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As the application of network encryption technology expands, malicious attacks will also be protected by encryption mechanism, increasing the difficulty of detection. This paper focuses on the analysis of encrypted traffic in the network by hosting long-day encrypted traffic, coupled with a weighted algorithm commonly used in information retrieval and SSL/TLS fingerprint to detect malicious encrypted links. The experimental results show that the system proposed in this paper can identify potential malicious SSL/TLS fingerprints and malicious IP which cannot be recognized by other external threat information providers. The network packet decryption is not required to help clarify the full picture of the security incident and provide the basis of digital identification. Finally, the new threat intelligence obtained from the correlation analysis of this paper can be applied to regional joint defense or intelligence exchange between organizations. In addition, the framework adopts Google cloud platform and microservice technology to form an integrated serverless computing architecture.

Network security lacks the verification of real world net flow data, and lacks a platform to collect and integrate net flow data and threat intelligence, so as to generate an evaluation benchmark for machine learning on cybersecurity. NetFlowTotal develop many net flow analysis tools to detect malicious threats in the net flow data. Through the two-side market strategies, NetFlowTotal platform tie together two distinct groups of users in a network. One kind of user can upload net flow data to the NetFlowTotal platform to obtain security incidents reports; the other kind of user can share threat intelligence to the NetFlowTotal platform to obtain more associate threat intelligence according to global net flow data. The goal of this paper is to establish a net flow evaluation platform to provide real world dataset with security incidents reports for machine learning evaluation.

This paper introduces the common ways for pepper decorticating and the current status of their applications and effects. According to the actual needs of market development and our analysis on peppers’ physical structures, three kinds of decorticating principles are put forward which include spiral, frictional and centrifugal decorticators and their mechanisms and advantages are analyzed in detail respectively, which will be highly instructive to the research of pepper decorticating equipment.

Existing pepper fruit peeling machine for peeling incomplete, improper peeling force, pit protective film damaged, lead to a high impurity rate, pit crushing, unsatisfactory color and other issues. In order to solve these problems, manufacturing pepper fruit preprocessing machine based on analysis of pepper machining process. The machine achieves pepper fruit surface damaged by differential principle, work in with pepper peeling machine to peel completely.

Intrusion Detection Systems (IDSs) which have been deployed in computer networks to detect a wide variety of attacks are suffering how to manage of a large number of triggered alerts. Thus, reducing false alarms efficiently has become the most important issue in IDS. In this paper, we introduce the semi-supervised learning mechanism to build an alert filter, which will reduce up to 85% false alarms and still keep a high detection rate. In our semi-supervised learning approach, we only need a very small amount of label information. This will save a huge security officer's effort and make the alert filter be more practical for the real systems. Numerical comparison with conventional supervised learning approach with the same small portion labeled data, our method has significantly superior detection rate as well as in the false alarm reduction rate.