LDR 01777cam a2200265 n 450 001 TD20020559 005 20200507165138.0 049 $aTDMAGDIG 100 $a20190501d2020 --k--ita-50----ba 101 1 $aeng 200 1 $aIntelligent Agents for Active Malware Analysis$bTesi di dottorato 300 $adiritti: info:eu-repo/semantics/openAccess 300 $aIn relazione con info:eu-repo/semantics/altIdentifier/hdl/11562/1017764 328 0$btesi di dottorato$cSettore INF/01 - Informatica 330 $aThe main contribution of this thesis is to give a novel perspective on Active Malware Analysis modeled as a decision making process between intelligent agents. We propose solutions aimed at extracting the behaviors of malware agents with advanced Artificial Intelligence techniques. In particular, we devise novel action selection strategies for the analyzer agents that allow to analyze malware by selecting sequences of triggering actions aimed at maximizing the information acquired. The goal is to create informative models representing the behaviors of the malware agents observed while interacting with them during the analysis process. Such models can then be used to effectively compare a malware against others and to correctly identify the malware family 689 0 $aSettore INF/01$b- Informatica$cTDR 700 0$aRiccardo Sartea 702 0$aFarinelli, Alessandro 801 3$aIT$bIT-FI0098 856 4 $uhttp://memoria.depositolegale.it/*/http://hdl.handle.net/11562/1017764$2http://hdl.handle.net/11562/1017764 856 4 $uhttp://memoria.depositolegale.it/*/http://iris.univr.it/bitstream/11562/1017764/1/PHD_THESIS.pdf$2http://iris.univr.it/bitstream/11562/1017764/1/PHD_THESIS.pdf 977 $a CR 997 $aCF FMT $aTD FOR $aTD