功能与安全功能进行合并封装,简化整体工程架构,降低网络管理复杂度。进一步提出了内禀安全支撑能力框架,对符合内禀安全理念的安全能力进行归纳和枚举,将安全支撑能力分为采集、认知、执行、协同和弹复5类,并进一步介绍各类能力的子类型和基础ICT技术。基于该框架,介绍了典型安全业务场景在内禀安全理念下的增强实现。     

基于层次分析法的犹豫模糊语言包络分析模型及其在边缘节点网络安全评估中的应用

针对传统的网络安全评估方法仅用单一语言术语作为偏好信息难以评估复杂网络环境的安全性的问题,利用犹豫模糊语言术语集作为评估网络安全的偏好信息,提出了一种基于层次分析法(analytic hierarchy process,AHP)的犹豫模糊语言包络分析模型,用以评估边缘节点的网络安全性。该模型通过求解目标规划模型获得最优的网络安全准则权重信息,进一步构建网络安全准则权重信息对应的AHP约束锥作为犹豫模糊语言包络分析模型的约束条件,获得移动用户安全评估的排序结果。实例证明该模型能够合理地融合网络安全准则偏好信息,并有效地处理边缘节点的网络安全评估问题。     

高校网络安全建设的探索与思考

The construction of cybersecurity is an important part of the information construction in university. It is an indispensablecondition to help the information construction and a strong guarantee to serve the information support of teachers and students in uni-versity. The cybersecurity environment, atmosphere and guarantee ability of university need to be improved. Based on the relatedwork and achievements of cybersecurity construction, some general schemes are put forward to provide reference for cybersecurityconstruction in university.     

Security measure allocation for industrial control systems: Exploiting systematic search techniques and submodularity

To protect industrial control systems from cyberattacks, multiple layers of security measures need to be allocated to prevent critical security vulnerabilities. However, both finding the critical vulnerabilities and then allocating security measures in a cost‐efficient way become challenging when the number of vulnerabilities and measures is large. This paper proposes a framework that can be used once this is the case. In our framework, the attacker exploits security vulnerabilities to gain control over some of the sensors and actuators. The critical vulnerabilities are those that are not complex to exploit and can lead to a large impact on the physical world through the compromised sensors and actuators. To find these vulnerabilities efficiently, we propose an algorithm that uses the nondecreasing properties of the impact and complexity functions and properties of the security measure allocation problem to speed up the search. Once the critical vulnerabilities are located, the security measure allocation problem reduces to an integer linear program. Since integer linear programs are NP‐hard in general, we reformulate this problem as a problem of minimizing a linear set function subject to a submodular constraint. A polynomial time greedy algorithm can then be applied to obtain a solution with guaranteed approximation bound. The applicability of our framework is demonstrated on a control system used for regulation of temperature within a building.