Anomaly selection using distance metric-based diversity and relevance

In one embodiment, a device in a network receives a notification of a particular anomaly detected by a distributed learning agent in the network that executes a machine learning-based anomaly detector to analyze traffic in the network. The device computes one or more distance scores between the particular anomaly and one or more previously detected anomalies. The device also computes one or more relevance scores for the one or more previously detected anomalies. The device determines a reporting score for the particular anomaly based on the one or more distance scores and on the one or more relevance scores. The device reports the particular anomaly to a user interface based on the determined reporting score.

TECHNICAL FIELD

The present disclosure relates generally to computer networks, and, more particularly, to select anomalies using distance-metric based diversity and relevance.

BACKGROUND

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.

One type of network attack that is of particular concern in the context of computer networks is a Denial of Service (DoS) attack. In general, the goal of a DoS attack is to prevent legitimate use of the services available on the network. For example, a DoS jamming attack may artificially introduce interference into the network, thereby causing collisions with legitimate traffic and preventing message decoding. In another example, a DoS attack may attempt to overwhelm the network's resources by flooding the network with requests, to prevent legitimate requests from being processed. A DoS attack may also be distributed, to conceal the presence of the attack. For example, a distributed DoS (DDoS) attack may involve multiple attackers sending malicious requests, making it more difficult to distinguish when an attack is underway. When viewed in isolation, a particular one of such a request may not appear to be malicious. However, in the aggregate, the requests may overload a resource, thereby impacting legitimate requests sent to the resource.

Botnets represent one way in which a DDoS attack may be launched against a network. In a botnet, a subset of the network devices may be infected with malicious software, thereby allowing the devices in the botnet to be controlled by a single master. Using this control, the master can then coordinate the attack against a given network resource.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Overview

According to one or more embodiments of the disclosure, a device in a network receives a notification of a particular anomaly detected by a distributed learning agent in the network that executes a machine learning-based anomaly detector to analyze traffic in the network. The device computes one or more distance scores between the particular anomaly and one or more previously detected anomalies. The device also computes one or more relevance scores for the one or more previously detected anomalies. The device determines a reporting score for the particular anomaly based on the one or more distance scores and on the one or more relevance scores. The device reports the particular anomaly to a user interface based on the determined reporting score.

DESCRIPTION

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).

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, shared-media mesh networks, such as wireless or PLC 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: LLN routers typically operate with constraints, e.g., processing power, memory, and/or energy (battery), and their interconnects 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 (between devices inside the LLN), point-to-multipoint traffic (from a central control point such at the root node to a subset of devices inside the LLN), and multipoint-to-point traffic (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 network160may be an LLN in which CE-2 operates as a root node for nodes/devices10-16in the local mesh, in some embodiments.

In contrast to traditional networks, LLNs face a number of communication challenges. First, LLNs communicate over a physical medium that is strongly affected by environmental conditions that change over time. Some examples include temporal changes in interference (e.g., other wireless networks or electrical appliances), physical obstructions (e.g., doors opening/closing, seasonal changes such as the foliage density of trees, etc.), and propagation characteristics of the physical media (e.g., temperature or humidity changes, etc.). The time scales of such temporal changes can range between milliseconds (e.g., transmissions from other transceivers) to months (e.g., seasonal changes of an outdoor environment). In addition, LLN devices typically use low-cost and low-power designs that limit the capabilities of their transceivers. In particular, LLN transceivers typically provide low throughput. Furthermore, LLN transceivers typically support limited link margin, making the effects of interference and environmental changes visible to link and network protocols. The high number of nodes in LLNs in comparison to traditional networks also makes routing, quality of service (QoS), security, network management, and traffic engineering extremely challenging, to mention a few.

Routing process/services244include computer executable instructions executed by processor220to perform functions provided by one or more routing protocols, such as the Interior Gateway Protocol (IGP) (e.g., Open Shortest Path First, “OSPF,” and Intermediate-System-to-Intermediate-System, “IS-IS”), the Border Gateway Protocol (BGP), etc., as will be understood by those skilled in the art. These functions may be configured to manage a forwarding information database including, e.g., data used to make forwarding decisions. In particular, changes in the network topology may be communicated among routers200using routing protocols, such as the conventional OSPF and IS-IS link-state protocols (e.g., to “converge” to an identical view of the network topology).

Notably, routing process244may also perform functions related to virtual routing protocols, such as maintaining VRF instance, or tunneling protocols, such as for MPLS, generalized MPLS (GMPLS), etc., each as will be understood by those skilled in the art. Also, EVPN, e.g., as described in the IETF Internet Draft entitled “BGP MPLS Based Ethernet VPN”<draft-ietf-l2vpn-evpn>, introduce a solution for multipoint L2VPN services, with advanced multi-homing capabilities, using BGP for distributing customer/client media access control (MAC) address reach-ability information over the core MPLS/IP network.

