A Practical Online Approach to Protecting Kernel Heap Buffers in Kernel Modules

Heap overflow attack is one of the major memory corruption attacks that have become prevalent for decades. To defeat this attack,many protection methods are proposed in recent years. However,most of these existing methods focus on user-level heap overflow detection. Only a few methods are proposed for kernel heap protection. Moreover,all these kernel protection methods need modifying the existing OS kernel so that they may not be adopted in practice. To address this problem,we propose a lightweight virtualization-based solution that can protect the kernel heap buffers allocated for the target kernel modules. The key idea of our approach is to combine the static binary analysis and virtualization technology to trap a memory allocation operation of the target kernel module,and then add one secure canary word to the end of the allocated buffer. After that,a monitor process is launched to check the integrity of the canaries. The evaluations show that our system can detect kernel heap overflow attacks effectively with minimal performance cost.

Joint Rain Streaks & Haze Removal Network for Object Detection

In the realm of low-level vision tasks,such as image deraining and dehazing,restoring images distorted by adverse weather conditions remains a significant challenge.The emergence of abundant computational resources has driven the dominance of deep Convolutional Neural Networks(CNNs),supplanting traditional methods reliant on prior knowledge.However,the evolution of CNN architectures has tended towards increasing complexity,utilizing intricate structures to enhance performance,often at the expense of computational efficiency.In response,we propose the Selective Kernel Dense Residual M-shaped Network(SKDRMNet),a flexible solution adept at balancing computational efficiency with network accuracy.A key innovation is the incorporation of an M-shaped hierarchical structure,derived from the U-Net framework as M-Network(M-Net),within which the Selective Kernel Dense Residual Module(SDRM)is introduced to reinforce multi-scale semantic feature maps.Our methodology employs two sampling techniques-bilinear and pixel unshuffled and utilizes a multi-scale feature fusion approach to distil more robust spatial feature map information.During the reconstruction phase,feature maps of varying resolutions are seamlessly integrated,and the extracted features are effectively merged using the Selective Kernel Fusion Module(SKFM).Empirical results demonstrate the comprehensive superiority of SKDRMNet across both synthetic and real rain and haze datasets.

A Multi-Category Brain Tumor Classification Method Bases on Improved ResNet50

Brain tumor is one of the most common tumors with high mortality.Early detection is of great significance for the treatment and rehabilitation of patients.The single channel convolution layer and pool layer of traditional convolutional neural network(CNN)structure can only accept limited local context information.And most of the current methods only focus on the classification of benign and malignant brain tumors,multi classification of brain tumors is not common.In response to these shortcomings,considering that convolution kernels of different sizes can extract more comprehensive features,we put forward the multi-size convolutional kernel module.And considering that the combination of average-pooling with max-pooling can realize the complementary of the high-dimensional information extracted by the two structures,we proposed the dual-channel pooling layer.Combining the two structures with ResNet50,we proposed an improved ResNet50 CNN for the applications in multi-category brain tumor classification.We used data enhancement before training to avoid model over fitting and used five-fold cross-validation in experiments.Finally,the experimental results show that the network proposed in this paper can effectively classify healthy brain,meningioma,diffuse astrocytoma,anaplastic oligodendroglioma and glioblastoma.