Patent Description:
Enterprise networks are carrying a very fast growing volume of both business and non-business critical traffic. Often, business applications such as video collaboration, cloud applications, etc., use the same hypertext transfer protocol (HTTP) and/or HTTP secure (HTTPS) techniques that are used by non-business critical web traffic. This complicates the task of optimizing network performance for specific applications, as many applications use the same protocols, thus making it difficult to distinguish and select traffic flows for optimization.

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.

<CIT> describes proxy device that intercepts a client transport layer security message including a server name indicator from a client device. The first client transport layer security message is addressed to a server. The proxy device generates a second client transport layer security message including the server name indicator from the first client transport layer security message and sends the second client transport layer security message to the server. The proxy device receives a certificate from the server, validates its identity, and performs policy functions based on that identity.

According to one or more embodiments of the disclosure, a device in a network observes traffic between a client and a server for an encrypted session. The device makes a determination that a server certificate should be obtained from the server. The device, based on the determination, sends a handshake probe to the server. The device extracts server certificate information from a handshake response from the server that the server sent in response to the handshake probe. The device uses the extracted server certificate information to analyze the traffic between the client and the server.

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 <NUM>, IEEE P1901. <NUM>, 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> is a schematic block diagram of an example computer network <NUM> illustratively comprising nodes/devices, such as a plurality of routers/devices interconnected by links or networks, as shown. For example, customer edge (CE) routers <NUM> may be interconnected with provider edge (PE) routers <NUM> (e.g., PE-<NUM>, PE-<NUM>, and PE-<NUM>) in order to communicate across a core network, such as an illustrative network backbone <NUM>. For example, routers <NUM>, <NUM> may be interconnected by the public Internet, a multiprotocol label switching (MPLS) virtual private network (VPN), or the like. Data packets <NUM> (e.g., traffic/messages) may be exchanged among the nodes/devices of the computer network <NUM> 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:.

<FIG> illustrates an example of network <NUM> in greater detail, according to various embodiments. As shown, network backbone <NUM> may provide connectivity between devices located in different geographical areas and/or different types of local networks. For example, network <NUM> may comprise local networks <NUM>, <NUM> that include devices/nodes <NUM>-<NUM> and devices/nodes <NUM>-<NUM>, respectively, as well as a data center/cloud environment <NUM> that includes servers <NUM>-<NUM>. Notably, local networks <NUM>-<NUM> and data center/cloud environment <NUM> may be located in different geographic locations.

Servers <NUM>-<NUM> 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 <NUM> 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 <NUM> 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 <NUM> may be an LLN in which CE-<NUM> operates as a root node for nodes/devices <NUM>-<NUM> in the local mesh, in some embodiments.

<FIG> is a schematic block diagram of an example node/device <NUM> that may be used with one or more embodiments described herein, e.g., as any of the computing devices shown in <FIG>, particularly the PE routers <NUM>, CE routers <NUM>, nodes/device <NUM>-<NUM>, servers <NUM>-<NUM> (e.g., a network controller located in a data center, etc.), any other computing device that supports the operations of network <NUM> (e.g., switches, etc.), or any of the other devices referenced below. The device <NUM> may also be any other suitable type of device depending upon the type of network architecture in place, such as IoT nodes, etc. Device <NUM> comprises one or more network interfaces <NUM>, one or more processors <NUM>, and a memory <NUM> interconnected by a system bus <NUM>, and is powered by a power supply <NUM>.

The network interfaces <NUM> include the mechanical, electrical, and signaling circuitry for communicating data over physical links coupled to the network <NUM>. The network interfaces may be configured to transmit and/or receive data using a variety of different communication protocols. Notably, a physical network interface <NUM> 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 <NUM> comprises a plurality of storage locations that are addressable by the processor(s) <NUM> and the network interfaces <NUM> for storing software programs and data structures associated with the embodiments described herein. The processor <NUM> may comprise necessary elements or logic adapted to execute the software programs and manipulate the data structures <NUM>. An operating system <NUM> (e.g., the Internetworking Operating System, or IOS®, of Cisco Systems, Inc. , another operating system, etc.), portions of which are typically resident in memory <NUM> 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 <NUM>.

