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Preventing network intrusion is the essential requirement of network security. In recent years, people have conducted a lot of research on network intrusion detection systems. However, with the increasing number of advanced threat attacks, traditional intrusion detection mechanisms have defects and it is still indispensable to design a powerful intrusion detection system. This paper researches the NSL-KDD data set and analyzes the latest developments and existing problems in the field of intrusion detection technology. For unbalanced distribution and feature redundancy of the data set used for training, some training samples are under-sampling and feature selection processing. To improve the detection effect, a Deep Stacking Network model is proposed, which combines the classification results of multiple basic classifiers to improve the classification accuracy. In the experiment, we screened and compared the performance of various mainstream classifiers and found that the four models of the decision tree, k-nearest neighbors, deep neural network and random forests have outstanding detection performance and meet the needs of different classification effects. Among them, the classification accuracy of the decision tree reaches 86.1%. The classification effect of the Deeping Stacking Network, a fusion model composed of four classifiers, has been further improved and the accuracy reaches 86.8%. Compared with the intrusion detection system of other research papers, the proposed model effectively improves the detection performance and has made significant improvements in network intrusion detection.

Paclitaxel (PTX) is a first-line chemotherapeutic agent for treating ovarian cancer. However, PTX resistance has become a major obstacle in ovarian cancer therapy. The underlying mechanism associated with PTX resistance is still unclear. We used qPCR to detect taurine up-regulated 1 (TUG1) expression in normal ovarian tissues and ovarian tumor tissues. A combination of small interfering RNA (siRNA), cell counting kit 8 (CCK8), colony formation assay and nude mouse model were used to detect the effect of TUG1 on ovarian cancer cell PTX-resistance. Autophagy/cytotoxicity dual staining assay, luciferase reporter assay, Western blot and RNA-binding protein immunoprecipitation assay were used for further mechanistic studies. TUG1 is highly expressed not only in ovarian tumor tissues compared with normal ovarian tissues but also in the chemo-resistant group compared with the sensitive group. Knockdown of TUG1 by siRNA decreased ovarian cancer cell and xenograft tumor PTX resistance with or without PTX treatment. Moreover, deletion of TUG1 in ovarian cancer cells decreased autophagosome formation and increased apoptosis as demonstrated by autophagy/cytotoxicity dual staining and Western blot assays. Furthermore, microRNA-29b-3p (miR-29b-3p) was found as the direct target of TUG1. Additionally, TUG1 could directly bind Ago2, a key protein of the RNA-induced silencing complex. Our findings suggest that TUG1, through targeting miR-29b-3p, induces autophagy and consequently results in PTX resistance in ovarian cancer.

This paper presents a new model for multi-object tracking (MOT) with a transformer. MOT is a spatiotemporal correlation task among interest objects and one of the crucial technologies of multi-unmanned aerial vehicles (Multi-UAV). The transformer is a self-attentional codec architecture that has been successfully used in natural language processing and is emerging in computer vision. This study proposes the Vision Transformer Tracker (ViTT), which uses a transformer encoder as the backbone and takes images directly as input. Compared with convolution networks, it can model global context at every encoder layer from the beginning, which addresses the challenges of occlusion and complex scenarios. The model simultaneously outputs object locations and corresponding appearance embeddings in a shared network through multi-task learning. Our work demonstrates the superiority and effectiveness of transformer-based networks in complex computer vision tasks and paves the way for applying the pure transformer in MOT. We evaluated the proposed model on the MOT16 dataset, achieving 65.7% MOTA, and obtained a competitive result compared with other typical multi-object trackers.

