No abstract

No abstract

Glioma is one of the most fatal primary brain tumors, and it is well-known for its difficulty in diagnosis and management. Medical imaging techniques such as magnetic resonance imaging (MRI), positron emission tomography (PET), and spectral imaging can efficiently aid physicians in diagnosing, treating, and evaluating patients with gliomas. With the increasing clinical records and digital images, the application of artificial intelligence (AI) based on medical imaging has reduced the burden on physicians treating gliomas even further. This review will classify AI technologies and procedures used in medical imaging analysis. Additionally, we will discuss the applications of AI in glioma, including tumor segmentation and classification, prediction of genetic markers, and prediction of treatment response and prognosis, using MRI, PET, and spectral imaging. Despite the benefits of AI in clinical applications, several issues such as data management, incomprehension, safety, clinical efficacy evaluation, and ethical or legal considerations, remain to be solved. In the future, doctors and researchers should collaborate to solve these issues, with a particular emphasis on interdisciplinary teamwork.

No abstract

No abstract

For malware detection, current state-of-the-art research concentrates on machine learning techniques. Binary n -gram OpCode features are commonly used for malicious code identification and classification with high accuracy. Binary OpCode modification is much more difficult than modification of image pixels. Traditional adversarial perturbation methods could not be applied on OpCode directly. In this paper, we propose a bidirectional universal adversarial learning method for effective binary OpCode perturbation from both benign and malicious perspectives. Benign features are those OpCodes that represent benign behaviours, while malicious features are OpCodes for malicious behaviours. From a large dataset of benign and malicious binary applications, we select the most significant benign and malicious OpCode features based on the feature SHAP value in the trained machine learning model. We implement an OpCode modification method that insert benign OpCodes into executables as garbage codes without execution and modify malicious OpCodes by equivalent replacement preserving execution semantics. The experimental results show that the benign and malicious OpCode perturbation (BMOP) method could bypass malicious code detection models based on the SVM, XGBoost, and DNN algorithms.

Fifth‐generation (5G) wireless systems provide an opportunity for improving the existing Voice over Internet Protocol communication service's user experience. To mitigate the security risk of 5G data leakage, building covert channel is an alternative approach of providing confidential data transmission. Due to the high transmission rate of 5G, the interpacket intervals become small and derandomized, this caused the encoding phase of the covert timing channel imports relatively large modulation errors. rearranging is a widespread phenomenon that is occurred over the data communications. In this paper, we propose a rearrangement covert channel approach named Hybrid Variable‐length Packet Rearrangement Covert Timing Channel (HVPR‐CTC), which artificially chooses the delimiter packets and identification (ID) packets from the overt traffics, and encodes the packet sending order between adjacent delimiter packets according to a generated hybrid variable‐length codeword dictionary, and embeds the secret message by rearranging the sending order of the ID packets. The experiments demonstrate that the HVPR‐CTC scheme can effectively perform strategy adjustment: its minimum Location Square Deviation is 0.832 and minimum Swap Deviation is 139. the maximum throughput reaches 14.37 bps, and the optimal Bit Error Rate is 3.27% and 9.70% for low‐channel noise and high‐channel noise communication conditions, respectively.

No abstract