Mısra, SanjayAzeez, Nureni AyofeAyemobola, Tolulope JideMisra, SanjayMaskeliunas, RytisDamasevicius, RobertasComputer Engineering2024-07-052024-07-052019222073-431X10.3390/computers80400862-s2.0-85076591140https://doi.org/10.3390/computers8040086https://hdl.handle.net/20.500.14411/3457Misra, Sanjay/0000-0002-3556-9331; Maskeliunas, Rytis/0000-0002-2809-2213; Damaševičius, Robertas/0000-0001-9990-1084; azeez, nureni ayofe/0000-0002-1475-2612Ubiquitous nature of Internet services across the globe has undoubtedly expanded the strategies and operational mode being used by cybercriminals to perpetrate their unlawful activities through intrusion on various networks. Network intrusion has led to many global financial loses and privacy problems for Internet users across the globe. In order to safeguard the network and to prevent Internet users from being the regular victims of cyber-criminal activities, new solutions are needed. This research proposes solution for intrusion detection by using the improved hashing-based Apriori algorithm implemented on Hadoop MapReduce framework; capable of using association rules in mining algorithm for identifying and detecting network intrusions. We used the KDD dataset to evaluate the effectiveness and reliability of the solution. Our results obtained show that this approach provides a reliable and effective means of detecting network intrusion.eninfo:eu-repo/semantics/openAccessassociation rule miningintrusion detectioncyberattacknetwork securityaprioriMapReduceArticle84WOS:000505731900021