SLN process248includes computer executable instructions that, when executed by processor(s)220, cause device200to perform anomaly detection functions as part of an anomaly detection infrastructure within the network. In general, anomaly detection attempts to identify patterns that do not conform to an expected behavior. For example, in one embodiment, the anomaly detection infrastructure of the network may be operable to detect network attacks (e.g., DDoS attacks, the use of malware such as viruses, rootkits, etc.). However, anomaly detection in the context of computer networking typically presents a number of challenges: 1.) a lack of a ground truth (e.g., examples of normal vs. abnormal network behavior), 2.) being able to define a “normal” region in a highly dimensional space can be challenging, 3.) the dynamic nature of the problem due to changing network behaviors/anomalies, 4.) malicious behaviors such as malware, viruses, rootkits, etc. may adapt in order to appear “normal,” and 5.) differentiating between noise and relevant anomalies is not necessarily possible from a statistical standpoint, but typically also requires domain knowledge.

Anomalies may also take a number of forms in a computer network: 1.) point anomalies (e.g., a specific data point is abnormal compared to other data points), 2.) contextual anomalies (e.g., a data point is abnormal in a specific context but not when taken individually), or 3.) collective anomalies (e.g., a collection of data points is abnormal with regards to an entire set of data points). Generally, anomaly detection refers to the ability to detect an anomaly that could be triggered by the presence of malware attempting to access data (e.g., data exfiltration), spyware, ransom-ware, etc. and/or non-malicious anomalies such as misconfigurations or misbehaving code. Particularly, an anomaly may be raised in a number of circumstances:Security threats: the presence of a malware using unknown attacks patterns (e.g., no static signatures) may lead to modifying the behavior of a host in terms of traffic patterns, graphs structure, etc. Machine learning processes may detect these types of anomalies using advanced approaches capable of modeling subtle changes or correlation between changes (e.g., unexpected behavior) in a highly dimensional space. Such anomalies are raised in order to detect, e.g., the presence of a 0-day malware, malware used to perform data ex-filtration thanks to a Command and Control (C2) channel, or even to trigger (Distributed) Denial of Service (DoS) such as DNS reflection, UDP flood, HTTP recursive get, etc. In the case of a (D)DoS, although technical an anomaly, the term “DoS” is usually used.
SLN process248may detect malware based on the corresponding impact on traffic, host models, graph-based analysis, etc., when the malware attempts to connect to a C2 channel, attempts to move laterally, or exfiltrate information using various techniques.Misbehaving devices: a device such as a laptop, a server of a network device (e.g., storage, router, switch, printer, etc.) may misbehave in a network for a number of reasons: 1.) a user using a discovery tool that performs (massive) undesirable scanning in the network (in contrast with a lawful scanning by a network management tool performing device discovery), 2.) a software defect (e.g. a switch or router dropping packet because of a corrupted RIB/FIB or the presence of a persistent loop by a routing protocol hitting a corner case).Dramatic behavior change: the introduction of a new networking or end-device configuration, or even the introduction of a new application may lead to dramatic behavioral changes. Although technically not anomalous, an SLN-enabled node having computed behavioral model(s) may raise an anomaly when detecting a brutal behavior change. Note that in such as case, although an anomaly may be raised, a learning system such as SLN is expected to learn the new behavior and dynamically adapts according to potential user feedback.Misconfigured devices: a configuration change may trigger an anomaly: a misconfigured access control list (ACL), route redistribution policy, routing policy, QoS policy maps, or the like, may have dramatic consequences such a traffic black-hole, QoS degradation, etc. SLN process248may advantageously identify these forms of misconfigurations, in order to be detected and fixed.

Computational entities that rely on one or more machine learning techniques to perform a task for which they have not been explicitly programmed to perform are typically referred to as learning machines. In particular, learning machines are capable of adjusting their behavior to their environment. For example, a learning machine may dynamically make future predictions based on current or prior network measurements, may make control decisions based on the effects of prior control commands, etc.

For purposes of anomaly detection in a network, a learning machine may construct a model of normal network behavior, to detect data points that deviate from this model. For example, a given model (e.g., a supervised, un-supervised, or semi-supervised model) may be used to generate and report anomaly scores to another device. Example machine learning techniques that may be used to construct and analyze such a model 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, etc.), neural networks (e.g., reservoir networks, artificial neural networks, etc.), support vector machines (SVMs), or the like.

