TKRD : trusted kernel rootkit detection for cybersecurity of VMs based on machine learning and memory forensic analysis

The promotion of cloud computing makes the virtual machine (VM) increasingly a target of malware attacks in cybersecurity such as those by kernel rootkits. Memory forensic, which observes the malicious tracks from the memory aspect, is a useful way for malware detection. In this paper, we propose a novel TKRD method to automatically detect kernel rootkits in VMs from private cloud, by combining VM memory forensic analysis with bio-inspired machine learning technology. Malicious features are extracted from the memory dumps of the VM through memory forensic analysis method. Based on these features, various machine learning classifiers are trained including Decision tree, Rule based classifiers, Bayesian and Support vector machines (SVM). The experiment results show that the Random Forest classifier has the best performance which can effectively detect unknown kernel rootkits with an Accuracy of 0.986 and an AUC value (the area under the receiver operating characteristic curve) of 0.998

An Innovative Approach for Gob-Side Entry Retaining With Thick and Hard Roof: A Case Study

An innovative roadway layout in a Chinese colliery based on gob-side entry retaining (GER) with thick and hard roof (THR) was introduced. Suspended roof is left with a large area in GER with THR, which leads to large area roof weighting (LARW). LARW for GER with THR and mechanism of shallow-hole blasting to force roof caving in GER were expounded. Key parameters of shallow-hole blasting to force roof caving are proposed. LS-DYNA3D was used to validate the rationality of those key parameters, and UDEC was used to discuss and validate shallow-hole blasting to force roof-caving effect by contrast to the model without blasting and the model with shallow-hole blasting. Moreover, shallow-hole blasting technology to force roof caving for GER with THR was carried out in the Chinese colliery as a case study. Field test indicates that shallow-hole blasting technology effectively controls ground deformation of GER with THR and prevents LARW

Review and development of surrounding rock control technology for gob-side entry retaining in China

The research and application of gob-side entry retaining (GER) technology in Chinese coal mines has been over 60 years. Two major technical types including GER with filling and GER with roof cutting were developed. However, due to the complex mining and geological conditions of the mined-out coal seam in different mining areas, as well as the strong mining pressure behaviours in the retained roadway, the promotion and application of GER technology presented ups and downs. Firstly, the achievements and key technological advances in the main GER types in China, the principle of surrounding rock stability, road-in support technology, roadside support technology, adaptability evaluation and surrounding rock stability monitoring are summarized, and the application applicability of different technologies at this stage is analyzed. Then, the difficulties and challenges faced by current GER technology are summarized: there is still no systematic theory for GER with strong mining pressure; there are still shortcomings in the theoretical understanding of the interaction mechanism between the surrounding rock and the support body for GER with filling; the mechanical and deformation characteristics of the filling materials for the roadside support body are not yet suitable for GER in deep working faces or with strong mining pressure; the mechanism and control technology of floor heave for GER are not yet perfect; the research on stability control of filling body for GER under strong dynamic load or rock burst is still in a blank. Finally, concerned with such difficulties and challenges, several reserve technologies have been proposed: the coordinated control of controlled roof cutting and filling for GER in fully mechanized caving/ full-seam mining in the thick coal seam, and the GER technology with additive modified high-water materials in working faces with strong mining pressure; finally, a set of intelligent inversion workflow for rock mechanical parameters used in GER numerical simulations for GER is established, and an intelligent optimization design method for GER support parameters is proposed

Immunoglobulin and T Cell Receptor Gene High-Throughput Sequencing Quantifies Minimal Residual Disease in Acute Lymphoblastic Leukemia and Predicts Post-Transplantation Relapse and Survival

AbstractMinimal residual disease (MRD) quantification is an important predictor of outcome after treatment for acute lymphoblastic leukemia (ALL). Bone marrow ALL burden ≥ 10−4 after induction predicts subsequent relapse. Likewise, MRD ≥ 10−4 in bone marrow before initiation of conditioning for allogeneic (allo) hematopoietic cell transplantation (HCT) predicts transplantation failure. Current methods for MRD quantification in ALL are not sufficiently sensitive for use with peripheral blood specimens and have not been broadly implemented in the management of adults with ALL. Consensus-primed immunoglobulin (Ig), T cell receptor (TCR) amplification and high-throughput sequencing (HTS) permit use of a standardized algorithm for all patients and can detect leukemia at 10−6 or lower. We applied the LymphoSIGHT HTS platform (Sequenta Inc., South San Francisco, CA) to quantification of MRD in 237 samples from 29 adult B cell ALL patients before and after allo-HCT. Using primers for the IGH-VDJ, IGH-DJ, IGK, TCRB, TCRD, and TCRG loci, MRD could be quantified in 93% of patients. Leukemia-associated clonotypes at these loci were identified in 52%, 28%, 10%, 35%, 28%, and 41% of patients, respectively. MRD ≥ 10−4 before HCT conditioning predicted post-HCT relapse (hazard ratio [HR], 7.7; 95% confidence interval [CI], 2.0 to 30; P = .003). In post-HCT blood samples, MRD ≥10−6 had 100% positive predictive value for relapse with median lead time of 89 days (HR, 14; 95% CI, 4.7 to 44, P < .0001). The use of HTS-based MRD quantification in adults with ALL offers a standardized approach with sufficient sensitivity to quantify leukemia MRD in peripheral blood. Use of this approach may identify a window for clinical intervention before overt relapse

