网安专业创新实践人才的培养路径与建设机制

网络空间安全不仅涉及国家政治、军事和经济各个方面,而且影响国家的安全和主权,其重要性不言而喻.面对网安人才需求紧迫的现实情况,国内诸多高校陆续开展了网安专业人才的培养.然而,由于各个方面的原因,其培养方法与机制仍存在诸多不足.首先,从现有安全领域的背景出发,分析了国内外网络空间安全专业人才培养的特征.其次,对现有高校网...     

面向网络空间安全专业的汇编语言课程改革

为了提升网络空间安全专业人才培养质量,在剖析该专业汇编语言课程特点的基础上,对课程内容、实践设计与教学方法提出针对性的教学改革方案,从而更好地契合人才培养需求,提升人才培养的质量,为网安专业的整体课程体系提供服务支撑。     

网络空间安全人才培养探讨

网络空间的竞争,归根结底是人才的竞争。当前,世界各国高度重视网络空间安全人才的培养。基于此,首先介绍了网络空间安全人才培养的现状;然后分析了网络空间安全人才的需求特点,并提出了网络空间安全人才培养的若干建议措施;最后给出了暨南大学在网络空间安全人才培养方面的一些探索。     

基层网安部门网络舆情管控的现状分析与对策研究——以S省Z市网安大队为例

网络社会的快速发展让人们充分感受到了生活的便捷和科技的进步，但同时也给社会治理带来了新的挑战，尤其是网络舆情事件的发生，严重扰乱了我国社会公共秩序和安全稳定。因此，公安机关网络安全部门作为网络空间安全“守护者”，需不断加强网络舆情管控力度，有效维护网络空间良好秩序。基于此，本文以S省Z市为例，通过对该市网安大队进行实地调研，充分了解当前基层网安部门对于网络舆情的管控现状，发现在网络舆情管控方面存在的不足，并对此提出相应的对策和建议。     

3G原则做安全管家

3月29日获悉,威视科技推出了“NSM(networksecurity manager,网络安全管家)”,从安全策略、产品技术、技术服务等方面,帮助信息技术主管及信息安全主管解决企业网络安全问题。“NSM网安管家”倡导为客户度身构建符合3G原则(Gate门,Guard警卫,Gun武器)的网络安全防御体系。“NSM网安管家”针对网络安全的“服务生命周期”,也就是从需求分析、项目评估,到方案设计、产品选型和项目实施及评测认证等提出。根据风险分析与评估的结果提取客观的安全需求;然后根据设计出的方案,依照项目管理方法和工程能力成熟度模型的规范去进行工程实施;最…     

网络安全实验示范班人才培养模式探索

分析网络空间安全人才培养过程中面临的问题,提出构建网络安全实验示范班的培养模式,介绍网络空间安全实验示范班的人才选拔考核机制和标准。在此基础上,围绕网络空间安全学科的人才培养目标,从多个方面阐述创新网络空间安全人才培养模式的方案,并给出面向网络空间安全实验示范班的人才激励与竞争机制。     

网络空间安全

<正>月刊2010年创刊总第110期2019年4月第10卷第4期《网络空间安全》的办刊宗旨是面向网络空间安全领域,展现高水平专业技术成果和最新推广应用成果,传播网络空间安全领域的新理论和新技术,创建中国网络空间安全领域有影响力的学术交流平台,以期提高我国网络空间安全技术水平和管理能力。《网络空间安全》主要涉及内容包括数据安全与治理、网络与系统安     

网络空间安全

<正>月刊2010年创刊总第108期2019年2月第10卷第2期《网络空间安全》的办刊宗旨是面向网络空间安全领域,展现高水平专业技术成果和最新推广应用成果,传播网络空间安全领域的新理论和新技术,创建中国网络空间安全领域有影响力的学术交流平台,以期提高我国网络空间安全技术水平和管理能力。《网络空间安全》主要涉及内容包括数据安全与治理、网络与系统安     

网络空间安全学科人才培养模式与途径探索

针对网络空间安全一级学科刚设立的现实,围绕网络空间安全学科人才培养过程中存在的不足,分析网络空间安全学科人才培养问题的根源。结合我国网络空间安全学科的建设目标和实际需求,指出如何培养服务国家安全战略的网络空间安全人才已成为当前我国高等工程教育的一个新课题,在此基础上,有针对性地提出一系列较为切实可行的网络空间安全学科人才模式及途径。     

赛宁网安获A轮融资发布网络靶场平台及攻防世界产品

近日,赛宁网安宣布,获得360集团、天融信科技联合A轮投资数千万元。同时还发布了两款自主研发的新产品——网络靶场平台及攻防世界。赛宁网安联合创始人兼CEO谢峥表示,融资是业内对赛宁网安业务模式和成长潜力的认可,本轮融资后,今后公司发展的重点都将会围绕新发布的两款产品进行落地。赛宁网安主营业务为网络安全实战培训、网络安全服务,其打造了业界知名的国际网络安全攻防联赛XCTF,一直为网络安全人才的发掘和培养而努力。     