One class of machine learning techniques that is of particular use in the context of anomaly detection is clustering. Generally speaking, clustering is a family of techniques that seek to group data according to some typically predefined notion of similarity. For instance, clustering is a very popular technique used in recommender systems for grouping objects that are similar in terms of people's taste (e.g., because you watched X, you may be interested in Y, etc.). Typical clustering algorithms are k-means, density based spatial clustering of applications with noise (DBSCAN) and mean-shift, where a distance to a cluster is computed with the hope of reflecting a degree of anomaly (e.g., using a Euclidian distance and a cluster based local outlier factor that takes into account the cluster density).

Replicator techniques may also be used for purposes of anomaly detection. Such techniques generally attempt to replicate an input in an unsupervised manner by projecting the data into a smaller space (e.g., compressing the space, thus performing some dimensionality reduction) and then reconstructing the original input, with the objective of keeping the “normal” pattern in the low dimensional space. Example techniques that fall into this category include principal component analysis (PCA) (e.g., for linear models), multi-layer perceptron (MLP) ANNs (e.g., for non-linear models), and replicating reservoir networks (e.g., for non-linear models, typically for time series).

According to various embodiments, SLN process248may also use graph-based models for purposes of anomaly detection. Generally speaking, a graph-based model attempts to represent the relationships between different entities as a graph of nodes interconnected by edges. For example, ego-centric graphs have been used to represent the relationship between a particular social networking profile and the other profiles connected to it (e.g., the connected “friends” of a user, etc.). The patterns of these connections can then be analyzed for purposes of anomaly detection. For example, in the social networking context, it may be considered anomalous for the connections of a particular profile not to share connections, as well. In other words, a person's social connections are typically also interconnected. If no such interconnections exist, this may be deemed anomalous.

An example self learning network (SLN) infrastructure that may be used to detect network anomalies is shown inFIG. 3, according to various embodiments. Generally, network devices may be configured to operate as part of an SLN infrastructure to detect, analyze, and/or mitigate network anomalies such as network attacks (e.g., by executing SLN process248). Such an infrastructure may include certain network devices acting as distributed learning agents (DLAs) and one or more supervisory/centralized devices acting as a supervisory and control agent (SCA). A DLA may be operable to monitor network conditions (e.g., router states, traffic flows, etc.), perform anomaly detection on the monitored data using one or more machine learning models, report detected anomalies to the SCA, and/or perform local mitigation actions. Similarly, an SCA may be operable to coordinate the deployment and configuration of the DLAs (e.g., by downloading software upgrades to a DLA, etc.), receive information from the DLAs (e.g., detected anomalies/attacks, compressed data for visualization, etc.), provide information regarding a detected anomaly to a user interface (e.g., by providing a webpage to a display, etc.), and/or analyze data regarding a detected anomaly using more CPU intensive machine learning processes.

One type of network attack that is of particular concern in the context of computer networks is a Denial of Service (DoS) attack. In general, the goal of a DoS attack is to prevent legitimate use of the services available on the network. For example, a DoS jamming attack may artificially introduce interference into the network, thereby causing collisions with legitimate traffic and preventing message decoding. In another example, a DoS attack may attempt to overwhelm the network's resources by flooding the network with requests (e.g., SYN flooding, sending an overwhelming number of requests to an HTTP server, etc.), to prevent legitimate requests from being processed. A DoS attack may also be distributed, to conceal the presence of the attack. For example, a distributed DoS (DDoS) attack may involve multiple attackers sending malicious requests, making it more difficult to distinguish when an attack is underway. When viewed in isolation, a particular one of such a request may not appear to be malicious. However, in the aggregate, the requests may overload a resource, thereby impacting legitimate requests sent to the resource.

Botnets represent one way in which a DDoS attack may be launched against a network. In a botnet, a subset of the network devices may be infected with malicious software, thereby allowing the devices in the botnet to be controlled by a single master. Using this control, the master can then coordinate the attack against a given network resource.

DoS attacks are relatively easy to detect when they are brute-force (e.g. volumetric), but, especially when highly distributed, they may be difficult to distinguish from a flash-crowd (e.g., an overload of the system due to many legitimate users accessing it at the same time). This fact, in conjunction with the increasing complexity of performed attacks, makes the use of “classic” (usually threshold-based) techniques useless for detecting them. However, machine learning techniques may still be able to detect such attacks, before the network or service becomes unavailable. For example, some machine learning approaches may analyze changes in the overall statistical behavior of the network traffic (e.g., the traffic distribution among flow flattens when a DDoS attack based on a number of microflows happens). Other approaches may attempt to statistically characterizing the normal behaviors of network flows or TCP connections, in order to detect significant deviations. Classification approaches try to extract features of network flows and traffic that are characteristic of normal traffic or malicious traffic, constructing from these features a classifier that is able to differentiate between the two classes (normal and malicious).