Baicalein-corrected gut microbiota may underlie the amelioration of memory and cognitive deficits in APP/PS1 mice

Background: Baicalein is an active ingredient extracted from the root of S. baicalensis Georgi, which exhibits cardiovascular protection, anti-inflammatory, and anti-microbial properties. Our previous study showed that chronic treatment of Baicalein ameliorated cognitive dysfunction in a mouse model of Alzheimer's disease (AD). However, it remains unknown whether Baicalein ameliorates cognitive deficits in AD mouse models by altering gut microbiota and its metabolites.Methods: Behavioral tests, metagenomic and untargeted metabolomics analyses were used to evaluate the effects of Baicalein on the APP/PS1 mice.Results: Our research showed that treatment of Baicalein for 2 weeks ameliorated cognition and memory in a dose-dependent manner, as indicated by the significant increases in the Discrimination index and Number of crossings and decrease in latency to the previous platform location in 8-month of age APP/PS1 mice in novel object recognition and water maze tests. The metagenomic analysis showed the abundance of the dominant phyla in all groups, including Bacteroidetes (14.59%–67.02%) and Firmicutes (20.19%–61.39%). LEfSe analysis of metagenomics identified three species such as s__Roseburia_sp_1XD42_69, s__Muribaculaceae_bacterium_Isolate_104_HZI, s__Muribaculaceae_bacterium_Isolate_110_HZI as Baicalein-treated potential biomarkers. Metabolite analysis revealed the increment of metabolites, including glutamate, thymine and hexanoyl-CoA.Conclusion: The effects of Baicalein on memory and cognition may relate to the metabolism of nucleotides, lipids and glucose

CNID: Research of Network Intrusion Detection Based on Convolutional Neural Network

Network intrusion detection system can effectively detect network attack behaviour, which is very important to network security. In this paper, a multiclassification network intrusion detection model based on convolutional neural network is proposed, and the algorithm is optimized. First, the data is preprocessed, the original one-dimensional network intrusion data is converted into two-dimensional data, and then the effective features are learned using optimized convolutional neural networks, and, finally, the final test results are produced in conjunction with the Softmax classifier. In this paper, KDD-CUP 99 and NSL-KDD standard network intrusion detection dataset were used to carry out the multiclassification network intrusion detection experiment; the experimental results show that the multiclassification network intrusion detection model proposed in this paper improves the accuracy and check rate, reduces the false positive rate, and also obtains better test results for the detection of unknown attacks

A nonlinear model for magnetocapacitance effect in PZT-ring/Terfenol-D-strip magnetoelectric composites

In previous works, most of them employ a linear constitutive model to describe magnetocapacitance (MC) effect in magnetoelectric (ME) composites, which lead to deficiency in their theoretical results. In view of this, based on a nonlinear magnetostrictive constitutive relation and a linear piezoelectric constitutive relation, we establish a nonlinear model for MC effect in PZT-ring/Terfenol-D-strip ME composites. The numerical results in this paper coincide better with experimental data than that of a linear model, thus, it’s essential to utilize a nonlinear constitutive model for predicting MC effect in ME composites. Then the influences of external magnetic fields, pre-stresses, frequencies, and geometric sizes on the MC effect are discussed, respectively. The results show that the external magnetic field is responsible for the resonance frequency shift. And the resonance frequency is sensitive to the ratio of outer and inner radius of the PZT ring. Moreover, some other piezoelectric materials are employed in this model and the corresponding MC effects are calculated, and we find that different type of piezoelectric materials affect the MC effect obviously. The proposed model is more accurate for multifunction devices designing

Performance Study of PID and Fuzzy Controllers for Position Control of 6 DOF arm Manipulator with Various Defuzzification Strategies

Today’s arm manipulators are more and more demanding in terms of productivity. Conventional controllers are not always able to provide good and accurate results. To complete a position movement of the manipulator’s end-effector, a set of joint angles of manipulator first required to be converted to the position coordinates by using the forward kinematics method, and each joint rotation is executed using a servomotor feedback control. The kinematic model has been validated using MATLAB® robotics toolbox. An end-effector based 6 degree of freedom (6-DOF) platform is proposed in this work which uses DC servomotor for actuation of the three revolute joints. PID controller is used as a reference benchmark. And FLC controller with different defuzzification strategies was employed. Results were compared in terms of time response criteria. Simulation results using MATLAB are demonstrated that PID has superior performance in terms of transient parameters. In Steady state response, both PID and FLC manage to converge to the desired output but in terms of overshot FLC is outperformed

Performance Study of PID and Fuzzy Controllers for Position Control of 6 DOF arm Manipulator with Various Defuzzification Strategies

Today’s arm manipulators are more and more demanding in terms of productivity. Conventional controllers are not always able to provide good and accurate results. To complete a position movement of the manipulator’s end-effector, a set of joint angles of manipulator first required to be converted to the position coordinates by using the forward kinematics method, and each joint rotation is executed using a servomotor feedback control. The kinematic model has been validated using MATLAB® robotics toolbox. An end-effector based 6 degree of freedom (6-DOF) platform is proposed in this work which uses DC servomotor for actuation of the three revolute joints. PID controller is used as a reference benchmark. And FLC controller with different defuzzification strategies was employed. Results were compared in terms of time response criteria. Simulation results using MATLAB are demonstrated that PID has superior performance in terms of transient parameters. In Steady state response, both PID and FLC manage to converge to the desired output but in terms of overshot FLC is outperformed