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 <NUM> 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 <NUM> may assess captured telemetry data regarding one or more traffic flows, to determine whether a given traffic flow or set of flows are caused by 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 <NUM> 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), or the like.

Traffic analysis process <NUM> 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 <NUM> 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 <NUM> 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 that is "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 <NUM> 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, 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) 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-generated, 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-generated, anomalous, etc. True negatives and positives may refer to the number of traffic flows that the model correctly classifies as normal or malware-generated, 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 <NUM> may assess the captured telemetry data on a per-flow basis. In other embodiments, traffic analysis <NUM> 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.

As shown in <FIG>, various mechanisms can be leveraged to capture information about traffic in a network, such as telemetry data regarding a traffic flow. For example, consider the case in which client node <NUM> initiates a traffic flow with remote server <NUM> that includes any number of packets <NUM>. Any number of networking devices along the path of the flow may analyze and assess packet <NUM>, to capture telemetry data regarding the traffic flow. For example, as shown, consider the case of edge router CE-<NUM> through which the traffic between node <NUM> and server <NUM> flows.

In some embodiments, a networking device may analyze packet headers, to capture feature information about the traffic flow. For example, router CE-<NUM> may capture the source address and/or port of host node <NUM>, the destination address and/or port of server <NUM>, the protocol(s) used by packet <NUM>, or other header information by analyzing the header of a packet <NUM>. 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, 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(s).

In further embodiments, the device may also assess the payload of the packet to capture information about the traffic flow. For example, router CE-<NUM> or another device may perform deep packet inspection (DPI) on one or more of packets <NUM>, 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., packets <NUM> were sent by a web browser of node <NUM>, packets <NUM> were sent by a videoconferencing application, etc.). However, as would be appreciated, a traffic flow may also be encrypted, thus preventing the device from assessing the actual payload of the packet.

The networking device that captures the flow telemetry data may also compute any number of statistics or metrics regarding the traffic flow. For example, CE-<NUM> may determine the start time, end time, duration, packet size(s), the distribution of bytes within a flow, etc., associated with the traffic flow by observing packets <NUM>. In further examples, the capturing device may capture sequence of packet lengths and time (SPLT) data regarding the traffic flow, sequence of application lengths and time (SALT) data regarding the traffic flow, or byte distribution (BD) data regarding the traffic flow.

As noted above, TLS information, SPLT data, inter-arrival times, and the like, provide a rich set of data that can be used for purposes of assessing network traffic. Notably, a malware classifier can be trained on this information to detect malicious traffic, even if the traffic is encrypted, with high recall, but sometimes at the cost of high precision. In another example, information about a client device can be discerned from analysis of its traffic, such as the operating system of the client, and the like.

However, there has been a recent push to conceal even more traffic information behind encryption. For example, in TLS version <NUM> and, presumably, subsequent versions of TLS, information such as the server certificate are now protected from access by intermediary devices. In other words, while these changes are intended to improve security for the encrypted session itself, these changes can also thwart attempts to determine whether the encrypted traffic is malicious (e.g., associated with malware, etc.).

The techniques herein introduce a probing approach that can be used to obtain certain information that can be used to analyze network traffic, even in the case in which the traffic is part of an encrypted session. For example, the probing can be performed to obtain information about the encryption mechanism itself (e.g., the server certificate used, etc.), which can be included as input to a traffic classifier, to determine whether the encrypted traffic between a client and server is malicious or to infer properties of the client, such as its operating system. In some aspects, the probing may be semi-active (e.g., conditional), thus allowing resources to be conserved in the network.

Specifically, according to one or more embodiments of the disclosure as described in detail below, a device in a network observes traffic between a client and a server for an encrypted session. The device makes a determination that a server certificate should be obtained from the server. The device, based on the determination, sends a handshake probe to the server. The device extracts server certificate information from a handshake response from the server that the server sent in response to the handshake probe. The device uses the extracted server certificate information to analyze the traffic between the client and the server.