As shown inFIG. 3, routers CE-2 and CE-3 may be configured as DLAs and server152may be configured as an SCA, in one implementation. In such a case, routers CE-2 and CE-3 may monitor traffic flows, router states (e.g., queues, routing tables, etc.), or any other conditions that may be indicative of an anomaly in network100. As would be appreciated, any number of different types of network devices may be configured as a DLA (e.g., routers, switches, servers, blades, etc.) or as an SCA.

Assume, for purposes of illustration, that CE-2 acts as a DLA that monitors traffic flows associated with the devices of local network160(e.g., by comparing the monitored conditions to one or more machine-learning models). For example, assume that device/node10sends a particular traffic flow302to server154(e.g., an application server, etc.). In such a case, router CE-2 may monitor the packets of traffic flow302and, based on its local anomaly detection mechanism, determine that traffic flow302is anomalous. Anomalous traffic flows may be incoming, outgoing, or internal to a local network serviced by a DLA, in various cases.

In some cases, traffic302may be associated with a particular application supported by network100. Such applications may include, but are not limited to, automation applications, control applications, voice applications, video applications, alert/notification applications (e.g., monitoring applications), communication applications, and the like. For example, traffic302may be email traffic, HTTP traffic, traffic associated with an enterprise resource planning (ERP) application, etc.

In various embodiments, the anomaly detection mechanisms in network100may use Internet Behavioral Analytics (IBA). In general, IBA refers to the use of advanced analytics coupled with networking technologies, to detect anomalies in the network. Although described later with greater details, the ability to model the behavior of a device (networking switch/router, host, etc.) will allow for the detection of malware, which is complementary to the use of a firewall that uses static signatures. Observing behavioral changes (e.g., a deviation from modeled behavior) thanks to aggregated flows records, deep packet inspection, etc., may allow detection of an anomaly such as an horizontal movement (e.g. propagation of a malware, etc.), or an attempt to perform information exfiltration.

FIG. 4illustrates an example distributed learning agent (DLA)400in greater detail, according to various embodiments. Generally, a DLA may comprise a series of modules hosting sophisticated tasks (e.g., as part of an overall SLN process248). Generally, DLA400may communicate with an SCA (e.g., via one or more northbound APIs402) and any number of nodes/devices in the portion of the network associated with DLA400(e.g., via APIs420, etc.).

In some embodiments, DLA400may execute a Network Sensing Component (NSC)416that is a passive sensing construct used to collect a variety of traffic record inputs426from monitoring mechanisms deployed to the network nodes. For example, traffic record inputs426may include Cisco™ Netflow records, application identification information from a Cisco™ Network Based Application Recognition (NBAR) process or another application-recognition mechanism, administrative information from an administrative reporting tool (ART), local network state information service sets, media metrics, or the like.

Furthermore, NSC416may be configured to dynamically employ Deep Packet Inspection (DPI), to enrich the mathematical models computed by DLA400, a critical source of information to detect a number of anomalies. Also of note is that accessing control/data plane data may be of utmost importance, to detect a number of advanced threats such as data exfiltration. NSC416may be configured to perform data analysis and data enhancement (e.g., the addition of valuable information to the raw data through correlation of different information sources). Moreover, NSC416may compute various networking based metrics relevant for the Distributed Learning Component (DLC)408, such as a large number of statistics, some of which may not be directly interpretable by a human.

In some embodiments, DLA400may also include DLC408that may perform a number of key operations such as any or all of the following: computation of Self Organizing Learning Topologies (SOLT), computation of “features” (e.g., feature vectors), advanced machine learning processes, etc., which DLA400may use in combination to perform a specific set of tasks. In some cases, DLC408may include a reinforcement learning (RL) engine412that uses reinforcement learning to detect anomalies or otherwise assess the operating conditions of the network. Accordingly, RL engine412may maintain and/or use any number of communication models410that model, e.g., various flows of traffic in the network. In further embodiments, DLC408may use any other form of machine learning techniques, such as those described previously (e.g., supervised or unsupervised techniques, etc.). For example, in the context of SLN for security, DLC408may perform modeling of traffic and applications in the area of the network associated with DLA400. DLC408can then use the resulting models410to detect graph-based and other forms of anomalies (e.g., by comparing the models with current network characteristics, such as traffic patterns. The SCA may also send updates414to DLC408to update model(s)410and/or RL engine412(e.g., based on information from other deployed DLAs, input from a user, etc.).