Background As one of the fundamental problems in bioinformatics, the double digest problem (DDP) focuses on reordering genetic fragments in a proper sequence. Although many algorithms for dealing with the DDP problem were proposed during the past decades, it is believed that solving DDP is still very time-consuming work due to the strongly NP-completeness of DDP. However, none of these algorithms consider the privacy issue of the DDP data that contains critical business interests and is collected with days or even months of gel-electrophoresis experiments. Thus, the DDP data owners are reluctant to deploy the task of solving DDP over cloud. Results Our main motivation in this paper is to design a secure outsourcing computation framework for solving the DDP problem. We at first propose a privacy-preserving outsourcing framework for handling the DDP problem by using a cloud server; Then, to enable the cloud server to solve the DDP instances over ciphertexts, an order-preserving homomorphic index scheme (OPHI) is tailored from an order-preserving encryption scheme published at CCS 2012; And finally, our previous work on solving DDP problem, a quantum inspired genetic algorithm (QIGA), is merged into our outsourcing framework, with the supporting of the proposed OPHI scheme. Moreover, after the execution of QIGA at the cloud server side, the optimal solution, i.e. two mapping sequences, would be transferred publicly to the data owner. Security analysis shows that from these sequences, none can learn any information about the original DDP data. Performance analysis shows that the communication cost and the computational workload for both the client side and the server side are reasonable. In particular, our experiments show that PP-DDP can find optional solutions with a high success rate towards typical test DDP instances and random DDP instances, and PP-DDP takes less running time than DDmap, SK05 and GM12, while keeping the privacy of the original DDP data. Conclusion The proposed outsourcing framework, PP-DDP, is secure and effective for solving the DDP problem.

Ischemic postconditioning (IPOC), or relief of ischemia in a stuttered manner, has emerged as an innovative treatment strategy to reduce programmed cell death, attenuate ischemic injuries, and improve neurological outcomes. However, the mechanisms involved have not been completely elucidated. Recent studies indicate that autophagy is a type of programmed cell death that plays elusive roles in controlling neuronal damage and metabolic homeostasis. This study aims to determine the role of autophagy in IPOC-induced neuroprotection against focal cerebral ischemia in rats. A focal cerebral ischemic model with permanent middle cerebral artery (MCA) occlusion plus transient common carotid artery (CCA) occlusion was established. The autophagosomes and the expressions of LC3/Beclin 1/p62 were evaluated for their contribution to the activation of autophagy. We found that autophagy was markedly induced with the upregulation of LC3/Beclin 1 and downregulation of p62 in the penumbra at various time intervals following ischemia. IPOC, performed at the onset of reperfusion, reduced infarct size, mitigated brain edema, inhibited the induction of LC3/Beclin 1 and reversed the reduction of p62 simultaneously. Rapamycin, an inducer of autophagy, partially reversed all the aforementioned effects induced by IPOC. Conversely, autophagy inhibitor 3-methyladenine (3-MA) attenuated the ischemic insults, inhibited the activation of autophagy, and elevated the expression of anti-apoptotic protein Bcl-2, to an extent comparable to IPOC. The present study suggests that inhibition of the autophagic pathway plays a key role in IPOC-induced neuroprotection against focal cerebral ischemia. Thus, pharmacological inhibition of autophagy may provide a novel therapeutic strategy for the treatment of stroke.

Object detection is an essential task in computer vision. Many methods have made significant progress in ordinary object detection. Due to the particularity of remote sensing images, the detection target is tiny, the background is messy, dense, and has mutual occlusion, which makes the general detection method challenging to apply to remote sensing images. For these problems, we propose a new detection framework feature extraction and filtration method with a mask improvement network (EFM-Net) to enhance object detection ability. In EFM-Net, we designed a multi-branched feature extraction (MBFE) module to better capture the information in the feature graph. In order to suppress the background interference, we designed a background filtering module based on attention mechanisms to enhance the attention of objects. Finally, we proposed a mask generate the boundary improvement method to make the network more robust to occlusion detection. We tested the DOTA v1.0, NWPU VHR-10, and UCAS-AOD datasets, and the experimental results show that our method has excellent effects.

With the emergence of network security issues, various security devices that generate a large number of logs and alerts are widely used. This paper proposes an alert aggregation scheme that is based on conditional rough entropy and knowledge granularity to solve the problem of repetitive and redundant alert information in network security devices. Firstly, we use conditional rough entropy and knowledge granularity to determine the attribute weights. This method can determine the different important attributes and their weights for different types of attacks. We can calculate the similarity value of two alerts by weighting based on the results of attribute weighting. Subsequently, the sliding time window method is used to aggregate the alerts whose similarity value is larger than a threshold, which is set to reduce the redundant alerts. Finally, the proposed scheme is applied to the CIC-IDS 2018 dataset and the DARPA 98 dataset. The experimental results show that this method can effectively reduce the redundant alerts and improve the efficiency of data processing, thus providing accurate and concise data for the next stage of alert fusion and analysis.

