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Malicious software, sometimes known as malware, is software designed to harm a computer, network, or any of the connected resources. Without the user's knowledge, malware can spread throughout their computer system. Malware is typically disseminated via online connections and mobile devices. While malware has always been a problem in the digital age, its effects have gotten increasingly serious. Traditional malware detection methods seek to locate specific malware samples and families to recognize harmful codes and can be located using traditional signature- and rule-based detection methods. The research focuses on developing malware detectors using data mining techniques. The proposed method outlined below sets itself apart by emphasizing the processing of malware behaviors significantly dependent on aspects. Finding more dependable intelligent detecting techniques is a crucial component of this paper. In order to identify the cluster of the most essential malware features and use decision tree classifiers for malware detection, the study, a common methodology for creating malware detectors based on data mining, is implemented and investigated. Our approach can identify the most significant features of malware that can significantly determine and detect a malware code.

BACKGROUND: Inflammatory breast cancer (IBC) is an aggressive type of advanced breast cancer with a poor prognosis. We recently found that focal adhesion kinase 1 (FAK1) is upregulated and phosphorylated (active) in IBC. In this study, we investigated the effect of CEP-37440, a dual inhibitor of FAK1 and anaplastic lymphoma kinase (ALK), using human IBC cell lines and preclinical models of IBC. METHODS: Cell proliferation assays were performed in the presence of several concentrations of CEP-37440 using IBC and triple-negative breast cancer non-IBC cell lines. In vitro, we studied the expression of total FAK1, phospho-FAK1 (Tyr 397), total ALK and phospho-ALK (Tyr 1604). In vivo, we tested CEP-37440 using FC-IBC02, SUM149, and SUM190 IBC xenograft mouse models. RESULTS: CEP-37440 at low concentration decreased the proliferation of the IBC cell lines FC-IBC02, SUM190, and KPL4, while not affecting the proliferation of normal breast epithelial cells. At higher concentration, CEP-37440 was also able to inhibit the proliferation of the IBC cell line MDA-IBC03 and the triple-negative non-IBC cell lines MDA-MB-231 and MDA-MB-468; the IBC cell line SUM149 showed a slight response to the drug. CEP-37440 decreased the cell proliferation of FC-IBC02, SUM190, and KPL4 by blocking the autophosphorylation kinase activity of FAK1 (Tyr 397). None of the cells evaluated expressed ALK. In vivo, after 7 weeks of CEP-37440 treatment, the SUM190, FC-IBC02, and SUM149 breast tumor xenografts were smaller in mice treated with 55 mg/kg bid CEP-37440 compared to the controls; the tumor growth inhibition (TGI) was 79.7 %, 33 %, and 23 %, respectively. None of the FC-IBC02 breast xenografts mice treated with CEP-37440 developed brain metastasis while 20 % of the mice in the control group developed brain metastasis. Expression array analyses in FC-IBC02 cells showed that CEP-37440 affects the expression of genes related to apoptosis, interferon signaling, and cytokines. CONCLUSIONS: CEP-37440