When present, RL engine412may enable a feed-back loop between the system and the end user, to automatically adapt the system decisions to the expectations of the user and raise anomalies that are of interest to the user (e.g., as received via a user interface of the SCA). In one embodiment, RL engine412may receive a signal from the user in the form of a numerical reward that represents for example the level of interest of the user related to a previously raised event. Consequently the agent may adapt its actions (e.g. search for new anomalies), to maximize its reward over time, thus adapting the system to the expectations of the user. More specifically, the user may optionally provide feedback thanks to a lightweight mechanism (e.g., ‘like’ or ‘dislike’) via the user interface.

In some cases, DLA400may include a threat intelligence processor (TIP)404that processes anomaly characteristics so as to further assess the relevancy of the anomaly (e.g. the applications involved in the anomaly, location, scores/degree of anomaly for a given model, nature of the flows, or the like). TIP404may also generate or otherwise leverage a machine learning-based model that computes a relevance index. Such a model may be used across the network to select/prioritize anomalies according to the relevancies.

DLA400may also execute a Predictive Control Module (PCM)406that triggers relevant actions in light of the events detected by DLC408. In order words, PCM406is the decision maker, subject to policy. For example, PCM406may employ rules that control when DLA400is to send information to the SCA (e.g., alerts, predictions, recommended actions, trending data, etc.) and/or modify a network behavior itself. For example, PCM406may determine that a particular traffic flow should be blocked (e.g., based on the assessment of the flow by TIP404and DLC408) and an alert sent to the SCA.

Network Control Component (NCC)418is a module configured to trigger any of the actions determined by PCM406in the network nodes associated with DLA400. In various embodiments, NCC418may communicate the corresponding instructions422to the network nodes using APIs420(e.g., DQoS interfaces, ABR interfaces, DCAC interfaces, etc.). For example, NCC418may send mitigation instructions422to one or more nodes that instruct the receives to reroute certain anomalous traffic, perform traffic shaping, drop or otherwise “black hole” the traffic, or take other mitigation steps. In some embodiments, NCC418may also be configured to cause redirection of the traffic to a “honeypot” device for forensic analysis. Such actions may be user-controlled, in some cases, through the use of policy maps and other configurations. Note that NCC418may be accessible via a very flexible interface allowing a coordinated set of sophisticated actions. In further embodiments, API(s)420of NCC418may also gather/receive certain network data424from the deployed nodes such as Cisco™ OnePK information or the like.

The various components of DLA400may be executed within a container, in some embodiments, that receives the various data records and other information directly from the host router or other networking device. Doing so prevents these records from consuming additional bandwidth in the external network. This is a major advantage of such a distributed system over centralized approaches that require sending large amount of traffic records. Furthermore, the above mechanisms afford DLA400additional insight into other information such as control plane packet and local network states that are only available on premise. Note also that the components shown inFIG. 4may have a low footprint, both in terms of memory and CPU. More specifically, DLA400may use lightweight techniques to compute features, identify and classify observation data, and perform other functions locally without significantly impacting the functions of the host router or other networking device.

As noted above, distributed anomaly detection systems allow for the monitoring of events (e.g., network traffic) that occur in a distributed fashion and can scale to very large systems. In such distributed settings, although the DLA may be able to exchange information in a peer-to-peer (e.g., between DLAs) or hub-and-spoke (e.g., between DLAs and an SLA) manner, a substantial portion of the computation and decision of whether an event is anomalous is usually made by the agent itself, with imperfect and partial knowledge of the overall system state. For example, with a large number of DLAs, a moderately rare phenomenon may have been observed multiple times at the scale of the system as a whole, but very rarely at the level of a single DLA. Depending on the extent to which the DLAs communicate and exchange information, a single DLA might thus consider anomalous something that is actually rather frequent at the scale of the whole system. Of note also is that a typical implementation may include dozens of DLAs across the entire system.

A core functionality of the anomaly detection system described herein to raise alerts regarding detected anomalies that are of relevance to the user/administrator. However, the definition of what constitutes a relevant anomalous event may vary depending on application, the subjective views of the actual users/administrators, etc.

In some cases, a feedback mechanism may be employed between the user interface and the DLAs, thereby allowing the DLAs to control which anomalies are reported by the DLAs. However, in further cases, a DLA may not have the sufficient capacity to correctly optimize for the end user's utility, due to its limited view of the overall system and events, as well as potentially the computational and storage resources of the DLA.