With the frequent occurrence of network security events, the intrusion detection system will generate alarm and log records when monitoring the network environment in which a large number of log and alarm records are redundant, which brings great burden to the server storage and security personnel. How to reduce the redundant alarm records in network intrusion detection has always been the focus of researchers. In this paper, we propose a method using the whale optimization algorithm to deal with massive redundant alarms. Based on the alarm hierarchical clustering, we integrate the whale optimization algorithm into the process of generating alarm hierarchical clustering and optimizing the cluster center and put forward two versions of local hierarchical clustering and global hierarchical clustering, respectively. To verify the feasibility of the algorithm, we conducted experiments on the UNSW-NB15 data set; compared with the previous alarm clustering algorithms, the alarm clustering algorithm based on the whale optimization algorithm can generate higher quality clustering in a shorter time. The results show that the proposed algorithm can effectively reduce redundant alarms and reduce the load of IDS and staff.

Background Ceramide accumulation is considered a contributing factor to neuronal dysfunction and damage. However, the underlying mechanisms that occur following ischemic insult are still unclear. Methods In the present study, we established cerebral ischemia models using four-vessel occlusion and oxygen-glucose deprivation methods. The hippocampus neural cells were subjected to immunohistochemistry and immunofluorescence staining for ceramide and neutral sphingomyelinase 2 (nSMase2) levels; immunoprecipitation and immunoblot analysis for nSMase2, receptor for activated C kinase 1 (RACK1), embryonic ectoderm development (EED), p38 mitogen-activated protein kinase (p38MAPK) and phosphorylated p38MAPK expression; SMase assay for nSMase and acid sphingomyelinase (aSMase) activity; real-time reverse transcription polymerase chain reaction for cytokine expression; and Nissl, microtubule-associated protein 2 and terminal deoxynucleotidyl transferase–mediated deoxyuridine triphosphate nick-end labeling staining. Results We found considerable production of ceramide in astrocytes, but not in neurons, during early cerebral ischemia. This was accompanied by the induction of nSMase (but not aSMase) activity in the rat hippocampi. The inhibition of nSMase2 activity effectively reduced ceramide accumulation in astrocytes and alleviated neuronal damage to some extent. Meanwhile, the expression levels of proinflammatory cytokines, including tumor necrosis factor α (TNF-α), interleukin 1β (IL-1β) and IL-6, were found to be upregulated, which may have played an import role in neuronal damage mediated by the nSMase2/ceramide pathway. Although enhanced binding of nSMase2 with RACK1 and EED were also observed after cerebral ischemia, nSMase2 activity was not blocked by the TNF-α receptor inhibitor through RACK1/EED signaling. p38MAPK, but not protein kinase Cζ or protein phosphatase 2B, was able to induce nSMase2 activation after ischemia. p38MAPK can be induced by A2B adenosine receptor (A 2B AR) activity. Conclusions These results indicate that the inhibition of ceramide production in astrocytes by targeting A 2B AR/p38MAPK/nSMase2 signaling may represent a viable approach for attenuating inflammatory responses and neuronal damage after cerebral ischemia.

Ethereum attracts extensive attention due to its distinctive function of smart contract and decentralized applications (Dapps). Since the number of contracts on blockchain has increased vigorously, various security vulnerabilities come up. Researchers rely on static symbolic analysis method at first, and it seems to perform well in the accuracy of vulnerability detection. However, this method requires manual analysis in advance and it needs to traverse all the possible execution paths to find out the vulnerable ones. The deeper the path goes, the more time it costs to detect the contracts. This paper proposes an approach to detect smart contracts vulnerability on blockchain by using machine learning(ML) methods. This approach aims to build a general benchmark for new vulnerability detection in order to reduce the demand of expert manpower. Moreover, the high-speed-performance ML algorithm makes quick detection comes true. As long as we adjust the threshold of the model, it can work as a fast prefilter for the traditional symbolic analysis tools in further improvement of accuracy.