As　network　techniques　rapidly　evolve,　attacks　are　becoming　increasingly　sophisticated　and　threatening.　Network　intrusion　detection　has　been　widely　accepted　as　an　effective　method　to　deal　with　network　threats.　Many　approaches　have　been　proposed,　exploring　different　techniques　and　targeting　different　types　of　traffic.　Anomaly-based　network　intrusion　detection　is　an　important　research　and　development　di-rection　of　intrusion　detection.　Despite　the　extensive　investigation　of　anomaly-based　network　intrusion　de-tection　techniques,　there　lacks　a　systematic　literature　review　of　recent　techniques　and　datasets.　We　follow　the　methodology　of　systematic　literature　review　to　survey　and　study　119　top-cited　papers　on　anomaly-based　intrusion　detection.　Our　study　rigorously　and　comprehensively　investigates　the　technical　landscape　of　the　field　in　order　to　facilitate　subsequent　research　within　this　field.　Specifically,　our　investigation　is　conducted　from　the　following　perspectives:　application　domains,　data　preprocessing　and　attack-detection　techniques,　evaluation　metrics,　coauthor　relationships,　and　datasets.　Based　on　the　research　results,　we　identify　unsolved　research　challenges　and　unstudied　research　topics　from　each　perspective,　respectively.　Finally,　we　present　several　promising　high-impact　future　research　directions.　(c)　2022　The　Authors.　Published　by　Elsevier　Ltd.　This　is　an　open　access　article　under　the　CC　BY　license　(　http://creativecommons.org/licenses/by/4.0/　)