Patent Publication Number: US-2021194894-A1

Title: Packet metadata capture in a software-defined network

Description:
TECHNICAL FIELD 
     The present disclosure relates generally to computer networks, and, more particularly, to packet metadata capture in a software-defined network (SDN). 
     BACKGROUND 
     Beyond the various types of legitimate application traffic in a network, some network traffic may also be malicious. For example, some traffic may seek to overwhelm a service by sending a large number of requests to the service. Such attacks are also sometimes known as denial of service (DoS) attacks. Other forms of malicious traffic may seek to exfiltrate sensitive information from a network, such as credit card numbers, trade secrets, and the like. Typically, such traffic is generated by a client that has been infected with malware. Thus, further types of malicious network traffic include network traffic that propagate the malware itself and network traffic that passes control commands to already infected devices. However, many instances of malware now use encryption, to conceal their network activity from detection. 
     With the proliferation of advanced machine learning techniques, it is now possible to discern the contents of encrypted network traffic, or its intent (e.g., by distinguishing between malware-related traffic and benign traffic), without actually decrypting the traffic. However, doing so still requires the collection of sufficient telemetry from the network regarding the encrypted traffic. This telemetry collection is often non-trivial and can, in some cases, actually impede on the operations of the network due to the additional overhead. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The embodiments herein may be better understood by referring to the following description in conjunction with the accompanying drawings in which like reference numerals indicate identically or functionally similar elements, of which: 
         FIGS. 1A-1B  illustrate an example communication network; 
         FIG. 2  illustrates an example network device/node; 
         FIG. 3  illustrates an example architecture for the analysis of traffic in a network; 
         FIG. 4  illustrates an example software-defined network (SDN); 
         FIG. 5  illustrates an example comparison of the full packets captured by a telemetry exporter to the packet metadata actually needed by a traffic analysis service; 
         FIG. 6  illustrates an example architecture for capturing packet metadata in an SDN; and 
         FIG. 7  illustrates an example simplified procedure for packet metadata capture in an SDN. 
     
    
    
