Social　engineering　attacks　are　considered　one　of　themost　hazardous　cyberattacks　in　cybersecurity,　as　human　vulnerabilities　are　often　the　weakest　link　in　the　entire　network.　Such　vulnerabilities　are　becoming　increasingly　susceptible　to　network　security　risks.　Addressing　the　social　engineering　attack　defense　problem　has　been　the　focus　of　many　studies.　However,　two　main　challenges　hinder　its　successful　resolution.　Firstly,　the　vulnerabilities　in　social　engineering　attacks　are　unique　due　to　multistage　attacks,　leading　to　incorrect　social　engineering　defense　strategies.　Secondly,　social　engineering　attacks　are　real-time,　and　the　defense　strategy　algorithms　based　on　gaming　or　reinforcement　learning　are　too　complex　to　make　rapid　decisions.　This　paper　proposes　a　multiattribute　quantitative　incentive　method　based　on　human　vulnerability　and　an　improved　Q-learning　(IQL)　reinforcement　learning　method　on　human　vulnerability　attributes.　The　proposed　algorithm　aims　to　address　the　two　main　challenges　in　social　engineering　attack　defense　by　using　a　multiattribute　incentive　method　based　on　human　vulnerability　to　determine　the　optimal　defense　strategy.　Furthermore,　the　IQL　reinforcement　learning　method　facilitates　rapid　decision-making　during　real-time　attacks.　The　experimental　results　demonstrate　that　the　proposed　algorithm　outperforms　the　traditional　Qlearning　(QL)　and　deep　Q-network　(DQN)　approaches　in　terms　of　time　efficiency,　taking　9.1%　and　19.4%　less　time,　respectively.　Moreover,　the　proposed　algorithm　effectively　addresses　the　non-uniformity　of　vulnerabilities　in　social　engineering　attacks　and　provides　a　reliable　defense　strategy　based　on　human　vulnerability　attributes.　This　study　contributes　to　advancing　social　engineering　attack　defense　by　introducing　an　effective　and　efficient　method　for　addressing　the　vulnerabilities　of　human　factors　in　the　cybersecurity　domain.　©　2023　CRL　Publishing.　All　rights　reserved.