provides an end-to-end payload classification without feature extraction using CNN-based and RNN-based classification approaches to learn features from raw payloads. proposes a conceptual SDN-based security monitoring framework for the smart grid with behavioral analysis and deep learning models. Since the feature extraction in traditional machine learning is complicated and time-consuming, more and more researches pay attention to provide end-to-end detection with deep learning. The payloads inside packets are sometimes text strings indeed (e.g., web queries), and thus such packet metadata can provide high-level information. Recently, machine learning algorithms have emerged as a promising solution to identify anomalous packets, which are widely explored in many works The second category gains inspiration from machine learning and deep learning technologies. With the recognition of exponential growth of network traffic, packet inspection with high throughput as well as efficient intrusion detection is demanded. Even though pattern matching is valid for checking payloads of captured packets, this approach still remains limitations in efficiently processing large volume of traffic. In fact, whether implementing DPI modules in the data plane or in a remote proxy, these approaches usually utilize open-source DPI tools that are largely pattern-based or port-based Nevertheless, enabling application-aware functionality in the data plane will result in dramatic performance degeneration in network nodes, whose QoS demands can no longer be guaranteed Apart from traffic redirection to DPI proxies, some researches turn their eyes on developing DPI modules in OpenFlow switch (OvS) to avoid additional routing hop. In particular, extend the SDN architecture to redirect traffic to the specified DPI proxies. Specifically, network function virtualization (NFV) opens up new venues for DPI approaches by mirroring flows to specific modules. First, SDN has naturally resorted to third-party deep packet inspection (DPI) tools, e.g., OpenDPI, nDPI, and L7-filter, accomplished by incorporating DPI modules into specific vendor proprietary hardware or transforming DPI into middleboxes. Existing DPI schemes in SDN can be divided into two categories. To identify the high-level anomaly traffic, the researchers explore to provide deep packet inspection (DPI) service in the SDN paradigm. OFDPI can provide a significant improvement in detection accuracy withĪcceptable overheads. Performance and the overhead of the proposed sulotion are assessed using the Of OFDPI is implemented on the Ryu SDN controller and the Mininet platform. Instead of decrypting the encrypted traffic to weaken user privacy. Linear prediction and (ii) for encrypted packets, OFDPI extracts notableįeatures of packets and then trains a binary classifier with a decision tree, In order toīalance the detection accuracy and performance bottleneck of the SDNĬontroller, OFDPI introduces an adaptive packet sampling window based on the Regression rather than matching with specific pattern combinations. Representation and are then applied to train a binary classifier with logistic These features are concatenated into a sparse matrix Packets, OFDPI extracts the features of accessible payloads, including tri-gramįrequency based on Term Frequency and Inverted Document Frequency (TF-IDF) and
Then, OFDPI allowsįor deep packet inspection at the packet-level granularity: (i) for unencrypted The IP addresses of each new flow via OpenFlow protocols. OFDPI prescribes an early detection at the flow-level granularity by checking The SDN paradigm to provide adaptive and efficient packet inspection. OpenFlow-enabled deep packet inspection (OFDPI) approach is proposed based on Processing a large volume of data traffic. Port-based third-party DPI tools can suffer from limitations in efficiently Software-defined networking (SDN) as complicated attacks may intractably inject Deep packet inspection (DPI) has been extensively investigated in
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