Illustratively, the techniques described herein may be performed by hardware, software, and/or firmware, such as in accordance with the traffic analysis process <NUM>, which may include computer executable instructions executed by the processor <NUM> (or independent processor of interfaces <NUM>) to perform functions relating to the techniques described herein.

Operationally, <FIG> illustrates an example encrypted session <NUM> between a client <NUM> and a server <NUM>. As shown, client <NUM> may begin by initiating a handshake with server <NUM> in which cryptographic information is first exchanged. This cryptographic information can then be used by client <NUM> and server <NUM>, to encrypt subsequent messages between the two. In particular, client <NUM> may send a ClientHello message <NUM> to server <NUM>, thereby signifying that client <NUM> wishes to establish an encrypted session with server <NUM>. Note that, in some cases, client <NUM> and server <NUM> may first perform a SYN-ACK, to establish the TCP/IP connection via which ClientHello message <NUM> may be sent.

In various cases, ClientHello message <NUM> may include cryptographic keys for client <NUM> that can be used by server <NUM> to immediately begin encrypting the messages sent by server <NUM> back to client <NUM>. This is the approach taken by some encryption mechanisms, such as TLS version <NUM>. In prior versions of TLS, and in other schemes, the key exchange is performed only after an exchange of Hello messages.

In response to receiving ClientHello message <NUM>, server <NUM> may generate and send a ServerHello message <NUM> back to client <NUM>. Such a ServerHello message <NUM> may include the server key information for server <NUM>, extensions, and the server certificate of server <NUM>, which may be encrypted using the client keys sent by client <NUM> as part of ClientHello message <NUM>. Client <NUM> can then use its own keys to decrypt ClientHello message <NUM> and begin encrypting its subsequent messages based on the information included in ServerHello message <NUM>. For example, client <NUM> may use the server certificate included in ServerHello message <NUM> to authenticate server <NUM> and the server keys and extensions included in ServerHello message <NUM> to control the encryption of a GET HTTP message <NUM> sent by client <NUM> to server <NUM>. In turn, server <NUM> can use the information that it obtained from the handshake, to encrypt an HTTP response message <NUM> sent to client <NUM> in response to message <NUM>.

By virtue of performing the key exchange in conjunction with the Hello messages, the information that would otherwise be available to an intermediate device between client <NUM> and server <NUM>, is now protected from access by that intermediate device. Thus, in the case of a traffic analyzer device, information such as the server certificate information from server <NUM> would be protected from access by the device. In doing so, this prevents the traffic analyzer device from leveraging this information to classify the traffic exchanged between client <NUM> and server <NUM>, such as by making a malware assessment.

<FIG> illustrates an example architecture <NUM> for performing traffic analysis with semi-active probing of server certificate information, according to various embodiments. As shown, traffic analysis process <NUM> may include any number of sub-processes and/or may access any number of memory locations. As would be appreciated, these sub-processes and/or memory locations may be located on the same device or implemented in a distributed manner across multiple devices, the combination of which may be viewed as a single system/device that executes traffic analysis process <NUM>. Further, while certain functionalities are described with respect to the sub-processes and memory locations, these functions can be added, removed, or combined as desire, in further implementations.

During operation, traffic analysis process <NUM> may observe the traffic in the monitored network through review of telemetry data <NUM> regarding the traffic. In some cases, the device hosting traffic analysis process <NUM> may collect telemetry data <NUM> directly, such as in the case of the traffic flowing through the hosting device. In other cases, however, the device hosting traffic analysis process <NUM> may observe the traffic in the network through collection of telemetry data <NUM> from any number of data collection nodes in the network, such as NetFlow or IPFIX exporters. Note that an IPFIX information element is equivalent to a data feature, as described herein, with the former term being used typically in the field of network telemetry and the latter term being used typically in the field of data science. In further embodiments, one or more of the features in telemetry data <NUM> may also be in a compressed form.

In some embodiments, traffic analysis process <NUM> may include a telemetry preprocessor <NUM> that preprocesses the incoming telemetry data <NUM> regarding the network traffic. For example, telemetry preprocessor <NUM> may convert telemetry data <NUM> into proper form for analysis, such as by converting telemetry data <NUM> into feature vectors. In the case in which telemetry data <NUM> is compressed, telemetry preprocessor <NUM> may first decompress the data.

