Targeting the operating system (OS) kernels, kernel rootkits pose a formidable threat to computer systems and their users. Recent efforts have made significant progress in blocking them from injecting malicious code into the OS kernel for execution. Unfortunately, they cannot block the emerging so-called return-oriented rootkits (RORs). Without the need of injecting their own malicious code, these rootkits can discover and chain together "return-oriented gadgets" (that consist of only legitimate kernel code) for rootkit computation.In this paper, we propose a compiler-based approach to defeat these return-oriented rootkits. Our approach recognizes the hallmark of return-oriented rootkits, i.e., the ret instruction, and accordingly aims to completely remove them in a running OS kernel. Specifically, one key technique named return indirection is to replace the return address in a stack frame into a return index and disallow a ROR from using their own return addresses to locate and assemble returnoriented gadgets. Further, to prevent legitimate instructions that happen to contain return opcodes from being misused, we also propose two other techniques, register allocation and peephole optimization, to avoid introducing them in the first place. We have developed a LLVM-based prototype and used it to generate a return-less FreeBSD kernel. Our evaluation results indicate that the proposed approach is generic, effective, and can be implemented on commodity hardware with a low performance overhead.
Leucine-rich α2 glycoprotein 1 (LRG1) has been shown to be aberrantly expressed in multiple human malignancies. However, the biological functions of LRG1 in human glioblastoma remain unknown. Here, we report for the first time the role of LRG1 in glioblastoma development based on the preliminary in vitro and in vivo data. We first confirmed the expression of LRG1 in human glioblastoma cell lines. Next, to investigate the role of LRG1 in the tumorigenesis and development of glioblastoma, a short hairpin RNA (shRNA) construct targeting LRG1 mRNA was transfected into U251 glioblastoma cells to generate a cell line with stably silenced LRG1 expression. The results showed that silencing of LRG1 significantly inhibited cell proliferation, induced cell cycle arrest at G0/G1 phase, and enhanced apoptosis in U251 cells in vitro. Consistently, LRG1 silencing resulted in the downregulation of key cell cycle factors including cyclin D1, B, and E and apoptotic gene Bcl-2 while elevated the levels of pro-apoptotic Bax and cleaved caspase-3, as determined by Western blot analysis. We further demonstrate that the silencing of LRG1 expression effectively reduced the tumorigenicity of U251 cells, delayed tumor formation, and promoted apoptosis in a xenograft tumor model in vivo. In conclusion, silencing the expression of LRG1 suppresses the growth of glioblastoma U251 cells in vitro and in vivo, suggesting that LRG1 may play a critical role in glioblastoma development, and it may have potential clinical implications in glioblastoma therapy.
Electrical Capacitance Tomography (ECT) image reconstruction has developed for decades and made great achievements, but there is still a need to find a new theoretical framework to make it better and faster. In recent years, machine learning theory has been introduced in the ECT area to solve the image reconstruction problem. However, there is still no public benchmark dataset in the ECT field for the training and testing of machine learning-based image reconstruction algorithms. On the other hand, a public benchmark dataset can provide a standard framework to evaluate and compare the results of different image reconstruction methods. In this paper, a benchmark dataset for ECT image reconstruction is presented. Like the great contribution of ImageNet that transformed machine learning research, this benchmark dataset is hoped to be helpful for society to investigate new image reconstruction algorithms since the relationship between permittivity distribution and capacitance can be better mapped. In addition, different machine learning-based image reconstruction algorithms can be trained and tested by the unified dataset, and the results can be evaluated and compared under the same standard, thus, making the ECT image reconstruction study more open and causing a breakthrough.
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