原创力文档- 1、本文档共34页，可阅读全部内容。
- 2、原创力文档（book118）网站文档一经付费（服务费），不意味着购买了该文档的版权，仅供个人/单位学习、研究之用，不得用于商业用途，未经授权，严禁复制、发行、汇编、翻译或者网络传播等，侵权必究。
- 3、本站所有内容均由合作方或网友上传，本站不对文档的完整性、权威性及其观点立场正确性做任何保证或承诺！文档内容仅供研究参考，付费前请自行鉴别。如您付费，意味着您自己接受本站规则且自行承担风险，本站不退款、不进行额外附加服务；查看《如何避免下载的几个坑》。如果您已付费下载过本站文档，您可以点击 这里二次下载。
- 4、如文档侵犯商业秘密、侵犯著作权、侵犯人身权等，请点击“版权申诉”（推荐），也可以打举报电话：400-050-0827(电话支持时间：9:00-18:30)。
查看更多
Bad Data Injection in Smart Grid Attack and Defense Mec：坏数据注入在智能电网中的攻击和防御机制
The 1st simulation of the proposed scheme is shown with 2 case : case 1 has FAR of 1% and case 2 as the 0.1%. The attacker becomes active at time 6, where the a change distribution from H0 to H1, The proposed algorithm singal the alarm and terminates the pdetection process at time 7, for case 2 is time 8, each has its own threshold h1 h2 repectively, in term of different FAR . As you can see the missing detection occurs , for case is T_d =1 and case 2 is T_d=2. We also can conclude that the higher constraint will need more time to make decision, therefore, the slower detection,. That why we have longer detection delay in case 2. (FAR is 1% as same as MDR). The adversary becomes active and injects the malicious data at time t = 6. In other words, a change distribution is at τ = 6 from N(0,z) to N(a,z), where a is unknown. The curve of adaptive CUSUM statistic (St) shows the sudden increase right after a change of distributions. The proposed algorithm quickly responses the abnormal event by signaling an alarm of out-of control. As a result, ARL (Th) of adaptive CUSUM algorithm is 7 at St = 13.2351, and ARL (Td) of detection delay is 1 in this simulation. The proposed algorithm signals the alarm and terminates the process at time 7 ; the detection delay occurs because of the missing detection at time 6 as shown in Figure 3. The system continuos the detection process until the CUSUM statistic St, which excesses the threshold. However, the detection accuracy of the proposed scheme is comparable high while maintaining a certain level of detection error rate. * we consider the Rao test [18], which is the asymptotically equivalent test model of GLRT. GLRT is to min the worst case effect by maximizing the unknown using ML estimation. But giving us high Complexity, near impossible to implement in the reality. The derivation of Rao test is similar to the locally most powerful (LMP) test but only much simpler; Rao test has the straight-forward calculation by taking d
文档评论（0）