Traffic analysis process <NUM> may also include a traffic classifier <NUM> that is configured to classify the traffic data from telemetry preprocessor <NUM> and output resulting traffic classification <NUM>. For example, in the case of malware detection, traffic classifier <NUM> may be a machine learning-based classifier configured to discern between "malicious" and "benign" traffic, based on the observed characteristics of the traffic. In more complex implementations, traffic classifier <NUM> may be configured to assess whether the observed traffic is associated with a particular type of malware or family of malware. In yet another embodiment, traffic classifier <NUM> may be configured to infer information about a client or server involved in a communication, by classifying the traffic between the client and server. For example, the operating system of a client can be inferred through analysis of its network traffic. In some embodiment, traffic classifier <NUM> may comprise a number of classifiers configured to make different determinations based on the observed traffic.

Traffic analysis process <NUM> may include a handshake detector <NUM> that is configured to identify messages in the observed traffic that are involved in a cryptographic handshake. For example, handshake detector <NUM> may assess telemetry data <NUM>, to identify when a client sends a ClientHello message to a server, as well as the ServerHello message sent back in response. Such information can be used by traffic analysis process <NUM> to control which classification is performed on the traffic data by traffic classifier <NUM> and, as detailed below, help trigger probing of the server, in some cases.

In various embodiments, traffic analysis process <NUM> may include a probing engine <NUM> configured to generate and send probes to servers, to capture information about the servers, such as their certificate information. More specifically, probing by probing engine <NUM> may entail initiating a handshake between the target server and the device executing probing engine <NUM>. For example, in the case of TLS, probing engine <NUM> may send a ClientHello message to the target server, to capture the information sent by the server in its corresponding ServerHello message, such as its server certificate information. In turn, the information obtained by probing engine <NUM> (e.g., the handshake response data, etc.) can then be used as input to traffic classifier <NUM>, to enhance the analysis of the network traffic.

In some embodiments, probing engine <NUM> may cache the information that it obtains from its probing in an address database <NUM>. In turn, traffic analysis process <NUM> may use address database <NUM> to reduce the number of probes that probing engine <NUM> sends. Notably, if probing results for a given server are already exists in address database <NUM>, such as based on a lookup of the address of the server, this information can be used by traffic classifier <NUM> to classify traffic between a given client and the server and without necessitating probing engine <NUM> sending a probe. Example information that may be stored in address database <NUM> may include, but is not limited to, handshake message lengths or sizes, server certificate information, extensions, and the like.

Various cases may trigger probing engine <NUM> to determine that it should send a probe to a server. In various embodiments, these cases may include any or all of the following:.

As noted, traffic classifier <NUM> can use the information obtained by probing engine <NUM> to assess the observed traffic in the network and output a traffic classification <NUM>. Based on the resulting classification, the system can take any number of actions. For example, in the case of traffic classification <NUM> indicating the presence of malicious traffic, classification <NUM> can be used to cause the performance of a mitigation action, such as sending an alert to an administrator or security expert, sending an alert to the user of the client, blocking the malicious traffic, subjecting the traffic to additional scrutiny, or the like.

<FIG> illustrate examples of a traffic analyzer <NUM> performing semi-active probing of server certificate information, according to various embodiments. As shown, traffic analyzer <NUM> may be any device or service configured to analyze traffic between a client <NUM> and a server <NUM>. For example, traffic analyzer <NUM> may implement architecture <NUM> described previously. In some cases, traffic analyzer <NUM> may be an intermediary device or service via which traffic between client <NUM> and server <NUM> flows. In other cases, traffic analyzer <NUM> may observe this traffic indirectly by receiving telemetry data regarding the traffic from one or more intermediary devices located along the path between client <NUM> and server <NUM>.

For purposes of illustration, assume that client <NUM> sends a ClientHello message <NUM> to server <NUM>, signifying that client <NUM> wishes to establish a secure session with server <NUM>. In TLS version <NUM> and other similar protocols, message <NUM> may also include cryptographic key information that can be used by server <NUM> to encrypt its response. Based on its observation of the traffic exchanged between client <NUM> and server <NUM>, traffic analyzer <NUM> may determine that message <NUM> is a ClientHello message intended to initiate a secure session.