     DESCRIPTION OF EXAMPLE EMBODIMENTS 
     Overview 
     According to one or more embodiments of the disclosure, a switch in a software-defined network receives a packet sent by an endpoint device via the SDN. The switch makes a copy of the packet based on one or more header fields of the packet matching one or more flow table entries of the switch. The switch forms telemetry data for reporting to a traffic analysis service by applying a metadata filter to the copy of the packet. The metadata filter prevents at least a portion of the copy of the packet from inclusion in the telemetry data. The switch sends the formed telemetry data to the traffic analysis service. 
     DESCRIPTION 
     A computer network is a geographically distributed collection of nodes interconnected by communication links and segments for transporting data between end nodes, such as personal computers and workstations, or other devices, such as sensors, etc. Many types of networks are available, with the types ranging from local area networks (LANs) to wide area networks (WANs). LANs typically connect the nodes over dedicated private communications links located in the same general physical location, such as a building or campus. WANs, on the other hand, typically connect geographically dispersed nodes over long-distance communications links, such as common carrier telephone lines, optical lightpaths, synchronous optical networks (SONET), or synchronous digital hierarchy (SDH) links, or Powerline Communications (PLC) such as IEEE 61334, IEEE P1901.2, and others. The Internet is an example of a WAN that connects disparate networks throughout the world, providing global communication between nodes on various networks. The nodes typically communicate over the network by exchanging discrete frames or packets of data according to predefined protocols, such as the Transmission Control Protocol/Internet Protocol (TCP/IP). In this context, a protocol consists of a set of rules defining how the nodes interact with each other. Computer networks may further be interconnected by an intermediate network node, such as a router, to extend the effective “size” of each network. 
     Smart object networks, such as sensor networks, in particular, are a specific type of network having spatially distributed autonomous devices such as sensors, actuators, etc., that cooperatively monitor physical or environmental conditions at different locations, such as, e.g., energy/power consumption, resource consumption (e.g., water/gas/etc. for advanced metering infrastructure or “AMI” applications) temperature, pressure, vibration, sound, radiation, motion, pollutants, etc. Other types of smart objects include actuators, e.g., responsible for turning on/off an engine or perform any other actions. Sensor networks, a type of smart object network, are typically shared-media networks, such as wireless networks. That is, in addition to one or more sensors, each sensor device (node) in a sensor network may generally be equipped with a radio transceiver or other communication port, a microcontroller, and an energy source, such as a battery. Often, smart object networks are considered field area networks (FANs), neighborhood area networks (NANs), personal area networks (PANs), etc. Generally, size and cost constraints on smart object nodes (e.g., sensors) result in corresponding constraints on resources such as energy, memory, computational speed and bandwidth. 
       FIG. 1A  is a schematic block diagram of an example computer network  100  illustratively comprising nodes/devices, such as a plurality of routers/devices interconnected by links or networks, as shown. For example, customer edge (CE) routers  110  may be interconnected with provider edge (PE) routers  120  (e.g., PE- 1 , PE- 2 , and PE- 3 ) in order to communicate across a core network, such as an illustrative network backbone  130 . For example, routers  110 ,  120  may be interconnected by the public Internet, a multiprotocol label switching (MPLS) virtual private network (VPN), or the like. Data packets  140  (e.g., traffic/messages) may be exchanged among the nodes/devices of the computer network  100  over links using predefined network communication protocols such as the Transmission Control Protocol/Internet Protocol (TCP/IP), User Datagram Protocol (UDP), Asynchronous Transfer Mode (ATM) protocol, Frame Relay protocol, or any other suitable protocol. Those skilled in the art will understand that any number of nodes, devices, links, etc. may be used in the computer network, and that the view shown herein is for simplicity. 
     In some implementations, a router or a set of routers may be connected to a private network (e.g., dedicated leased lines, an optical network, etc.) or a virtual private network (VPN), such as an MPLS VPN, thanks to a carrier network, via one or more links exhibiting very different network and service level agreement characteristics. For the sake of illustration, a given customer site may fall under any of the following categories: 
     1.) Site Type A: a site connected to the network (e.g., via a private or VPN link) using a single CE router and a single link, with potentially a backup link (e.g., a 3G/4G/LTE backup connection). For example, a particular CE router  110  shown in network  100  may support a given customer site, potentially also with a backup link, such as a wireless connection. 
     2.) Site Type B: a site connected to the network using two MPLS VPN links (e.g., from different service providers), with potentially a backup link (e.g., a 3G/4G/LTE connection). A site of type B may itself be of different types: 
     2a.) Site Type B1: a site connected to the network using two MPLS VPN links (e.g., from different service providers), with potentially a backup link (e.g., a 3G/4G/LTE connection). 
     2b.) Site Type B2: a site connected to the network using one MPLS VPN link and one link connected to the public Internet, with potentially a backup link (e.g., a 3G/4G/LTE connection). For example, a particular customer site may be connected to network  100  via PE- 3  and via a separate Internet connection, potentially also with a wireless backup link. 
     2c.) Site Type B3: a site connected to the network using two links connected to the public Internet, with potentially a backup link (e.g., a 3G/4G/LTE connection). 
     Notably, MPLS VPN links are usually tied to a committed service level agreement, whereas Internet links may either have no service level agreement at all or a loose service level agreement (e.g., a “Gold Package” Internet service connection that guarantees a certain level of performance to a customer site). 
     3.) Site Type C: a site of type B (e.g., types B1, B2 or B3) but with more than one CE router (e.g., a first CE router connected to one link while a second CE router is connected to the other link), and potentially a backup link (e.g., a wireless 3G/4G/LTE backup link). For example, a particular customer site may include a first CE router  110  connected to PE- 2  and a second CE router  110  connected to PE- 3 . 
       FIG. 1B  illustrates an example of network  100  in greater detail, according to various embodiments. As shown, network backbone  130  may provide connectivity between devices located in different geographical areas and/or different types of local networks. For example, network  100  may comprise local networks  160 ,  162  that include devices/nodes  10 - 16  and devices/nodes  18 - 20 , respectively, as well as a data center/cloud environment  150  that includes servers  152 - 154 . Notably, local networks  160 - 162  and data center/cloud environment  150  may be located in different geographic locations. 
     Servers  152 - 154  may include, in various embodiments, a network management server (NMS), a dynamic host configuration protocol (DHCP) server, a constrained application protocol (CoAP) server, an outage management system (OMS), an application policy infrastructure controller (APIC), an application server, etc. As would be appreciated, network  100  may include any number of local networks, data centers, cloud environments, devices/nodes, servers, etc. 
     The techniques herein may also be applied to other network topologies and configurations. For example, the techniques herein may be applied to peering points with high-speed links, data centers, etc. Further, in various embodiments, network  100  may include one or more mesh networks, such as an Internet of Things network. Loosely, the term “Internet of Things” or “IoT” refers to uniquely identifiable objects/things and their virtual representations in a network-based architecture. In particular, the next frontier in the evolution of the Internet is the ability to connect more than just computers and communications devices, but rather the ability to connect “objects” in general, such as lights, appliances, vehicles, heating, ventilating, and air-conditioning (HVAC), windows and window shades and blinds, doors, locks, etc. The “Internet of Things” thus generally refers to the interconnection of objects (e.g., smart objects), such as sensors and actuators, over a computer network (e.g., via IP), which may be the public Internet or a private network. 
     Notably, shared-media mesh networks, such as wireless networks, etc., are often on what is referred to as Low-Power and Lossy Networks (LLNs), which are a class of network in which both the routers and their interconnect are constrained. In particular, LLN routers typically operate with highly constrained resources, e.g., processing power, memory, and/or energy (battery), and their interconnections are characterized by, illustratively, high loss rates, low data rates, and/or instability. LLNs are comprised of anything from a few dozen to thousands or even millions of LLN routers, and support point-to-point traffic (e.g., between devices inside the LLN), point-to-multipoint traffic (e.g., from a central control point such at the root node to a subset of devices inside the LLN), and multipoint-to-point traffic (e.g., from devices inside the LLN towards a central control point). Often, an IoT network is implemented with an LLN-like architecture. For example, as shown, local network  160  may be an LLN in which CE- 2  operates as a root node for nodes/devices  10 - 16  in the local mesh, in some embodiments. 