Anomaly Selection Using Distance Metric-Based Diversity and Relevance

The techniques herein introduce a mechanism that selects relevant anomaly for reporting to a user interface, based on anomalies raised by DLAs with imperfect knowledge of the full system. In some aspects, a central controller, such as an SCA, builds models of distance and similarity between previously detected anomalies. When a new anomaly is observed at a DLA, the central controller may decide whether to report the new anomaly to the user interface based on its similarity to the previously detected anomalies and the relevance of the prior anomalies. Said differently, the techniques herein allow a central control to explore the space of anomalies raised by the DLA(s), to present the most relevant anomalies to the user interface according to a given criterion, by mapping the anomalies into a metric space. Notably, the central controller may build a space of anomalies and score new, incoming anomalies according to their similarity with past anomalies and the relevance of such similar past anomalies. In contrast with approaches in which anomalies of interest are evaluated using a relevance metric, the techniques herein rank the degree of interest of an anomaly according to a distance between the anomaly under scrutiny and other anomalies raised in the past.

Specifically, according to one or more embodiments of the disclosure as described in detail below, a device in a network receives a notification of a particular anomaly detected by a distributed learning agent in the network that executes a machine learning-based anomaly detector to analyze traffic in the network. The device computes one or more distance scores between the particular anomaly and one or more previously detected anomalies. The device also computes one or more relevance scores for the one or more previously detected anomalies. The device determines a reporting score for the particular anomaly based on the one or more distance scores and on the one or more relevance scores. The device reports the particular anomaly to a user interface based on the determined reporting score.

Illustratively, the techniques described herein may be performed by hardware, software, and/or firmware, such as in accordance with the SLN process248, which may include computer executable instructions executed by the processor220(or independent processor of interfaces210) to perform functions relating to the techniques described herein, e.g., in conjunction with routing process244.

Operationally,FIG. 5illustrates an example architecture500for reporting a detected anomaly in an SLN, according to various embodiments. One aspect of the techniques herein illustratively involves a remote learning agent that is equipped with a machine learning-based anomaly detection engine, such as DLA400shown. Notably, the anomaly detection engine (e.g., DLC408) may use a set of machine learning models, to detect anomalies at the edge of a local network and/or deeper within the network. For example, DLC408may employ an unsupervised machine learning-based anomaly detector that identifies statistical deviations in the characteristics of the network traffic. DLA400may also employ a traffic capture mechanism (e.g., NSC416, etc.) that is in charge of dynamically capturing traffic data of interest.

As described above, architecture500may also include an SCA502that provides supervisory control over DLA400and receives notification of any of the anomalies detected by DLA400. In turn, SCA502may report the detected anomalies to a user interface (UI) process518, which may be executed by a client device504in communication with SCA502or direction on SCA502. Notably, SCA502may generate visualizations for display by UI process518, thereby allowing an administrator or other user to review the anomaly detection mechanisms in the network and any detected anomalies. In response, the user may provide feedback via UI process518regarding any detected anomalies and/or the reporting mechanism to SCA502. The user may also provide, via UI process518, other configurations, settings, or the like, to SCA502, to adjust the operation of the SLN. In further embodiments, UI process518may be executed directly on SCA502.

In some embodiments, SCA502may be configured to compute distances between anomalies. For example, SCA502may execute an anomaly distance calculator508that computes the distances between anomalies stored in an anomaly database506of anomalies reported by the various DLAs in the network (e.g., DLA400shown, etc.). In general, an anomaly may be represented by the feature vector of observed network characteristics that triggered the anomaly detector of a DLA. For example, a given anomaly may comprise a highly dimensional vector of traffic features such as average packet size, flow duration, binary indicators as to whether the traffic flow is an HTTP flow, a DNS flow, etc.

In one embodiment, anomaly distance calculator508may execute a static set of rules that have been defined by one or more domain experts based on their knowledge. In another embodiment, anomaly distance calculator508may employ a dynamic model. In such a case, for example, the static expert model may serve as a baseline and UI process518may provide feedback to anomaly distance calculator508regarding the distance between two anomalies reported to UI process518by SCA502. In turn, anomaly distance calculator508may use this similarity feedback as input to statistical methods, to infer a distance metric between anomalies that is the most consistent with the user-provided similarity feedback (e.g., using a statistical model). In various cases, this feedback functionality of UI process518may be integrated in the full anomaly detection system and workflow or, alternatively, be separate and be used to derive a distance metric between anomalies through offline user studies.

In some cases, anomaly database506may be of various forms, to store information about previously detected and reported anomalies. In some instances, anomaly database506may be a traditional database that potentially uses compression to reduce its storage size. In other cases, anomaly database506may comprise an ad-hoc approximate data structure, such as classical locality sensitive hashing data structures. In some embodiments, SCA502may also push some or all of anomaly database506to the various DLAs in the network, possibly in an incremental fashion. Doing so, for example, may help to share anomaly information among the various DLAs.

