VirusTotal (VT) provides aggregated threat intelligence on various entities including URLs, IP addresses, and binaries. It is widely used by researchers and practitioners to collect ground truth and evaluate the maliciousness of entities. In this work, we provide a comprehensive analysis of VT URL scanning reports containing the results of 95 scanners for 1.577 Billion URLs over two years. Individual VT scanners are known to be noisy in terms of their detection and attack type classification. To obtain high quality ground truth of URLs and actively take proper actions to mitigate different types of attacks, there are two challenges: (1) how to decide whether a given URL is malicious given noisy reports and (2) how to determine attack types (e.g., phishing or malware hosting) that the URL is involved in, given conflicting attack labels from different scanners. In this work, we provide a systematic comparative study on the behavior of VT scanners for different attack types of URLs. A common practice to decide the maliciousness is to use a cut-off threshold of scanners that report the URL as malicious. However, in this work, we show that using a fixed threshold is suboptimal, due to several reasons: (1) correlations between scanners; (2) lead/lag behavior; (3) the specialty of scanners; (4) the quality and reliability of scanners. A common practice to determine an attack type is to use majority voting. However, we show that majority voting could not accurately classify the attack type of a URL due to the bias from correlated scanners. Instead, we propose a machine learning-based approach to assign an attack type to URLs given the VT reports.
Mitochondrial damage-associated molecular patterns (mtDAMPs) include proteins, lipids, metabolites and DNA and have various context specific immunoregulatory functions. Cell-free mitochondrial DNA (mtDNA) is recognised via pattern recognition receptors and is a potent activator of the innate immune system. Cell-free mtDNA is elevated in the circulation of trauma and cancer patients, however the functional consequences of elevated mtDNA are largely undefined. Multiple myeloma (MM) relies upon cellular interactions within the bone marrow (BM) microenvironment for survival and progression. Here, using in-vivo models, we describe the role of MM cell derived mtDAMPs in the pro-tumoral BM microenvironment, and the mechanism and functional consequence of mtDAMPs in myeloma disease progression. Initially, we identified elevated levels of mtDNA in the peripheral blood serum of MM patients compared to healthy controls. Using the MM1S cells engrafted into NSG mice we established that elevated mtDNA was derived from MM cells. We further show that BM macrophages sense and respond to mtDAMPs through the STING pathway and inhibition of this pathway reduces MM tumor-burden in the KaLwRij-5TGM1 mouse model. Moreover, we found that MM derived mtDAMPs induced upregulation of chemokine signatures in BM macrophages and inhibition of this signature resulted in egress of MM cells from the BM. Here, we demonstrate that malignant plasma cells release mtDNA, a form of mtDAMPs, into the myeloma BM microenvironment, which in turn activates macrophages via STING signalling. We establish the functional role of these mtDAMP-activated macrophages in promoting disease progression and retaining MM cells in the pro-tumoral BM microenvironment.
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