       FIG. 2  is a schematic block diagram of an example node/device  200  that may be used with one or more embodiments described herein, e.g., as any of the computing devices shown in  FIGS. 1A-1B , particularly the PE routers  120 , CE routers  110 , nodes/device  10 - 20 , servers  152 - 154  (e.g., a network controller located in a data center, etc.), any other computing device that supports the operations of network  100  (e.g., switches, etc.), or any of the other devices referenced below. The device  200  may also be any other suitable type of device depending upon the type of network architecture in place, such as IoT nodes, etc. Device  200  comprises one or more network interfaces  210 , one or more processors  220 , and a memory  240  interconnected by a system bus  250 , and is powered by a power supply  260 . 
     The network interfaces  210  include the mechanical, electrical, and signaling circuitry for communicating data over physical links coupled to the network  100 . The network interfaces may be configured to transmit and/or receive data using a variety of different communication protocols. Notably, a physical network interface  210  may also be used to implement one or more virtual network interfaces, such as for virtual private network (VPN) access, known to those skilled in the art. 
     The memory  240  comprises a plurality of storage locations that are addressable by the processor(s)  220  and the network interfaces  210  for storing software programs and data structures associated with the embodiments described herein. The processor  220  may comprise necessary elements or logic adapted to execute the software programs and manipulate the data structures  245 . An operating system  242  (e.g., the Internetworking Operating System, or IOS®, of Cisco Systems, Inc., another operating system, etc.), portions of which are typically resident in memory  240  and executed by the processor(s), functionally organizes the node by, inter alia, invoking network operations in support of software processors and/or services executing on the device. These software processors and/or services may comprise a traffic analysis process  248  and/or a telemetry capture process  249 . 
     It will be apparent to those skilled in the art that other processor and memory types, including various computer-readable media, may be used to store and execute program instructions pertaining to the techniques described herein. Also, while the description illustrates various processes, it is expressly contemplated that various processes may be embodied as modules configured to operate in accordance with the techniques herein (e.g., according to the functionality of a similar process). Further, while processes may be shown and/or described separately, those skilled in the art will appreciate that processes may be routines or modules within other processes. 
     In general, traffic analysis process  248  may execute one or more machine learning-based classifiers to classify encrypted traffic in the network (and its originating application) for any number of purposes. In one embodiment, traffic analysis process  248  may assess captured telemetry data (e.g., captured by telemetry capture process  249 ) regarding one or more traffic flows, to determine whether a given traffic flow or set of flows are associated with malware in the network, such as a particular family of malware applications. Example forms of traffic that can be caused by malware may include, but are not limited to, traffic flows reporting exfiltrated data to a remote entity, spyware or ransomware-related flows, command and control (C2) traffic that oversees the operation of the deployed malware, traffic that is part of a network attack, such as a zero day attack or denial of service (DoS) attack, combinations thereof, or the like. In further embodiments, traffic analysis process  248  may classify the gathered telemetry data to detect other anomalous behaviors (e.g., malfunctioning devices, misconfigured devices, etc.), traffic pattern changes (e.g., a group of hosts begin sending significantly more or less traffic), protocol or application identification, passive operating system fingerprinting, or the like. 
     Traffic analysis process  248  may employ any number of machine learning techniques, to classify the gathered telemetry data. In general, machine learning is concerned with the design and the development of techniques that receive empirical data as input (e.g., telemetry data regarding traffic in the network) and recognize complex patterns in the input data. For example, some machine learning techniques use an underlying model M, whose parameters are optimized for minimizing the cost function associated to M, given the input data. For instance, in the context of classification, the model M may be a straight line that separates the data into two classes (e.g., labels) such that M=a*x+b*y+c and the cost function is a function of the number of misclassified points. The learning process then operates by adjusting the parameters a,b,c such that the number of misclassified points is minimal. After this optimization/learning phase, traffic analysis  248  can use the model M to classify new data points, such as information regarding new traffic flows in the network. Often, M is a statistical model, and the cost function is inversely proportional to the likelihood of M, given the input data. 
     In various embodiments, traffic analysis process  248  may employ one or more supervised, unsupervised, or semi-supervised machine learning models. Generally, supervised learning entails the use of a training set of data, as noted above, that is used to train the model to apply labels to the input data. For example, the training data may include sample telemetry data for encrypted traffic that has been labeled as “normal,” or “malware-generated.” On the other end of the spectrum are unsupervised techniques that do not require a training set of labels. Notably, while a supervised learning model may look for previously seen attack patterns that have been labeled as such, an unsupervised model may instead look to whether there are sudden changes in the behavior of the network traffic. Semi-supervised learning models take a middle ground approach that uses a greatly reduced set of labeled training data. 
     Example machine learning techniques that traffic analysis process  248  can employ may include, but are not limited to, nearest neighbor (NN) techniques (e.g., k-NN models, replicator NN models, etc.), statistical techniques (e.g., Bayesian networks, etc.), clustering techniques (e.g., k-means, mean-shift, etc.), neural networks (e.g., reservoir networks, convolutional neural networks, artificial neural networks, etc.), support vector machines (SVMs), logistic or other regression, Markov models or chains, principal component analysis (PCA) (e.g., for linear models), multi-layer perceptron (MLP) artificial neural networks (ANNs) (e.g., for non-linear models), replicating reservoir networks (e.g., for non-linear models, typically for time series), random forest classification, or the like. 
     The performance of a machine learning model can be evaluated in a number of ways based on the number of true positives, false positives, true negatives, and/or false negatives of the model. For example, the false positives of the model may refer to the number of traffic flows that are incorrectly classified as malware-associated, anomalous, etc. Conversely, the false negatives of the model may refer to the number of traffic flows that the model incorrectly classifies as normal, when actually malware-associated, anomalous, etc. True negatives and positives may refer to the number of traffic flows that the model correctly classifies as normal or malware-associated, etc., respectively. Related to these measurements are the concepts of recall and precision. Generally, recall refers to the ratio of true positives to the sum of true positives and false negatives, which quantifies the sensitivity of the model. Similarly, precision refers to the ratio of true positives the sum of true and false positives. 
     In some cases, traffic analysis process  248  may assess the captured telemetry data on a per-flow basis. In other embodiments, traffic analysis  248  may assess telemetry data for a plurality of traffic flows based on any number of different conditions. For example, traffic flows may be grouped based on their sources, destinations, temporal characteristics (e.g., flows that occur around the same time, etc.), combinations thereof, or based on any other set of flow characteristics. 
       FIG. 3  illustrates an example architecture for the analysis of traffic in a network, according to various embodiments. As shown in  FIG. 3 , assume that a network  300  includes an endpoint device  302  that communicates with another remote endpoint  304  via network  300 . For example, in many cases, endpoint device  302  may be a client device that communicates with a remote server or service via the network  300 . More specifically, endpoint device  302  may form a traffic session with endpoint  304  and send traffic flow  308  towards endpoint  304  via the network. 
