A survey of methods for encrypted network traffic fingerprinting.

Privacy protection in computer communication is gaining attention because plaintext transmission without encryption can be eavesdropped on and intercepted. Accordingly, the use of encrypted communication protocols is on the rise, along with the number of cyberattacks exploiting them. Decryption is essential for preventing attacks, but it risks privacy infringement and incurs additional costs. Network fingerprinting techniques are among the best alternatives, but existing techniques are based on information from the TCP/IP stack. They are expected to be less effective because cloud-based and software-defined networks have ambiguous boundaries, and network configurations not dependent on existing IP address schemes increase. Herein, we investigate and analyze the Transport Layer Security (TLS) fingerprinting technique, a technology that can analyze and classify encrypted traffic without decryption while addressing the problems of existing network fingerprinting techniques. Background knowledge and analysis information for each TLS fingerprinting technique is presented herein. We discuss the pros and cons of two groups of techniques, fingerprint collection and artificial intelligence (AI)-based. Regarding fingerprint collection techniques, separate discussions on handshake messages ClientHello/ServerHello, statistics of handshake state transitions, and client responses are provided. For AI-based techniques, discussions on statistical, time series, and graph techniques according to feature engineering are presented. In addition, we discuss hybrid and miscellaneous techniques that combine fingerprint collection with AI techniques. Based on these discussions, we identify the need for a step-by-step analysis and control study of cryptographic traffic to effectively use each technique and present a blueprint.

Blockchain Token-Based Wild-Simulated Ginseng Quality Management Method.

Countries require measures to prevent food fraud, such as forgery of certificates or content change during production, which can occur throughout the supply chain, even if they have a certification system for quality food management. Therefore, there are recent cases of the introduction of blockchain tokens for quality and supply chain management; however, there are difficulties in introducing tokens in food fields, such as forest and agricultural products. To introduce tokens in the food sector, we selected wild-simulated ginseng, subject to quality management in Korea, analyzed the quality management process of wild-simulated ginseng, and selected the target for blockchain token introduction. We then identified potential token-related issues from consumers and suggested possible solutions.

The utility of dual-energy CT collagen material decomposition technique for the visualization of tendon grafts after knee ligament reconstruction.

OBJECTIVE: The purpose of this article is to assess whether dual-energy CT (DECT) collagen material decomposition technique could reliably depict graft integrity in patients with knee ligament reconstruction. METHODS: Seventy patients (mean age, 29.8 years; age range, 15-57 years; 61 men, 9 women) who underwent knee DECT, from June 2016 to January 2018, after knee ligament reconstruction were included in our study. A total of 92 intact tendon grafts (autograft, n = 37; allograft, n = 50), confirmed by MRI and clinical assessment or second-look arthroscopy of the operated knee, were evaluated. The type and number of reconstructed ligaments were as follows: anterior cruciate ligament (ACL) (n = 30), posterior cruciate ligament (PCL) (n = 20), medial collateral ligament (MCL) (n = 12), lateral collateral ligament (LCL) (n = 10), posterolateral ligamentous complex (PLC) (n = 7), anterolateral ligament (ALL) (n = 6), and medial patellofemoral ligament (MPFL) (n = 7). All DECT tendon-specific color mapping images were analyzed by two radiologists independently. Each reconstructed ligament was divided into proximal, middle, and distal portion and rated separately using a three-point scale (0 = absent 'dual-energy color staining'; 1 = partial 'dual-energy color staining'; 2 = full 'dual-energy color staining'). RESULTS: The mean of total visualization scores of reconstructed ligaments were 5 or more out of 6 points (PCL: 5.0 ± 0.8; MCL: 5.4 ± 0.7; LCL: 5.5 ± 0.5; PLC: 5.4 ± 1.0; ALL: 5.3 ± 0.6; MPFL: 5.8 ± 0.5), except for ACL (4.3 ± 1.7). No significant difference was observed in the mean of total visualization scores between the autografts and allografts (p > 0.05). The frequency of the score 0 was greater than 10% for the ACL group (15.7%), while less than 5% or 0% for the other groups. Overall, substantial to almost perfect interobserver agreement (range 0.71-0.93) was found for all types of ligaments. CONCLUSION: DECT collagen material decomposition technique could be a valuable tool to qualitatively display tendon grafts in the patients with knee ligament reconstruction, but more caution would be needed to assess ACL graft.