基于残差空洞卷积神经网络的网络安全实体识别方法

近年来,网络安全威胁日益增多,数据驱动的安全智能分析成为网络安全领域研究的热点。特别是以知识图谱为代表的人工智能技术可为多源异构威胁情报数据中的复杂网络攻击检测和未知网络攻击检测提供支撑。网络安全实体识别是威胁情报知识图谱构建的基础。开放网络文本数据中的安全实体构成非常复杂,导致传统的深度学习方法难以准确识别。在BERT（pre-training of deep bidirectional transformers）预训练语言模型的基础上,提出一种基于残差空洞卷积神经网络和条件随机场的网络安全实体识别模型 BERT-RDCNN-CRF。通过BERT模型训练字符级特征向量表示,结合残差卷积与空洞神经网络模型有效提取安全实体的重要特征,最后通过CRF获得每一个字符的BIO标注。在所构建的大规模网络安全实体标注数据集上的实验表明,所提方法取得了比LSTM-CRF模型、BiLSTM-CRF模型和传统的实体识别模型更好的效果。     

Drawing the Line

Cybersecurity research and development raises questions of ethics in many areas. The essay lists several areas where ethics considerations may confront researchers and practitioners and considers in more detail the ethics of data collection and human subjects. It proposes three actions: security researchers should inform themselves about the ethics of human subject experimentation, Institutional Review Boards need to expand their expertise so that they can properly review cybersecurity research proposals, and professional societies should begin developing appropriate ethical guidance for their members who must confront these issues.     

探索“网安医生”人才培养模式

网络安全的竞争归根结底是人才的竞争，世界各国纷纷将网络安全人才培养放在了国家战略高度。网络空间安全学科作为一门新兴交叉学科，其学科的内涵外延还在更新发展，目前缺乏适合网络空间安全人才特点的培养模式及评价机制。根据多年的教学研究，参考国外的先进培养模式及评价机制，结合国内实际情况，提出适合网络空间安全学科特点的“网安医生”培养模式，该模式覆盖本科培养的全生命周期，以问题为导向，培养学生针对网络空间发现和解决安全问题的能力，高校与产业界、科研机构紧密互动构建人才培养的生态环境。以此提高学生掌握知识、技能和应用能力，培养学生探索最新前沿科技和核心技术的兴趣和能力，提升师资力量和教育水平，同时也有利于企业选拔优秀人才，提升企业研发能力和市场竞争力。最终实现合作方协同创新，优势互补，全面共赢的良好局面，促进网络空间安全人才培养和学科发展。     

The emerging EU framework on cybersecurity certification

The emerging EU cybersecurity certification framework proposed by the European Commission by means of the Cybersecurity Act seeks to complement the efforts of the EU Member States in the area of certification of products and services. While EU Member States retain full operational capacity in the aftermath of this proposal, a European Cybersecurity Coordination Group undertakes the governance of the certification framework and ENISA assumes a role in the drawing up of certification schemes. The expectation that the EU certification framework build upon is that it will enhance the ability of the EU to compete in the global cyber-security arena and improve security conditions for consumers and businesses in the EU as a whole.     

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。     

The Five Pillars of a Cognitive Risk Framework—Part II

The purpose of this study is to develop the first cognitive risk framework for cybersecurity to address two narrative arcs in cyber warfare: the rise of the “hacker” as an industry and the “cybersecurity paradox”, namely why billions spent on cybersecurity fail to address semantic cyberattacks. Semantic cyberattacks, also known as social engineering, manipulates human users’ perceptions and interpretation of computer-generated data to obtain non-public confidential data. The cyber battleground has shifted from an attack on hard assets to a much softer target: the human mind. If human behavior is the new and last “weakest link” in the cybersecurity armor, is it possible to build cognitive defenses at the intersection of human-machine interactions? The answer is yes, but the change that is needed requires a new way of thinking about security, data governance and strategy. The concepts referenced in the Cognitive Risk Framework for Cybersecurity (CRFC) are drawn from a large body of research in multidisciplinary topics. Cognitive risk management is a sister discipline of a parallel body of science called Cognitive Informatics Security or CogSec. It is also important to point out as the creator of the CRFC, the principles and practices prescribed herein are borrowed from cognitive informatics security, machine learning, artificial intelligence (AI), and behavioral and cognitive science, among just a few that are still evolving. The Cognitive Risk Framework for Cybersecurity revolves around five pillars: Intentional Controls Design, Cognitive Informatics Security, Cognitive Risk Governance, Cybersecurity Intelligence and Active Defense Strategies and Legal “Best Efforts” considerations in Cyberspace. Complete text of “Cognitive Hack: The New Battleground in Cybersecurity … the Human Mind” is available here: https://www.crcpress.com/Cognitive-Hack-The-New-Battleground-in-Cybersecurity–the-Human-Mind/Bone/p/book/9781498749817     