SCA502may also execute an anomaly scorer510that leverages anomaly database506and anomaly distance calculator508, to determine a reporting score for a particular anomaly detected and reported by DLA400. In various embodiments, these elements may operate in conjunction with one another as follows:1. Anomaly distance calculator508computes the distance(s) between the particular anomaly and the previously seen anomalies in anomaly database506(e.g., either in exact form or as approximations in the database). In some embodiments, lower bounds on distances between pairs of anomalies may be sufficient, and may be efficiently computed by anomaly distance calculator508using appropriate data structures such as k-d trees. Once computed, anomaly distance calculator508then provides the distances to anomaly scorer510.2. Anomaly scorer510may compute one or more relevance scores for the previously seen anomalies in anomaly database506(e.g., based as feedback from UI process518). Anomaly scorer510may then combine these relevance score(s) with the (lower bound on the) distance to the current anomaly under scrutiny into a single value score. In some cases, this can be performed according to a weighting function that discounts the relevance of an anomaly according to its distance to the anomalies in anomaly database506, such that the value score can be interpreted as the average relevance of similar anomalies.3. Anomaly scorer510may also compute a similarity score for the anomalies. For example, such a similarity score may be based on a weighting function that discounts an anomaly according to its distance to the current anomaly.4. Anomaly scorer510may combine the relevance and similarity scores into a single reporting score for the particular anomaly (e.g., by adding, multiplying, etc. the scores). Doing so allows for a final reporting score that takes into account both the diversity aspect of the particular anomaly (e.g., how new the anomaly actually is) and the relevance aspect of the particular anomaly (e.g., how relevant the anomaly may be to the user, based on prior user feedback). In one embodiment, anomaly scorer510may use a static weighting of the relevance and similarity scores, though not necessarily linear, to calculate the final reporting score. In another embodiment, the final reporting score may also take into account explicit feedback from UI process518on the reported anomalies in general, such as “Did you dislike this anomaly because it was too similar to past anomalies or because it was irrelevant?”

Usually, the final reporting score from anomaly scorer510will place non-negligible weighting on the diversity aspect to force exploration of the anomaly space. As would be appreciated, this is required to show new kind of anomalies to the user which is, in turn, necessary to get a good evaluation of the relevance everywhere in the anomaly space. Orthogonally, the weighting can also be dependent upon time and overall amount of feedbacks, such that the exploration is stronger when the system is beginning to operate.

Based on the final reporting score computed by anomaly scorer510, anomaly reporter512may determine whether to report the particular anomaly under analysis. For example, anomaly reporter512may only provide a notification regarding the anomaly to UI process518if the reporting score for the anomaly is above a predefined threshold. In further embodiments, UI process518may request anomalies that are above a specified reporting score threshold or the n-number of anomalies with the highest reporting scores (e.g., the user of client device504may opt to see the top ten anomalies, etc.).

In various embodiments, the scoring operations of anomaly reporter512, anomaly scorer510, anomaly distance calculator508, and anomaly database506may instead be implemented on DLA400, instead of centrally on SCA502. In other words, DLA400may itself determine whether to report a detected anomaly to SCA502, based on the reporting score of the anomaly.

In some embodiments, SCA502may execute a clustering engine514that performs clustering on detected anomalies based on their distance scores computed by anomaly distance calculator508. In turn, clustering engine514may report the clustering information to UI process518for review by the user/administrator. For instance, data exfiltration events/anomalies may be clustered together. The user of client device504may then label the clustered anomalies as “data exfiltration,” thus allowing future similar events to be directly qualified as such by SCA502. This clustering step may be performed using any number of clustering approaches, such as the Ordering Points to Identify the Clustering Structure (OPTICS) approach.

In yet another embodiment, SCA502may execute a DLA ranker516that ranks the various DLAs according to their ability to “see” anomalies with the highest degree of diversity. In other words, DLA ranker516may quantify how diverse the anomalies detected by a given DLA are in relation to one another. Indeed, in most highly scalable architectures, DLAs are subject to seeing anomalies that may be very similar to one another or, alternatively, a very diverse set of anomalies. By grouping remote agents as a function of diversity scores, this potentially allows for a SCA502to allocate resources to a given DLA400(e.g., WAN bandwidth, other networking resources, etc.), according to the diversity scores of its anomalies.

