User Behavior Detection Using Multi-Modal Signatures of Encrypted Network Traffic

With the development of the network environment and the emergence of new applications, network traffic has become increasingly complex. This paper focuses on user behavior detection based on encrypted traffic analysis. User behavior information plays a critical role in network management and security, leading to extensive research in this domain. This paper introduces two main contributions. Firstly, we present a categorization method for application types and a behavior definition approach for user behavior detection research. This enables consistent behavior definition for each application type, facilitating objective performance comparison with other studies in the field. Secondly, a behavior detection method based on multi-modal signatures is introduced. The multi-modal signatures represent the multiple signatures extracted from encrypted traffic, including header, SNI, and PSD signatures, which are subsequently defined as a rule. To validate the effectiveness of our proposed method, we conducted 4 experiments on 5 SaaS applications. As a result of the experiments, the proposed method achieves an F-measure of 94~99% and can detect other types of application behaviors with high performance. As this study conducts user behavior detection research based on encrypted traffic analysis, the proposed method can be applied to other research areas that utilize encrypted traffic

Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification

The classification of encrypted traffic plays a crucial role in network management and security. As encrypted network traffic becomes increasingly complicated and challenging to analyze, there is a growing need for more efficient and comprehensive analytical approaches. Our proposed method introduces a novel approach to network traffic classification, utilizing multi-task learning to simultaneously train multiple tasks within a single model. To validate the proposed method, we conducted experiments using the ISCX 2016 VPN/Non-VPN dataset, consisting of three tasks. The proposed method outperformed the majority of existing methods in classification with 99.29%, 97.38%, and 96.89% accuracy in three tasks (i.e., encapsulation, category, and application classification, respectively). The efficiency of the proposed method also demonstrated outstanding performance when compared to methods excluding lightweight models. The proposed approach demonstrates accurate and efficient multi-task classification on encrypted traffic

IGFL2-AS1, a Long Non-Coding RNA, Is Associated with Radioresistance in Colorectal Cancer

Precise prediction of radioresistance is an important factor in the treatment of colorectal cancer (CRC). To discover genes that regulate the radioresistance of CRCs, we analyzed an RNA sequencing dataset of patient-originated samples. Among various candidates, IGFL2-AS1, a long non-coding RNA (lncRNA), exhibited an expression pattern that was well correlated with radioresistance. IGFL2-AS1 is known to be highly expressed in various cancers and functions as a competing endogenous RNA. To further investigate the role of IGFL2-AS1 in radioresistance, which has not yet been studied, we assessed the amount of IGFL2-AS1 transcripts in CRC cell lines with varying degrees of radioresistance. This analysis showed that the more radioresistant the cell line, the higher the level of IGFL2-AS1 transcripts—a similar trend was observed in CRC samples. To directly assess the relationship between IGFL2-AS1 and radioresistance, we generated a CRC cell line stably expressing a small hairpin RNA (shRNA) targeting IGFL2-AS1. shRNA-mediated knockdown of IGFL2-AS1 decreased radioresistance and cell migration in vitro, establishing a functional role for IGFL2-AS1 in radioresistance. We also showed that downstream effectors of the AKT pathway played crucial roles. These data suggest that IGFL2-AS1 contributes to the acquisition of radioresistance by regulating the AKT pathway

A Patient-Derived Organoid-Based Radiosensitivity Model for the Prediction of Radiation Responses in Patients with Rectal Cancer

Patient-derived tumor organoids closely resemble original patient tumors. We conducted this co-clinical trial with treatment-naive rectal cancer patients and matched patient-derived tumor organoids to determine whether a correlation exists between experimental results obtained after irradiation in patients and organoids. Between November 2017 and March 2020, we prospectively enrolled 33 patients who were diagnosed with mid-to-lower rectal adenocarcinoma based on endoscopic biopsy findings. We constructed a prediction model through a machine learning algorithm using clinical and experimental radioresponse data. Our data confirmed that patient-derived tumor organoids closely recapitulated original tumors, both pathophysiologically and genetically. Radiation responses in patients were positively correlated with those in patient-derived tumor organoids. Our machine learning-based prediction model showed excellent performance. In the prediction model for good responders trained using the random forest algorithm, the area under the curve, accuracy, and kappa value were 0.918, 81.5%, and 0.51, respectively. In the prediction model for poor responders, the area under the curve, accuracy, and kappa value were 0.971, 92.1%, and 0.75, respectively. Our patient-derived tumor organoid-based radiosensitivity model could lead to more advanced precision medicine for treating patients with rectal cancer

Prediction of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer Patients Using Harmonized Radiomics of Multcenter ¹⁸F-FDG-PET Image

We developed machine and deep learning models to predict chemoradiotherapy in rectal cancer using 18F-FDG PET images and harmonized image features extracted from 18F-FDG PET/CT images. Patients diagnosed with pathologic T-stage III rectal cancer with a tumor size > 2 cm were treated with neoadjuvant chemoradiotherapy. Patients with rectal cancer were divided into an internal dataset (n = 116) and an external dataset obtained from a separate institution (n = 40), which were used in the model. AUC was calculated to select image features associated with radiochemotherapy response. In the external test, the machine-learning signature extracted from 18F-FDG PET image features achieved the highest accuracy and AUC value of 0.875 and 0.896. The harmonized first-order radiomics model had a higher efficiency with accuracy and an AUC of 0.771 than the second-order model in the external test. The deep learning model using the balanced dataset showed an accuracy of 0.867 in the internal test but an accuracy of 0.557 in the external test. Deep-learning models using 18F-FDG PET images must be harmonized to demonstrate reproducibility with external data. Harmonized 18F-FDG PET image features as an element of machine learning could help predict chemoradiotherapy responses in external tests with reproducibility

Anti-inflammatory effects of TP1 in LPS-induced Raw264.7 macrophages

Abstract Inflammation is an essential defense mechanism in health; however, excessive inflammation contributes to the pathophysiology of several chronic diseases. Although anti-inflammatory drugs are essential for controlling inflammation, they have several side effects. Recent findings suggest that naturally derived compounds possess physiological activities, including anti-inflammatory, antifungal, antiviral, anticancer, and immunomodulatory activities. Therefore, this study aimed to investigate the anti-inflammatory effects and molecular mechanisms of 2,5,6-trimethoxy-p-terphenyl (TP1), extracted from the Antarctic lichen Stereocaulon alpinum, using in vitro models. TP1 treatment decreased the production of nitric oxide (NO) and reactive oxygen species (ROS) in LPS-stimulated Raw264.7 macrophages. Additionally, TP1 treatment significantly decreased the mRNA levels of pro-inflammatory cytokines (IL-1β, TNF-α, IL-6) and the mRNA and protein levels of the pro-inflammatory enzymes (inducible nitric oxide synthase and cyclooxygenase-2). Moreover, TP1 suppressed lipopolysaccharide-induced phosphorylation of the NF-κB and MAPK signaling pathways in Raw264.7 macrophages. Conclusively, these results suggest that TP1 ameliorates inflammation by suppressing the expression of pro-inflammatory cytokines, making it a potential anti-inflammatory drug for the treatment of severe inflammatory diseases