     Located along the network path between endpoint device  302  and endpoint  304  may be any number of telemetry exporters, such as telemetry exporter  306 . For example, telemetry exporter  306  may be a switch, router, firewall, server, network controller, or other networking equipment via which traffic flow  308  sent between endpoint device  302  end endpoint  304  flows. During operation, traffic telemetry exporter  306  may capture data regarding traffic flow  308 , generate traffic telemetry data  312  based on the captured data, and send traffic telemetry data  312  to traffic analysis service  310  for assessment. For example, traffic telemetry data  312  may include Internet Protocol Flow Information Export (IPFIX) records and/or Netflow records regarding traffic flow  308 . In further cases, traffic telemetry data  312  may include one or more captured packets from traffic flow  308 , such as the first n-number of data packets of flow  308 . 
     More specifically, telemetry exporter  306  may analyze the packet headers of traffic flow  308 , and any associated flows in the opposite direction), to capture feature information about the traffic. For example, telemetry exporter  306  may capture the source address and/or port of endpoint device  304 , the destination address and/or port of endpoint  304 , the protocol(s) used by the packets of traffic flow  308 , or other header information by analyzing the header of a given packet in traffic flow  308 . Example captured features may include, but are not limited to, Transport Layer Security (TLS) information (e.g., from a TLS handshake), such as the ciphersuite offered, user agent, TLS extensions (e.g., type of encryption used, the encryption key exchange mechanism, the encryption authentication type, etc.), HTTP information (e.g., URI, etc.), Domain Name System (DNS) information, or any other data features that can be extracted from the observed traffic flow  308 . 
     In further embodiments, telemetry exporter  306  may also assess the payload of the packet to capture information about the traffic flow. For example, telemetry exporter  306  may perform deep packet inspection (DPI) on one or more of the packets of traffic flow  308 , to assess the contents of the packet. Doing so may, for example, yield additional information that can be used to determine the application associated with the traffic flow (e.g., the packets of flow  308  were sent by a web browser of endpoint device  304 , traffic flow  308  was sent by a videoconferencing application, etc.). However, as would be appreciated, a traffic flow may also be encrypted, thus preventing telemetry exporter  306  from assessing the actual payload of the packet. In such cases, the characteristics of the application can instead be inferred from the captured header information. 
     Telemetry exporter  306  may also compute any number of statistics or metrics regarding the traffic flow  308  for inclusion in traffic telemetry data  312 . For example, telemetry exporter  306  may determine the start time, end time, duration, packet size(s), the distribution of bytes within flow  308 , etc. In further examples, telemetry exporter  306  may capture sequence of packet lengths and time (SPLT) data regarding the traffic flow  308 , sequence of application lengths and time (SALT) data regarding the traffic flow  308 , or byte distribution (BD) data regarding traffic flow  308 . 
     In various embodiments, network  300  may also include a traffic analysis service  310  that is implemented by one or more devices in network  300  through the execution of traffic analysis process  248 . For example, in some cases, traffic analysis service  310  may be implemented by one or more devices in the local network of endpoint device  302 . However, in further cases, traffic analysis service  310  may be implemented as a cloud service that is in communication with telemetry exporter  306  and endpoint device  302 , either directly or indirectly. 
     During operation, traffic analysis service  310  may make any number of assessments of traffic flow  308 . Notably, the characteristics of flow  308  can be used as input to one or more machine learning-based classifiers that are configured to make assessments such as whether flow  308  is malware-related (e.g., is propagating malware or malware commands), is attempting to exfiltrate data from the local network of client device  302 , whether traffic flow  308  is using authorized security parameters (e.g., a particular TLS version, etc.) as part of a crypto audit, or for other determinations. A key aspect of the techniques herein is that the application of a machine learning classifier to telemetry data  312  allows traffic analysis service  310  to make inferences about traffic flow  308 , even if the traffic between endpoint device  302  and endpoint  304  is encrypted. Indeed, the captured packet header information, packet timing information, and the like, traffic analysis service  310  may determine whether the traffic is malicious, is associated with a particular application, or the like. 
     Based on the assessment of traffic flow  308 , traffic analysis service  312  may cause any number of mitigation actions to be performed in network  300 . For example, traffic analysis service  312  may block or drop traffic flow  308 . In more extreme cases, traffic analysis service  312  may prevent all future traffic in network  300  associated with endpoint device  302  and/or endpoint  304 . In yet another example, traffic analysis service  312  may send a notification to a user interface that is indicative of the assessment of traffic flow  308  by traffic analysis service  312 . For example, traffic analysis service  312  may notify a network administrator, if endpoint device  302  is suspected of being infected with malware. 
     As noted above, the collection of telemetry data regarding encrypted traffic creates additional overhead on the network. This is particularly true in the case in which a telemetry exporter simply captures copies of the encrypted packets and exports the packets for analysis. For example, this is typically done for the initial n-number of data packets of a flow (e.g., the first ten packets), which include valuable information for purposes of classifying the encrypted traffic. 
     Many network implementations are shifting towards software-defined networking, which presents both challenges and opportunities with respect to capturing and exporting traffic telemetry data. A key distinction between a traditional network and a software-defined network (SDN) is that control plane decisions are centralized in an SDN with an SDN controller, while control plane decisions in a traditional network are made in a decentralized manner. By centralizing the management of the network with a controller, control plane decisions can be made in an SDN in a more intelligent manner that takes into account the entire network. 
     Packet Metadata Capture in a Software-Defined Network (SDN) 
     The techniques herein introduce a telemetry collection system for an SDN that allows for the collection of packet metadata in a selective, intelligent, and context-aware manner, and without requiring a separate representation/format for the metadata, Through the use of a packet metadata filter, only the metadata needed by the traffic analysis service is reported, and stored, greatly reducing the resource consumption by telemetry collection system. 
     Specifically, according to one or more embodiments of the disclosure as described in detail below, a switch in a software-defined network receives a packet sent by an endpoint device via the SDN. The switch makes a copy of the packet based on one or more header fields of the packet matching one or more flow table entries of the switch. The switch forms telemetry data for reporting to a traffic analysis service by applying a metadata filter to the copy of the packet. The metadata filter prevents at least a portion of the copy of the packet from inclusion in the telemetry data. The switch sends the formed telemetry data to the traffic analysis service. 
     Illustratively, the techniques described herein may be performed by hardware, software, and/or firmware, such as in accordance with the traffic analysis process  248  and telemetry capture process  249 , which may include computer executable instructions executed by the processor  220  (or independent processor of interfaces  210 ) to perform functions relating to the techniques described herein. 
     Operationally,  FIG. 4  illustrates an example SDN  400 . As shown, SDN  400  may comprise any number of endpoints  406 , any number of switches (e.g., switches  402 - 404 ), and an SDN controller  408 . A key feature of an SDN architecture is that control plane decisions are made in a centralized manner by controller  408 . Data plane decisions are still made at the switch level, as in a traditional network. 
     Each of switches  402 - 404  may include one or more flow tables  412 ,  414 , respectively, that are configured by controller  408  via secure communication links  410  between controller  408  and switches  402 - 404 . For example, communication links  410  may comprise OpenFlow channels that are encrypted using TLS. Each of the flow tables  412 - 414  include a set of flow entries, with each flow entry indicating the following:
         Match field(s)—these are used by the switch to identify specific types of packets that include header fields that match those of the match fields in the flow entry.   Counter(s)—these are maintained by the switch to track the number of packets that match the flow entry.   Instruction(s)—these specify what actions the switch should take with respect to packets that match the match field(s) of the flow entry. For example, the instruction(s) in a certain flow entry may specify the port via which the switch should forward a matching packet, any changes that the switch should make to the packet (e.g., swapping headers, etc.), and the like. In some cases, the switch may also perform the instructions with the aid of a group table that maintains sets of ‘action buckets’ that are applied to the matching packets.       

