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涌现视角下的网络空间安全挑战(4)
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摘要:CharacteristicInterpretationExamples in Daily LifeExamples in CybersecurityPower-lawDistribution(also knownas Scale-freeNetworks)1) Mathematical treatment: The probability density function of variable
CharacteristicInterpretationExamples in Daily LifeExamples in CybersecurityPower-lawDistribution(also knownas Scale-freeNetworks)1) Mathematical treatment: The probability density function of variable x is f(x)=αx-r(α>0,r>0)2) Another treatment: The scales of most individuals are small, and only a few individuals have enormous scales.1) Zipf s law: In the natural language corpus, the number of occurrences of a word is inversely proportional to its ranking in the frequency table.2) Matthew effect: The stronger is getting stronger, and the weaker is getting weaker.1) Degree distribution: Only a few nodes have a high degree (these nodes are often called Hub), and others only have a small degree. The attacker only needs to focus on those Hub nodes to gain control of the ) Preferential linking: Newly added network nodes will also be preferentially linked to those Hub nodes, making the importance of the Hub nodes even more Loop(also known as Micro-Macro Link)Component behaviors affect system behaviors and are influenced by system behaviors, vice versa, which creates a feedback market: The buying∕selling be-haviors of investors make a big differ-ence in the stock in reverse, the rise and fall of the stock price will significantly affect the decision-making behaviors of : On the one hand, since the anonymity that it provides depends significantly on the number of users, the user s decisions about whether to use can affect the degree of the other hand, the degree of anonymity also affects users choices about whether to use we consider the system behavior we care about as a dependent variable f(x), as the independent variable x changes, f(x) must change rapidly in a nonlinear virus propagation:As the number of infected people increases, the infection rate and range will grow increasingly, and the number of newly infected people per unit of time shows a typical non-linear growth virus propagation: As for the In-ternet, because of the high connectivity and the nonexistent need for physical "contact" of network nodes, computer viruses spread much faster and more widely than biological that exhibit typical emergent phenomena often do not have central control them, control is of-ten dispersed to a significant number of nodes (components), which means greater : Obviously, there is no cen-tral control in the biosphere that con-trols the behavior of each organism so that each organism can act on its system: As a typical human-cen-tered computer system, there is no control in the password system for human users.
Fig. 1 The characteristics of emergence and typical examples图1 涌现的特征及示例
涌现性的应用主要可分为设计和控制.“设计”通常意味着“设计我们想要的涌现现象”.在系统的设计过程中,系统组件(微观)的特征与行为是已知的,我们所要做的就是基于这些已知的微观要素来“设计”出我们想要的涌现现象.这个问题涉及到微-宏观效应(micro-macro link)问题,事实上,这个问题称得上是涌现性研究中最核心也是最困难的问题.尽管仍然没有确切的解决方案,但是研究者[60-61,65]在思路和方向上基本达成了共识:结合自顶向下(top-down)的系统设计方法和自底向上(bottom-up)的仿真模拟实验方法,通过不断地进行循环迭代实验来达到最终的目的.在具体的施行过程中,研究和设计者应当始终坚持“全局观”,始终把想要获得的涌现现象作为系统设计的目标,通过分析这个目标找到相关联的系统组件,然后对这些组件的特征和行为进行设计和控制;紧接着进入仿真模拟阶段,通过仿真模拟平台运行这个系统,检查系统的整体行为是否符合预期设计目标,如果不符合的话,再对组件的行为进行修改,一直循环以上过程直到满足预期.
与“设计”相反的是,“控制”一般意味着“控制我们不想要的涌现现象”.而与“设计”类似的是,“控制”同样也很困难,因为涌现现象天生具有不可预测性.但是,尽管完全控制和预测是不可能的,但是仍然可以通过理论分析与仿真模拟对系统的涌现行为做出大致判断,至少可以判断会发生什么程度、属于哪个分类的涌现现象,有利于提前部署相应的防护措施,控制危害的规模.以全球气候系统为例,我们无法精确地预测天气情况,但是至少可以做出大致的判断,这样也就能在台风来临之前告知公众尽快撤离.同样,可以在大规模计算机病毒蔓延之前告知用户尽快安装防病毒软件.
总而言之,尽管涌现性的作用不是万能的,它具有特定的适用性[61],但是考虑到人类目前对于复杂世界的了解十分有限,涌现性研究仍然是深入了解和适应世界发展的强有力途径.
2 现实挑战
根据关键特征及其在安全研究中的意义,我们将涌现视角下的安全挑战分为3种类型:攻击、漏洞和防御.下面结合现实案例进行考察和分析.
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