02590nas a2200241   4500000000100000000000100001008004100002260001200043653001200055653003900067653002600106653003200132100001900164700002000183700002700203700001900230245007800249856008300327300000900410490001300419520190200432022001402334       9998                            d  c12/202310aMalware10aSemi Eager Classification (Semi-E)10aGranulometry Analysis10aStatic and Dynamic Analysis1 aMahendra Deore1 aManoj Tarambale1 aJambi Ratna Raja Kumar1 aSachin Sakhare00aGRASE: Granulometry Analysis With Semi Eager Classifier to Detect Malware  uhttps://www.ijimai.org/journal/sites/default/files/2023-12/ip2023_12_002_0.pdf  a1-150 vIn press3 aTechnological advancement in communication leading to 5G, motivates everyone to get connected to the internet including ‘Devices’, a technology named Web of Things (WoT). The community benefits from this large-scale network which allows monitoring and controlling of physical devices. But many times, it costs the security as MALicious softWARE (MalWare) developers try to invade the network, as for them, these devices are like a ‘backdoor’ providing them easy ‘entry’. To stop invaders from entering the network, identifying malware and its variants is of great significance for cyberspace. Traditional methods of malware detection like static and dynamic ones, detect the malware but lack against new techniques used by malware developers like obfuscation, polymorphism and encryption. A machine learning approach to detect malware, where the classifier is trained with handcrafted features, is not potent against these techniques and asks for efforts to put in for the feature engineering. The paper proposes a malware classification using a visualization methodology wherein the disassembled malware code is transformed into grey images. It presents the efficacy of Granulometry texture analysis technique for improving malware classification. Furthermore, a Semi Eager (SemiE) classifier, which is a combination of eager learning and lazy learning technique, is used to get robust classification of malware families. The outcome of the experiment is promising since the proposed technique requires less training time to learn the semantics of higher-level malicious behaviours. Identifying the malware (testing phase) is also done faster. A benchmark database like malimg and Microsoft Malware Classification challenge (BIG-2015) has been utilized to analyse the performance of the system. An overall average classification accuracy of 99.03 and 99.11% is achieved, respectively.  a1989-1660