     In the case in which a switch  402 - 404  receives a packet that does not match any flow entry, the switch may take a default action with respect to the packet. For example, the switch may forward the packet to controller  408 , drop the packet, or take any other configured default action. 
     With respect to capturing and exporting telemetry data in an SDN, IPFIX and Netflow can be configured to record summary representations of the traffic. For example, such record information may indicate the source and destination IP addresses, TCP/UDP ports, the start and stop times of each session, protocol information, as well as byte and packet counts. This summary data is quite compact relative to the full packets and is sufficient for some types of traffic analysis. 
     Unfortunately, many types of traffic analysis require more information than that afforded by IPFIX and Netflow records. For example, passive DNS monitoring, TLS client fingerprinting, and the other traffic classification tasks above all fall within this category. Indeed, certain machine learning-based traffic analysis services today also require the export of packet copies to the service for the first n-number of packets of a traffic flow. Typically, the first ten or so packets are sufficient for purposes of classifying the traffic. Thus, one challenge in an SDN is how to export the packet copies to the traffic analysis service and in a manner that does not deplete network resources. 
     A key observation herein is that, while the exporting of full packet copies to a traffic analysis service enables the service to perform certain types of traffic classifications, much of the exported packets are largely ignored during the analysis. For example,  FIG. 5  illustrates an example  500  comparing the full packets  502  captured by a telemetry exporter to the packet metadata  504  actually needed by the traffic analysis service. Indeed, network session metadata such as TCP/IP headers, HTTP headers, TLS handshake information, and DNS responses is information-rich and can be useful for traffic analysis purposes such as detecting malware, operating system fingerprinting, application identification, etc. Another important type of metadata for traffic analysis is message information such as message headers and the lengths, types, and arrival times of messages. 
       FIG. 6  illustrates an example architecture  600  for capturing packet metadata in an SDN, according to various embodiments. As shown, a given switch, such as SDN switch  402 , may include the following components: a flow table  412 , ports  602   a - 602   b , and telemetry capture process  249 , in various embodiments. In further implementations, the functionalities of these components may be combined or omitted, as desired. In addition, these components may be implemented by a singular device or multiple devices operating in conjunction with one another. 
     During operation, assume that switch  402  receives a packet  604  sent by an endpoint device in the SDN towards another endpoint device via switch  402 . In addition to the forwarding operations that switch  402  may perform with respect to packet  604 , switch  402  may be further configured as a telemetry exporter to provide traffic telemetry data  606  to a traffic analysis service for classification. 
     In various embodiments, switch  402  can be enabled as a telemetry exporter in the SDN, in part, through the use of specialized flow entries in flow table  412 . More specifically, the SDN controller overseeing switch  402  in the SDN may create one or more flow entries in flow table  412  of switch  402  to indicate the types/characteristics of packets that are to be captured by switch  402  for purposes of reporting telemetry data  606  to a traffic analysis service for analysis. More specifically, the SDN controller overseeing switch  402  may specify the header field(s) or other packet characteristics of a packet to be captured as match field(s) of a flow entry in flow table  412 . In further embodiments, the flow entries in flow table  412  set by the controller for purposes of telemetry reporting may further specify that such packets should be copied. 
     By way of example, assume that packet  604  is an initial data packet of a TCP handshake and that an entry in flow table  412  indicates that the packet should be copied. In such a case, switch  402  may create a packet copy  604   a  for further processing by telemetry capture process  249 . With respect to the original packet  604 , another entry in flow table  412  may cause switch  402  to process packet  604  as normal. For example, as shown, switch  402  may send packet  604  on to port  602   a  for forwarding towards the intended destination of packet  604 . 
     As would be appreciated, the match fields and flow entries in flow table  412  can be configured to capture packet copies of packets having any desired packet type. For example, flow entries in flow table  412  may be configured to copy all packets with TCP headers, UDP headers, TLS headers, HTTP headers, DNS headers, or the like. In some cases, the match field(s) of a flow entry in flow table  412  may further specify that only certain subsets of such packets should be copied. For example, only the first n-number of packets of a flow should be captured, only headers having certain header values should be captured (e.g., specific IP addresses, certain TLS extensions, etc.). 
     In general, telemetry capture process  249  is configured to form telemetry data  606  for reporting to the traffic analysis service, based on the captured packet copies. In the simplest case, this may entail telemetry capture process  249  simply including packet copy  604   a  in telemetry data  606 , which is forwarded on to the traffic analysis service via port  602   b . However, as noted, the traffic analysis service may not actually require the entirety of packet  604 , but may only need certain header metadata from packet copy  604   a , to classify the traffic. 
     According to various embodiments, telemetry capture process  249  may apply a packet metadata filter to packet copy  604   a , to form telemetry data  606 , according to a corresponding filter policy. Such a packet metadata filter may function as a truncating filter that, given a packet copy  604   a , truncates it to an appropriate length, resulting in a truncated version of the packet  604  for inclusion in telemetry data  606 . 
     Since the truncated packet copies sent to the traffic analysis service as part of telemetry data  606  are much smaller in size than that of the complete packet copies, the overall resources consumed by the telemetry exporting mechanism will be greatly reduced. Indeed, the packet data discarded by the packet metadata filter of telemetry capture process  249  may be much larger in size than the remaining metadata of the packet. For example, telemetry capture process  249  may discard sensitive, private information (e.g., the body of an HTTP message) or unintelligible data, such as the ciphertext fields/encrypted payloads of encrypted packets. 
     In addition, traffic metadata is often less sensitive in nature than the contents of a given packet copy  604   a , making it easier to anonymize. When user privacy is a concern, in some embodiments, the packet metadata filter of telemetry capture process  249  may also apply an anonymization technique to the truncated packet copy, such as by encrypting the address information in the truncated packet. 
     To more formally describe the packet metadata filtering operations of telemetry capture process  249  assume that a given packet can be represented as a byte string. In such a case, let P n  denote the set of all possible packets with lengths up to n. A packet sequence p 1 , p 2 , . . . , p l , p 1 , p 2 , . . . , p l ϵP n   l  represents a unidirectional communication between a sender and one or more receivers, whose identities are excluded from this notation for clarity. When p is a byte string, p[i] denotes its i th  byte, wherein the first byte is indexed 0, and p[i:j] denotes the substring of bytes i through j. 
     Generally, the packet sequences of interest for purposes of traffic classification by a traffic analysis service are those sent in a single flow, which are logically associated and all share the same flow key and occur within the same timespan. A function k:P n →K maps a packet to an element of the set K of flow keys. For a packet flow p 1 , p 2 , . . . , p l , k(p 1 )=k (p 2 )= . . . k(p l ). Informally, the value returned by the key function serves as a label that identifies the flow to which a packet belongs. For conventional TCP/IP, UDP/IP, and ICMP, k is 5-tuple consisting of the IP source and destination addresses, IP protocol number, and TCP/UDP source and destination ports. For non-TCP/UDP protocols, the ports are nil. 
     Each traffic flow may have a flow record associated with it that stores flow-specific state information. Accordingly, let r j  denote a flow record with key j and R denote the set of possible records. In some embodiments, the packet metadata filter of telemetry capture process  249  may be configured to store state information within such flow records, for purposes of applying filtering to the packet copies. Alternatively, the packet metadata filter of telemetry capture process  249  may operate in a stateless manner, in a further embodiment. Further, a given flow record can store the capturing/done state within a single bit. However, it is sometimes desirable to avoid storing even a single bit per flow (e.g., because the amount of available fast memory is insufficient, etc.). To further reduce the amount of state required in stateful filtering by the metadata filter, while not accidentally discarding metadata, the filter could employ the use of a Bloom filter to record when a flow is in a packet ‘capturing’ state or a ‘done’ state, in one embodiment. Then, when a flow enters a capturing state, the filter may increment the Bloom counter and decrement the counter, when entering the done state. 
     A packet metadata filter policy may be formally defined as a function ƒ:P n xR→{1,0} that defines which packets of filter. When applying the filter policy to a given packet copy  604   a , the packet metadata filter of telemetry capture process  249  may effectively apply a truncation function g:P n xR→{1, n} that indicates how many bytes of the prefix of the packet are of interest. 
     In addition, let protocol G represent a probabilistic source of packets with memory. That is, G is determined by the conditional distribution P G [p i |p 1 , p 2 , . . . , p i-1 ]. If I represents the set of known protocols, a protocol identification function h e :P n   l →I* will then indicate that one or more protocols that match a sequence of 1-number of packets. This function has the property that h(p 1 , p 2 , . . . , p l )=G for any p 1 , p 2 , . . . , p l  where P G [p i |p i , p 2 , . . . , p l ]≥ε. Also implicit to this definition is the fact that multiple protocols may generate the same packet sequence. Therefore, the parameter ε allows the packet metadata filter of process  249  to ignore ones whose likelihood is low. Note that this formalization neglects the arrival times of the packets and also implicitly assumes that all of the packets are observed within a limited timespan. 
     For a protocol G, the set of possible metadata elements is denoted M, and the metadata extracting function of the packet metadata filter of telemetry capture process  249  can be formalized as e G :p 1 , p 2 , . . . , p l →M G . Pseudocode for the application of the protocol identification function by the packet metadata filter of telemetry capture process  249  is as follows:
         Return true if Ω&lt;Ω′
           G←h ε (p 1 , p 2 , . . . , p l )   return e G (p 1 , p 2 , . . . , p l )   
               

