Intelligent　reflecting　surface　(IRS)　is　a　very　promising　technology　for　the　development　of　beyond　5G　or　6G　wireless　communications　due　to　its　low　complexity,　intelligence,　and　green　energy-efficient　properties.　In　this　paper,　we　combined　IRS　with　physical　layer　security　(PLS)　to　solve　the　security　issue　of　cognitive　radio　(CR)　networks.　Specifically,　an　IRS-assisted　multi-input　single-output　(MISO)　CR　wiretap　channel　was　studied.　To　maximize　the　secrecy　rate　of　secondary　users　subject　to　a　total　power　constraint　(TPC)　for　the　transmitter　and　interference　power　constraint　(IPC)　for　a　single　antenna　primary　receiver　(PR)　in　this　channel,　an　alternating　optimization　(AO)　algorithm　is　proposed　to　jointly　optimize　the　transmit　covariance　R　at　transmitter　and　phase　shift　coefficient　Q　at　IRS　by　fixing　the　other　as　constant.　When　Q　is　fixed,　R　is　globally　optimized　by　equivalently　transforming　the　quasi-convex　sub-problem　to　convex　one.　When　R　is　fixed,　bisection　search　in　combination　with　minorization–maximization　(MM)　algorithm　was　applied　to　optimize　Q　from　the　non-convex　fractional　programming　sub-problem.　During　each　iteration　of　MM,　another　bisection　search　algorithm　is　proposed,　which　is　able　to　find　the　global　optimal　closed-form　solution　of　Q　given　the　initial　point　from　the　previous　iteration　of　MM.　The　convergence　of　the　proposed　algorithm　is　analyzed,　and　an　extension　of　applying　this　algorithm　to　multi-antenna　PR　case　is　discussed.　Simulations　have　shown　that　our　proposed　IRS-assisted　design　greatly　enhances　the　secondary　user’s　secrecy　rate　compared　to　existing　methods　without　IRS.　Even　when　IPC　is　active,　the　secrecy　rate　returned　by　our　algorithm　increases　with　transmit　power　as　if　there　is　no　IPC　at　all.　©　2020　by　the　authors.　Licensee　MDPI,　Basel,　Switzerland.