In <FIG>, server <NUM> may send a ServerHello message <NUM> back to client <NUM> and in response to ClientHello message <NUM> from client <NUM>. However, as noted, the server certificate of server <NUM> may be protected from access by traffic analyzer <NUM> (e.g., encrypted using the keying information included in ClientHello message <NUM>. In such a case, traffic analyzer <NUM> may make a determination as to whether it should probe server <NUM> itself, to obtain this certificate information. For example, if a preliminary assessment of the traffic associated with client <NUM>, server <NUM>, or a combination thereof, is suspected of being malicious, traffic analyzer <NUM> may determine that it should probe server <NUM>. This may further be controlled based on the classification score (e.g., no probing may be necessary, if the traffic is already above a threshold probability of being malicious, etc.).

In other cases, if traffic analyzer <NUM> had previously probed server <NUM>, it may still opt to re-probe server <NUM>, such as when the cached server certificate information is out of date or traffic analyzer <NUM> detects a discrepancy between messages <NUM>/<NUM> and the cached information. For example, traffic analyzer <NUM> may opt to re-probe server <NUM>, if the size of ServerHello message <NUM> is much larger or smaller than the ServerHello message previously sent to traffic analyzer <NUM> during probing of server <NUM>. In yet further cases, traffic analyzer <NUM> may determine that it should probe server <NUM>, if it detects discrepancies in the associated DNS messages, if server <NUM> does not respond to client <NUM> with ServerHello message <NUM>, or the like.

In <FIG>, traffic analyzer <NUM> may send a ClientHello probe message <NUM> to server <NUM>, to obtain its server certificate information, as well as any other information that may be protected from purview in message <NUM>. In various embodiments, ClientHello probe message <NUM> may mimic the ClientHello message <NUM> sent by client <NUM>, to ensure that server <NUM> treats both client <NUM> and traffic analyzer <NUM> the same way. For example, ClientHello probe message <NUM> may use the same field parameters as that of ClientHello message <NUM>, but with the key information of traffic analyzer <NUM>, instead. Also, if server <NUM> behaves differently depending on the order in which it receives messages, then traffic analyzer <NUM> can send ClientHello probe message <NUM> before the actual one, or randomize the order of those messages.

In <FIG>, server <NUM> may respond to ClientHello probe message <NUM> with a ServerHello message <NUM>. In turn, traffic analyzer <NUM> can use its key information to decrypt ServerHello message <NUM> and obtain the server certificate of server <NUM>, as well as any other information that may otherwise be protected from outside access. Then, as shown in <FIG>, traffic analyzer <NUM> can use this obtained information, in combination with other information (e.g., SPLT information, etc.), to classify the traffic between client <NUM> and server <NUM> and take any corrective measures, as necessary.

In further embodiments, once the handshake is finished, traffic analyzer <NUM> can also send a set of requests to understand the resources hosted on server <NUM>, e.g., by sending one or more "GET / HTTP/<NUM>" messages. These requests can be generic, or predicted to be relevant for a specific server and observed traffic features. The data collected can be used for general threat intelligence, or it could be used as an orthogonal set of data features for a more advanced traffic classification. In other words, after performing the handshake with server <NUM>, traffic analyzer <NUM> can perform application layer probing of server <NUM>, to obtain further information that can be used to classify the traffic between client <NUM> and server <NUM>.

For OS detection on a private network, the techniques herein could also be used by traffic analyzer <NUM> whenever a passive inference of the OS of client <NUM> gives an 'unknown' result. Because the logic used in crafting probe messages and in processing their responses can be complex and potentially changing over time, remote session injection can be used, so that a collector can implement that logic, while the observation point can be simple and static.