     In some embodiments, the packet metadata filter of telemetry capture process  249  may combine truncation functions, to filter a packet copy  604   a . Formally, for any two truncation functions g a , g b :P n xR→{0, n}, their conjugation can be denoted as g a ∧g b  and is defined as g c =g a ∧g b  where g c (p)=max(g a (p), g b (p)). That is, the conjugation of two truncation functions is one that accepts as many bytes of a packet as either of them. 
     Example filter policies for the packet metadata filter of telemetry capture process  249  may include, but are not limited to, the following: 
     
       
         
           
               
               
             
               
                 TABLE 1 
               
               
                   
               
               
                 Name 
                 Metadata Included 
               
               
                   
               
             
            
               
                 Minimal TCP 
                 IP + TCP headers (SYN, FIN, RST only) 
               
               
                 Full TCP 
                 IP + TCP headers (all) 
               
               
                 TCP Initial Message 
                 TCP data before ACK advance, in each 
               
               
                   
                 direction 
               
               
                 Minimal [D]TLS 
                 Handshake records 
               
               
                 Full [D]TLS 
                 Handshake records, all record types and 
               
               
                   
                 lengths 
               
               
                 HTTP 
                 Version, command, headers, magic 
               
               
                 UDP 
                 IP + UDP headers 
               
               
                 DNS 
                 IP + UDP headers, DNS responses 
               
               
                   
               
            
           
         
       
     
     In various embodiments, the configuration of a filter policy for telemetry capture process  249  can be achieved in a number of ways. In some embodiments, the SDN controller overseeing switch  402  may provide a filter policy to switch  402 , in addition to the corresponding flow entry for flow table  412 . In other embodiments, the filter policy for telemetry capture process  249  may be provided to switch  402  by the traffic analysis service or another supervisory service for switch  402 . For example, if changes are made to the machine learning classifier of the traffic analysis service (e.g., to assess a different input dataset), the service may send an updated filter policy to switch  402 . 
     From Table 1 above, the HTTP metadata filter policy may require the HTTP command, protocol, and headers from heat request, as well as the ‘magic’ first several bytes of the body and similar data for each response. The DNS metadata filter policy may require the entirety of each DNS response packet and none of the request packets. For the [D]TLS policies, minimal and full policies are defined. Under the minimal policy, the packet metadata filter of telemetry capture process  249  may capture only the ContentType values of packets of the handshake, which includes clientHello, serverHello, and clientKeyExchange values, as well as change_cipher_spec, and alert values. The full [D]TLS policy may include the ContentType, Protocol Version, and length fields from each TLSCiphertext record that appears at the beginning of a TCP Data filed. Those TLS fields comprise the first five bytes of the record. Note that a single TLS record may span multiple TCP packets, or multiple records may appear in a single packet. In general, TLS records are not guaranteed to appear at the start of a TCP Data field, meaning that the packet metadata filter of telemetry capture process  249  may parse all of the records by moving forward as per the length field of the previous record. The TLS policies may similarly be divided into full and minimal policies whereby the full policy requires the TCP/IP headers from each packet, including IP and TCP options, while the minimal TLS policy may require that data only for the packet copy  604   a  for which the SYN, FIN, and RST flags are set. 
     The TCP initial message policy may be applied by telemetry capture process  249  to all packets captured in the client-to-server direction that contain one or more bytes of the initial application message, and the equivalent server-to-client packets. The packet metadata filter of telemetry capture process  249  may identify such packets based on the assumption that when a TCP-based application protocol is used synchronously, all of the TCP packets in the same direction share the same Acknowledgement number as part of the same application message. Pseudocode for application of the TCP initial message policy by the packet metadata filter of telemetry capture process  249  is as follows, in one embodiment: 
     
       
         
           
               
               
             
               
                   
                   
               
             
            
               
                   
                 if the packet is the first in a flow then 
               
            
           
           
               
               
            
               
                   
                 set S and A to nil 
               
            
           
           
               
               
            
               
                   
                 endif 
               
               
                   
                 if p.SYN = 1 then 
               
            
           
           
               
               
            
               
                   
                 S ← p.S 
               
            
           
           
               
               
            
               
                   
                 return #p 
               
               
                   
                 endif 
               
               
                   
                 if p.ACK = 1 then 
               
            
           
           
               
               
            
               
                   
                 A ← p.A 
               
            
           
           
               
               
            
               
                   
                 endif 
               
               
                   
                 if p.ACK = 0 and (A=nil or p.A = A) then 
               
            
           
           
               
               
            
               
                   
                 return #p 
               
            
           
           
               
               
            
               
                   
                 endif 
               
               
                   
                 if p.S &gt; S and p.A = A then 
               
            
           
           
               
               
            
               
                   
                 return #p 
               
            
           
           
               
               
            
               
                   
                 endif 
               
            
           
           
               
               
            
               
                   
                 return 0 
               
               
                   
                   
               
            
           
         
       
     
     More specifically, a TCP initial message filter policy may cause telemetry capture process  249  to include, for each TCP session between endpoints, the initial message in each direction, as part of telemetry data  606 . As a result, telemetry data  606  may include all of the data for messages that extend over multiple TCP/IP packets. This is particularly important for purposes of analyzing traffic that uses TLS or HTTP, but can also be useful for the analysis of traffic that uses unknown protocols, as well. In the case of TLS, for example, the most important initial message for purposes of analysis is the TLS serverHello/serverCertificate. 
     In various embodiments, switch  402  may enforce a TCP initial message filter policy by applying either or both of the following mechanisms:
         A Boolean filter—this filter may simply control whether a particular TCP packet is included in telemetry data  606  without alteration. This can be applied either at the flow table level or, alternatively, at telemetry capture process  249  (e.g., by dropping all but the initial packets of a TCP flow).   A reconstruction filter—in this case, the packet metadata filter of telemetry capture process  249  may merge together the data segments of all of the TCP/IP packet copies spanned by the initial message, creating a single TCP/IP packet that represents what the endpoint would have sent if it hadn&#39;t needed to fragment the packet.       

     Generally speaking, a Boolean filter may be simpler and perform better, while a reconstruction filter provides more functionality. In one embodiment, if the Boolean filter is confused due to packet loss, retransmissions, etc., it may default to a ‘fail open’ mode whereby it gathers all initial packets, even if doing so means capturing spurious packets, as well. 
     As would be appreciated, in a synchronous TCP connection, the server listens while the client talks, and vice-versa. When a client talks, it sends TCP packets with incrementing Sequence Number (Seq) fields, and the server sends TCP packets with incrementing Acknowledgement Number (Ack) fields (and unchanging sequence numbers and zero-length data fields) to indicate that it heard the message. Server-to-client Seq and Ack values are mirror images of the client-to-server Seq and Ack values. In other words, to acknowledge receipt of a packet with Seq=S, a packet with Ack=S is sent. The SYN flag is considered to logically take up one byte of the TCP data stream. Typical TCP behavior is to use a ‘relative sequence number’ in which the Seq and Ack that have their initial values subtracted out. 
     A TCP message is defined as the set of TCP/IP packets for which the ACK flag is set, the Ack value is constant, and the Seq is incrementing. In the TCP initial message, the relative Ack of the first packet is equal to 1, or the relative Seq of the first packet is equal to 1, or both. In a typical session, the client&#39;s initial message has both the relative Seq and Ack of the first packet equal to one, and the server&#39;s initial message has only the Seq equal to 1. 
     To identify the initial message from the server, switch  402  may simply look at the relative sequence number of a packet. If the relative sequence number is ‘1,’ then telemetry capture process  249  (or a flow entry in flow table  412 ) may opt to capture that packet. Similarly, subsequent packets of the initial server message can be identified based on their acknowledgement numbers matching that of the first message packet from the server. The following illustrates an example TCP session, omitting the TCP handshake: 
     
