Titlebook: Cyber Threat Intelligence; Ali Dehghantanha,Mauro Conti,Tooska Dargahi Book 2018 Springer International Publishing AG, part of Springer Na

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書目名稱Cyber Threat Intelligence影響因子(影響力)




書目名稱Cyber Threat Intelligence影響因子(影響力)學(xué)科排名




書目名稱Cyber Threat Intelligence網(wǎng)絡(luò)公開(kāi)度




書目名稱Cyber Threat Intelligence網(wǎng)絡(luò)公開(kāi)度學(xué)科排名




書目名稱Cyber Threat Intelligence被引頻次




書目名稱Cyber Threat Intelligence被引頻次學(xué)科排名




書目名稱Cyber Threat Intelligence年度引用




書目名稱Cyber Threat Intelligence年度引用學(xué)科排名




書目名稱Cyber Threat Intelligence讀者反饋




書目名稱Cyber Threat Intelligence讀者反饋學(xué)科排名

FIS

ALIBI

Adaptive Traffic Fingerprinting for Darknet Threat Intelligence,ts to reduce the candidate data set to eliminate client-side traffic that is most unlikely to be responsible for server-side connections of interest. Our test results show that MITM manipulated server responses lead to expected changes received by the Tor client. Using simulation data generated by s

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值得

FELON

Education as a Key Factor in Fighting AIDS2-bit malicious Portable Executable (PE32) Windows files and develop taxonomy for better understanding of these techniques. Afterwards, we offer a tutorial on how different machine learning techniques can be utilized in extraction and analysis of a variety of static characteristic of PE binaries and

FELON

Triangles in Heterosexual HIV Transmissionreduction. Using the CorrelationAttributeEval method close to 100% precision can be maintained with a feature reduction of 59.5%. The CFSSubset filter achieves the highest feature reduction of 97.7% however with a slightly lower precision at 94.2%..Using a ranking method applied across the attribute

現(xiàn)暈光

Keynote Address: AIDS in the United Kingdomts to reduce the candidate data set to eliminate client-side traffic that is most unlikely to be responsible for server-side connections of interest. Our test results show that MITM manipulated server responses lead to expected changes received by the Tor client. Using simulation data generated by s

Exclaim

obstinate