Machine Learning Based Cybersecurity Threat Detection for Secure IoT Assisted Cloud Environment

The Internet of Things (IoT) is determine enormous economic openings for industries and allow stimulating innovation which obtain between domains in childcare for eldercare, in health service to energy, and in developed to transport. Cybersecurity develops a difficult problem in IoT platform whereas the presence of cyber-attack requires that solved. The progress of automatic devices for cyber-attack classifier and detection employing Artificial Intelligence (AI) and Machine Learning (ML) devices are crucial fact to realize security in IoT platform. It can be required for minimizing the issues of security based on IoT devices efficiently. Thus, this research proposal establishes novel mayfly optimized with Regularized Extreme Learning Machine technique called as MFO-RELM model for Cybersecurity Threat classification and detection from the cloud and IoT environments. The proposed MFO-RELM model provides the effective detection of cybersecurity threat which occur in the cloud and IoT platforms. To accomplish this, the MFO-RELM technique pre-processed the actual cloud and IoT data as to meaningful format. Besides, the proposed models will receive the pre-processing data and carry out the classifier method. For boosting the efficiency of the proposed models, the MFO technique was utilized to it. The experiential outcome of the proposed technique was tested utilizing the standard CICIDS 2017 dataset, and the outcomes are examined under distinct aspects.     

Enhanced Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection

Recent developments in computer networks and Internet of Things (IoT) have enabled easy access to data. But the government and business sectors face several difficulties in resolving cybersecurity network issues, like novel attacks, hackers, internet criminals, and so on. Presently, malware attacks and software piracy pose serious risks in compromising the security of IoT. They can steal confidential data which results in financial and reputational losses. The advent of machine learning (ML) and deep learning (DL) models has been employed to accomplish security in the IoT cloud environment. This article presents an Enhanced Artificial Gorilla Troops Optimizer with Deep Learning Enabled Cybersecurity Threat Detection (EAGTODL-CTD) in IoT Cloud Networks. The presented EAGTODL-CTD model encompasses the identification of the threats in the IoT cloud environment. The proposed EAGTODL-CTD model mainly focuses on the conversion of input binary files to color images, where the malware can be detected using an image classification problem. The EAGTODL-CTD model pre-processes the input data to transform to a compatible format. For threat detection and classification, cascaded gated recurrent unit (CGRU) model is exploited to determine class labels. Finally, EAGTO approach is employed as a hyperparameter optimizer to tune the CGRU parameters, showing the novelty of our work. The performance evaluation of the EAGTODL-CTD model is assessed on a dataset comprising two class labels namely malignant and benign. The experimental values reported the supremacy of the EAGTODL-CTD model with increased accuracy of 99.47%.     

基于不完全信息博弈模型的信息系统安全风险评估方法

博弈理论具有的目标对立性、关系非合作性和策略依存性等特征与网络攻防对抗过程保持一致，将博弈理论应用于网络信息安全已经成为研究热点，但目前已有的研究成果大都采用完全信息博弈模型，与网络攻防实际不符。基于此，为提高信息系统风险评估的准确性，本文构建不完全信息条件下的静态贝叶斯攻防博弈模型，将其应用于网络信息系统安全风险评估，构建相应的信息系统安全风险评估算法。通过仿真实验验证了本文模型和方法的有效性，能够对信息系统安全威胁进行科学、有效的评估。     

多率量测下随机跳变系统迁移交互多模型估计

实际工业过程中,量测数据除了在线仪表采集的快速率数据,还有离线化验等慢速率辅助量测数据.为了更好地利用离线化验数据,增加在线估计的精度,针对随机跳变系统,引入迁移学习思想,提出迁移交互多模型估计(Transfer interacting multiple model state estimator, IMM-TF)新策略.首先,将离线化验数据的边缘分布作为可以迁移的知识,迁移到贝叶斯后验分布,实现辅助量测数据的充分利用.其次,利用KL (Kullback-Leibler)散度度量知识迁移前后任务间的差异性,求解最优的贝叶斯迁移估计器.同时,结合慢速率量测,利用平滑策略获取待迁移的估计值,解决多率量测下的迁移估计难题.然后,利用影响力函数构建辅助量测数据与估计性能之间的解析关系,从而对迁移效果进行定量评价.最后,通过在目标跟踪实例中的应用,表明所提方法的有效性及优越性.