       
         
           
               
               
               
               
               
               
             
               
                 TABLE 2 
               
               
                   
               
               
                 Src 
                 Dst 
                 Seq 
                 Ack 
                 Len 
                 Notes 
               
               
                   
               
             
            
               
                   
               
            
           
           
               
               
               
               
               
               
            
               
                 42708 
                 443 
                 1 
                 1 
                 313 
                 client initial message (1 of 1) 
               
               
                 443 
                 42708 
                 1 
                 313 
                 0 
               
               
                 443 
                 42708 
                 1 
                 313 
                 1460 
                 server initial message (1 of 3) 
               
               
                 42708 
                 443 
                 314 
                 1460 
                 0 
               
               
                 443 
                 42708 
                 1461 
                 313 
                 1036 
                 server initial message (2 of 3) 
               
               
                 42708 
                 443 
                 314 
                 2496 
                 0 
               
               
                 443 
                 42708 
                 2497 
                 313 
                 793 
                 server initial message (3 of 3) 
               
               
                 42708 
                 443 
                 314 
                 3289 
                 0 
               
               
                 42708 
                 443 
                 314 
                 3289 
                 126 
               
               
                   
               
            
           
         
       
     
     The Seq and Ack fields use arithmetic modulo 2 32 . Thus, the following preprocessor definitions can be used to compare those fields, in some embodiments:
         #define LT(X, Y) ((int)((X)−(Y))&lt;0)   #define LEQ(X, Y) ((int)((X)−(Y))&lt;=0)   #define GT(X, Y) ((int)((X)−(Y))&gt;0)   #define GEQ(X, Y) ((int)((X)−(Y))&gt;=0)       

     A retransmitted TCP packet can be ignored by switch  402 , if its data field has the same length as the previous packet, but it is important to check for that. In addition, a TCP packet containing a retransmission of a previous segment could also contain additional data, which may also be checked. 
     In one embodiment, a Boolean filter can be implemented by tracking the Seq and Ack numbers for each active traffic flow. This will require a large hash table, which is indexed using a hash of the flow key. It may be useful to have a hash function definition that is symmetric in how it handles addresses and ports, so that both the client-to-server and server-to-client flow keys hash to the same data structure. Doing so could minimize storage and help to reduce computation. In addition, each worker thread may maintain its own flow table, to avoid read/write contention. 
     In further embodiments, switch  402  may identify the initial messages of protocols of interest (e.g., TLS, HTTP) and parse enough of the packets to determine whether they are complete or not. Such a mechanism is referred to herein as a ‘completion test,’ and can be implemented with relatively minimal packet parsing. For TLS packets, this requires only understanding the TLS Record type and length fields, and for HTTP packets, involves only scanning for the 0x0D0A0D0A four-byte sequence. For example, a protocol-aware filter of process  249  can identify a TCP packet copy containing a TLS serverHello and serverCertificate message, test to see whether it is complete, and if not, create a flow table entry in flow table  412  that indicates that additional packets in the flow are needed. The flow table  412  is checked to see whether additional packets are needed in a given flow, and those packets are checked to see if they complete the TCP message. If they do, then process  249  may delete the flow table entry in table  412 . 
     Generally speaking, a protocol-aware approach might offer better performance and scalability, as compared with a protocol-agnostic approach that only looks at TCP headers, because its flow table is much smaller. In addition, the flow table will include an entry for a flow only during the handshake and only if the initial message was not in a single packet. The latency of looking up a flow table entry for each packet is the dominating cost for many packet-processing systems, so it would be a big performance boost to fit the entire flow table into the memory cache. 
     Truncation of a copy of a packet copy  604   a  by the packet metadata filter of telemetry capture process  249  generally requires that it have enough awareness about the protocol(s) in its applied filter policy to be able to identify the start of each new session. To do so, in various embodiments, the packet metadata filter of telemetry capture process  249  may attempt to match a given packet copy  604   a  against a pattern using a rooted keyword tree, with each node of the tree being associated with a single byte of one or more keywords. This approach minimizes the number of operations that it needs to perform for the match. In another embodiment, the filter may employ a multiple string-matching implementation, for protocol identification. In yet another embodiment, filter may employ a ‘mask and match’ scheme to test the equality of substrings that appear in the first x-number of bytes of a payload (e.g., first 8 or 16 bytes). Example strings that filter  602  may employ for protocol identification may include, but are not limited to, any or all of the following: 
     
       
         
           
               
               
               
               
             
               
                 TABLE 3 
               
               
                   
               
               
                 Protocol 
                 Direction 
                 Hex String 
                 Notes 
               
               
                   
               
             
            
               
                 TLS (443) 
                 C 
                 160301****01 
                 ClientHello v1.0 
               
               
                   
                   
                 160302****01 
                 ClientHello v1.1 
               
               
                   
                   
                 160303****01 
                 ClientHello v1.2 
               
               
                   
                   
                 160301****02 
                 ClientHello v1.0 
               
               
                   
                   
                 160302****02 
                 ClientHello v1.1 
               
               
                   
                   
                 160303****02 
                 ClientHello v1.2 
               
               
                 HTTP (80) 
                 C 
                 47455426 
                 ‘GET’ 
               
               
                   
                   
                 504f535420 
                 ‘POST’ 
               
               
                   
                   
                 4f5054494f4e5320 
                 ‘OPTIONS’ 
               
               
                   
                   
                 4845414420 
                 ‘HEAD’ 
               
               
                   
                   
                 50555420 
                 ‘PUT’ 
               
               
                   
                   
                 44454c45544520 
                 ‘DELETE’ 
               
               
                   
                   
                 545241434520 
                 ‘TRACE’ 
               
               
                   
                   
                 434f4e4e45435420 
                 ‘CONNECT’ 
               
               
                   
                 S 
                 485454502f312e3120 
                 ‘HTTP/1.1’ 
               
               
                 SSH (22) 
                 — 
                 5353482d322e302d 
                 ‘SSH-2.0’ 
               
               
                 DNS (53) 
                 C 
                 ****010000010000 
                 Query 
               
               
                 DNS (53) 
                 S 
                 ****818000010000 
                 Response 
               
               
                 DHCP (67) 
                 C 
                 01010600 
                 REQUEST 
               
               
                 DHCP (68) 
                 S 
                 02010600 
                 REPLY 
               
               
                   
               
            
           
         
       
     
