Identifying threats contained within encrypted network traffic poses a great challenge to Intrusion Detection Systems (IDS). Because traditional approaches like deep packet inspection could not operate on encrypted network traffic, machine learning-based IDS is a promising solution. However, machine learning-based IDS requires enormous amounts of statistical data based on network traffic flow as input data and also demands high computing power for processing, but is slow in detecting intrusions. We propose a lightweight IDS that transforms raw network traffic into representation images. We begin by inspecting the characteristics of malicious network traffic of the CSE-CIC-IDS2018 dataset. We then adapt methods for effectively representing those characteristics into image data. A Convolutional Neural Network (CNN) based detection model is used to identify malicious traffic underlying within image data. To demonstrate the feasibility of the proposed lightweight IDS, we conduct three simulations on two datasets that contain encrypted traffic with current network attack scenarios. The experiment results show that our proposed IDS is capable of achieving 95% accuracy with a reasonable detection time while requiring relatively small size training data.
In the age of data and machine learning, massive amounts of data produced throughout our society can be rapidly delivered to various applications through a broad spectrum of cloud services. However, the spectrum of applications has vastly different data quality requirements and Willingness-To-Pay(WTP), creating a general and complex problem matching consumer quality requirements and budgets with providers' data quality and price. This paper proposes the Information-Centric Networking(ICN)-based data marketplace to foster quality-data trading service to address the challenge above. We embed a WTP mechanism into an ICN-based data broker service running on cloud computing; therefore, a data consumer can request its desired data with a data name and quality requirement. By specifying nominal WTPs, data consumers can acquire data of the desired quality at the range of maximum nominal WTP. At the same time, a data broker can offer data of a suitable quality based on the profit-optimized price and the proposed service quality using ground-truth accuracy trained by data. We demonstrate that the data broker's profit can be almost doubled by using the optimal data size and budget determined by considering the one-leader-multiple-followers Stackelberg game. These results show that a value-added data brokering service can profitably facilitate data trading.
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