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One popular evasion method is to execute malicious code and perform malicious actions only in memory. Malicious programs that use this attack method are called memory-resident malware, with excellent evasion capability, and have posed huge threats to cyber security. Traditional static and dynamic methods are not effective in detecting memory-resident malware. In addition, existing memory forensics detection solutions perform unsatisfactorily in detection rate and depend on massive expert knowledge in memory analysis. This paper proposes MRm-DLDet, a state-of-the-art memory-resident malware detection framework, to overcome these drawbacks. MRm-DLDet first builds a virtual machine environment and captures memory dumps, then creatively processes the memory dumps into RGB images using a pre-processing technique that combines deduplication and ultra-high resolution image cropping, followed by our neural network MRmNet in MRm-DLDet to fully extract high-dimensional features from memory dump files and detect them. MRmNet receives the labeled sub-images of the cropped high-resolution RGB images as input of ResNet-18, which extracts the features of the sub-images. Then trains a network of gated recurrent units with an attention mechanism. Finally, it determines whether a program is memory-resident malware based on the detection results of each sub-image through a specially designed voting layer. We created a high-quality dataset consisting of 2,060 benign and memory-resident programs. In other words, the dataset contains 1,287,500 labeled sub-images cut from the MRm-DLDet transformed ultra-high resolution RGB images. We implement MRm-DLDet for Windows 10, and it performs better than the latest methods, with a detection accuracy of up to 98.34<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mo>%<\/mml:mo><\/mml:math><\/jats:alternatives><\/jats:inline-formula>. Moreover, we measured the effects of mimicry and adversarial attacks on MRm-DLDet, and the experimental results demonstrated the robustness of MRm-DLDet.<\/jats:p>","DOI":"10.1186\/s42400-023-00157-w","type":"journal-article","created":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T02:01:27Z","timestamp":1691028087000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["MRm-DLDet: a memory-resident malware detection framework based on memory forensics and deep neural network"],"prefix":"10.1186","volume":"6","author":[{"given":"Jiaxi","family":"Liu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianjun","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qixu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,8,3]]},"reference":[{"key":"157_CR1","unstructured":"Abrams L (2020) TrickBot malware now checks screen resolution to evade analysis. https:\/\/www.bleepingcomputer.com\/news\/security\/trickbot-malware-now-checks-screen-resolution-to-evade-analysis\/"},{"key":"157_CR2","unstructured":"Alrawi O, Ike M, Pruett M, Kasturi RP, Barua S, Hirani T, Hill B, Saltaformaggio B (2021) Forecasting malware capabilities from cyber attack memory images. 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