<FIG> illustrates an example simplified procedure for sending a probe to a server in a network, in accordance with one or more embodiments described herein. For example, a non-generic, specifically configured device (e.g., device <NUM>) may perform procedure <NUM> by executing stored instructions (e.g., process <NUM>). The procedure <NUM> may start at step <NUM>, and continues to step <NUM>, where, as described in greater detail above, the device may observer traffic between a client and a server for an encrypted session. For example, the observed traffic may include handshake messages, HTTP data messages, or the like. In various cases, the device may observe the traffic directly, such as when the traffic flows through the device. In other cases, the device may still observe the traffic indirectly, such as by receiving telemetry data regarding the traffic from one or more collectors in the network.

At step <NUM>, as detailed above, the device may make a determination that a server certificate should be obtained from the server. Notably, the server certificate may be protected from access by the device, depending on the mechanism used to secure the traffic between the client and the server. Nevertheless, this information may be useful for purposes of analyzing the client-server traffic (e.g., malware detection, OS detection, etc.). In some embodiments, the device may determine that the certificate information should be obtained, based on a preliminary assessment of the maliciousness of the observed traffic. In another embodiment, the device may determine that the server certificate information should be obtained based on previously cached certificate information for the server being out of date, or that a discrepancy exists between the observed client-server traffic and the expected traffic. For example, a discrepancy may be a size discrepancy in handshake messages, a discrepancy in an associated domain name system (DNS) flows, a failed handshake between the client and the server, or the like. In another case, the device may simply determine that the server certificate information should be obtained via a scan of the Internet or a portion thereof.

At step <NUM>, the device may send, based on the determination made in step <NUM>, a handshake probe to the server, as described in greater detail above. In some cases, the device may mimic the observed handshake information sent by the client to the server, but with key information associated with the device. In other cases, the device may simply send a ClientHello or other handshake request to the server, to obtain its server certificate information and any other information available during the handshake exchange.

At step <NUM>, as detailed above, the device may extract server certificate information from a handshake response from the server that the server sent in response to the handshake probe. For example, in the case in which the device sends a ClientHello message to the server that includes the device's key information, the device may use this key information to extract out the server's certificate information and/or any other information from a corresponding ServerHello sent back to the device.

At step <NUM>, the device may use the extracted server certificate information to analyze the traffic between the client and the server, as described in greater detail above. For example, the device may use the server certificate information as input to a machine learning-based traffic classifier, potentially with other traffic characteristics as well, to determine whether the traffic between the client and the server is malicious. In turn, the device may trigger the performance of any number of mitigation actions in the network, such as sending alerts, blocking the traffic, etc. Procedure <NUM> then ends at step <NUM>.

It should be noted that while certain steps within procedure <NUM> may be optional as described above, the steps shown in <FIG> 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, allow a traffic analyzer to still obtain and use server certificate information for purposes of traffic classification, even in cases where this information would otherwise be protected from access. For example, in the case of TLS version <NUM>, the techniques herein would allow the traffic analyzer to independently obtain the server certificate information, through handshake probing of the server. In further aspects, the traffic analyzer may only probe the server if a certain condition is met, so as to limit the impact of the probing on the performance of the network.

While there have been shown and described illustrative embodiments that provide for semi-active probing of a server, 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 malware detection, 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.

Claim 1:
A method comprising:
observing (<NUM>), by a device in a network (<NUM>), traffic between a client (<NUM>) and a server (<NUM>) for an encrypted session;
making, by the device, a determination (<NUM>) that a server certificate should be obtained from the server, comprising determining, by the device, that information for the server cached in an address database (<NUM>) is out of date;
sending (<NUM>), by the device and based on the determination, a handshake probe (<NUM>) to the server, wherein the handshake probe mimics a client hello message (<NUM>) sent by the client to the server in the observed traffic, wherein the handshake probe uses the same field parameters as the client hello message (<NUM>), but with cryptographic keying information of the device instead;
extracting (<NUM>), by the device (<NUM>), server certificate information from a handshake response (<NUM>) from the server (<NUM>) that the server encrypted using the cryptographic keying information in the handshake probe and sent in response to the handshake probe (<NUM>), comprising decrypting, by the device, the handshake response (<NUM>) to obtain the server certificate information;
caching, by the device, the extracted server certificate information in the address database; and
using (<NUM>), by the device, the cached server certificate information to analyze the traffic between the client (<NUM>) and the server (<NUM>).