     The start of a new TCP flow can be recognized by the packet metadata filter of telemetry capture process  249  by the SYN flag in the handshake. When a TCP SYN or SYN/ACK packet copy  604   a  is observed, the packet metadata filter of telemetry capture process  249  may create a new flow record and record the (initial) sequence number. When a non-SYN packet copy  604   a  is observed, process  249  may process the TCP payload as follows. First, process  249  may apply protocol identification to the packet copy  604   a . If the protocol is TLS, then filter  602  may parse the packet copy  604   a  as a stream of records. If the length of the record exceeds that of the packet copy  604   a , filter  602  may store the next sequence (NextSEQ) at which record-parsing should resume. Filter  602  may then keep a record of the highest TCP sequence number that has been processed (accepted) so far. If a TCP packet copy  604   a  then arrives with a lower sequence number, the packet metadata filter of telemetry capture process  249  may copy that packet in its entirety for inclusion in telemetry data  606 . 
     Stateless filtering can also be implemented by copying the entire packet, if the initial 16-byte prefix of the TCP payload is in the following 97-character character-set that can appear in HTTP headers of packets:
         , !, ″, #, $, %, &amp;, ′, (,), *, +, ,, -, ., /, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, :, ;, &lt;, =, &gt;, ?, @, A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P, Q, R, S, T, U, V, W, X, Y, Z, [,], ∧, ′, a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z, {, |,}, ˜,
 
or if that prefix consists of HTTPS characters up to the pattern CRLFCRLF. Otherwise, process  249  may truncate the packet copy  604   a . Because most HTTP 1.1 traffic uses a compressed encoding, this will capture all of the HTTP headers, while discarding almost all traffic with a compressed encoding (e.g., a 16-byte prefix). HTTP text encoding, however, may be copied. Process  249  can also implement a stateless filter for TLS traffic using a simple rule: copy only packets other than those whose TCP or UDP data fields start with the pattern  17030 , which indicates TLS data records. A similar pattern can be used by filter  602  to match SSL, as well.
       

     Note that there are two ways in which the packet metadata filter of telemetry capture process  249  may fail, when presented with a given packet copy  604   a  containing l≥0 bytes of metadata. In a first case, filter  602  could return m&gt;1 bytes, in which case p[m−l:m] is the residual data. In a second case, it could return m such that 0≤m≤1, in which case p[m:l] is referred to herein as lost data. The efficiency of the truncation by the packet metadata filter of telemetry capture process  249  can then be quantified in terms of the expected number of bytes of residual data and its failure rate by the expected number of bytes of lost data with respect to a particular traffic distribution. 
     In one embodiment, telemetry data  606  may take the form of a packet capture (PCAP) file that includes the metadata specified by the filter policy applied by process  249  and a minimum of other data. Such a PCAP file may store the sequence of packet copies processed by telemetry capture process  249 , which may have been truncated during processing, each of which may be associated with a timestamp and an indication of the number of bytes in the packet and how many bytes were actually captured and copied. 
     In another embodiment, telemetry data  606  may be flow-organized such that a stream of packets from multiple flows are captured and then multiplexed into multiple streams of packets, each containing packets from a single flow. To do so, process  249  may apply its filtering to a flow-organized capture, or by reversing those steps. When the filter policy includes the network or transport headers for many packets, process  249  may also apply header compression to the packets in each packet flow. Alternatively, process  249  may apply a compression algorithm, such as DEFLATE, to all of the packet data in the packet flow reported via telemetry data  606 . For example, in one embodiment, process  249  may apply header compression to the outputs of its packet metadata filter and/or by compressing the entire output telemetry data  606 , such as by forming a .gz file, .zip file, or the like. 
     As a result of the techniques herein, telemetry data  606  may include the packet metadata (e.g., header information, etc.) of packets captured by switch  402  that is needed by traffic analysis service and in a truncated manner. This greatly reduces the amount of telemetry data conveyed across the network and eliminates the transmission of unneeded information to the traffic analysis service. It is also important to note that the decision to include metadata from a given packet can be made at the flow table level and/or by telemetry capture process  249 . For example, a flow entry in flow table  412  may specify that copies of all packets with TLS headers should be sent to telemetry capture process  249 , which then decides what information, if any, should be included in telemetry data  606 . 
       FIG. 7  illustrates an example simplified procedure for packet metadata capture in an SDN, in accordance with one or more embodiments described herein. For example, a non-generic, specifically configured device (e.g., device  200 ) may perform procedure  700  by executing stored instructions (e.g., telemetry capture process  249 ), such as a switch in an SDN. The procedure  700  may start at step  705 , and continues to step  710 , where, as described in greater detail above, the switch may receive a packet sent by an endpoint device via the SDN. 
     At step  715 , as detailed above, the switch may make a copy of the packet based on one or more header fields of the packet matching one or more flow table entries of the switch. For example, a given flow table entry may be indicative of one or more TLS header fields (e.g., copy the packet if it has a TLS header, copy the packet if it has a specified ciphersuite in its TLS header, etc.). 
     At step  720 , the switch may form telemetry data for reporting to a traffic analysis service by applying a metadata filter to the copy of the packet, as described in greater detail above. In various embodiments, such a metadata filter may prevent at least a portion of the copy of the packet from inclusion in the telemetry data. For example, the filter may strip the payload off of the packet and only include the packet&#39;s headers in the telemetry data. 
     At step  725 , as detailed above, the switch may send the telemetry data to a traffic analysis service. Such a service may use the telemetry data for purposes of determining whether the endpoint device is infected with malware, which application or operating system is associated with the traffic, or the like. Procedure  700  then ends at step  730 . 
     It should be noted that while certain steps within procedures  700  may be optional as described above, the steps shown in  FIG. 7  are merely examples for illustration, and certain other steps may be included or excluded as desired. Further, while a particular order of the steps is shown, this ordering is merely illustrative, and any suitable arrangement of the steps may be utilized without departing from the scope of the embodiments herein. 
     The techniques described herein, therefore, introduce mechanisms that allow for the control over which traffic telemetry data is captured in an SDN and reported to a traffic analysis service. By tailoring the reported telemetry data to the metadata actually used by the analysis service, the amount of resources for the telemetry capture and reporting can be greatly reduced. 
     While there have been shown and described illustrative embodiments that provide packet metadata filtering, it is to be understood that various other adaptations and modifications may be made within the spirit and scope of the embodiments herein. For example, while certain embodiments are described herein with respect to using certain models for purposes of traffic analysis, the models are not limited as such and may be used for other functions, in other embodiments. In addition, while certain protocols are shown, such as TLS, other suitable protocols may be used, accordingly. 
     The foregoing description has been directed to specific embodiments. It will be apparent, however, that other variations and modifications may be made to the described embodiments, with the attainment of some or all of their advantages. For instance, it is expressly contemplated that the components and/or elements described herein can be implemented as software being stored on a tangible (non-transitory) computer-readable medium (e.g., disks/CDs/RAM/EEPROM/etc.) having program instructions executing on a computer, hardware, firmware, or a combination thereof. Accordingly, this description is to be taken only by way of example and not to otherwise limit the scope of the embodiments herein. Therefore, it is the object of the appended claims to cover all such variations and modifications as come within the true spirit and scope of the embodiments herein.