FIGS. 6A-6Billustrate examples of a device selectively reporting detected anomalies, according to various embodiments. As shown inFIG. 6A, assume that SCA502acts as a supervisory device over any number of deployed DLAs402a-402n(e.g., a first through nth DLA) in the network. For example, DLA400amay report a detected anomaly602to SCA502, DLA400nmay report a detected anomaly604to SCA502, etc. In particular, the DLAs may send notifications to SCA502indicative of detected anomalies which may include any or all of the available information regarding the anomaly. In turn, SCA502may report anomalies606to client device506for presentation to the user/administrator.

As noted above, anomalies may have any number of different underlying causes ranging from malware to simply previously unseen applications or behaviors in the network. Accordingly, reporting all anomalies to client device506may overwhelm a user/administrator. In various embodiments, the system may be configured to allow the user of client device506to provide feedback608regarding the reported anomalies606. For example, feedback608may specify the relevance of a particular anomaly to the user, the subjective similarity between two or more anomalies from the viewpoint of the user, a label for a cluster of anomalies, etc.

As shown inFIG. 6B, SCA502may use feedback608to control whether a particular anomaly610(e.g., as detected and reported by DLA400a) should be reported to client device506for review by the user/administrator. Such a determination may take into account the distance and/or similarity between anomaly610and previously detected anomalies, as well as the relevance of the previously detected anomalies to the user of client device506. For example, if anomaly610is nearly identical to a previously reported anomaly, that has a low relevancy score (e.g., based on feedback608from the user), SCA502may prevent anomaly610from being proactively reported to client device508. Conversely, SCA502may report anomaly610to client device506if anomaly610is very different from previously encountered anomalies (e.g., to explore the anomaly space for additional feedback from the user), if anomaly610is close to one or more prior anomalies that were of high relevance, etc. This allows the system to automatically control and explore the types of reported anomalies, while preventing the user from becoming overloaded with reported anomalies.

FIGS. 7A-7Billustrate examples of a device causing the allocation of network resources to a DLA, according to various embodiments. As shown inFIG. 7A, assume that SCA502receives notifications of anomalies702detected by DLA400a. In turn, SCA502may rank DLA400abased on the diversity or similarity of the reported anomalies702and may also take into account the corresponding relevance of the anomalies. As shown inFIG. 7B, SCA502may send one or more instructions704to the networking device(s) associated with DLA400a, to allocate networking resources based on the ranking of DLA400a. For example, if DLA400areports a very diverse set of anomalies702, SCA502may allocate additional resources to DLA400a, so that a larger anomaly space can be explored for purposes of reporting. Conversely, if the anomalies702reported by DLA400aare all very similar and of low relevance, SCA502may decrease the resources allocated to DLA400aor leave them at a default level.

FIG. 8illustrates an example simplified procedure for reporting a detected anomaly to a user interface, in a network in accordance with one or more embodiments described herein. For example, a non-generic, specifically configured device (e.g., device200) may perform procedure400by executing stored instructions (e.g., process248). The procedure800may start at step805, and continues to step810, where, as described in greater detail above, the device may receive a notification of a particular anomaly detected by a DLA that executes a machine learning-based anomaly detector to analyze traffic in the network. Such a notification may, for example, include information regarding the observed characteristics of the traffic that caused the DLA to deem the traffic anomalous.

At step815, as detailed above, the device may compute a distance between the particular anomaly from step810and one or more prior/previously detected anomalies. In some cases, the distance may be based on static rules that define the distances (e.g., the Euclidean distances between feature vectors, etc.). In further cases, the distance may be based on a dynamic model that is trained using a set of anomaly pairs that are labeled with distances (e.g., based on feedback from a user). In other words, the distance score may have an objective and/or subjective component to it. In some cases, the distance can also be used to compute a similarity score between the particular anomaly and another anomaly. For example, the similarity between the anomalies may drop exponentially as a function of their distance, etc.

At step820, the device may compute relevance scores for the one or more previously detected anomalies, as described in greater detail above. In many cases, the relevance score for a previously detected anomaly may be based on feedback from a user interface after reporting that anomaly. Notably, certain anomalies may be of greater importance and relevance to a user than others (e.g., when the anomaly is related malware or data exfiltration, as opposed to a newly deployed application, etc.).

At step825, as detailed above, the device may determine a final reporting score for the particular anomaly based on its distance(s) to the other anomalies and the relevance score(s) of the other anomalies. For example, if the particular anomaly is close/similar to a number of previously detected anomalies that all have high relevance, the device may compute a high relevance score for the particular anomaly, as well.

At step830, the device may report the particular anomaly to a user interface based on the reporting score for the anomaly, as described in greater detail above. For example, if the reporting score for the particular anomaly is above a given threshold, the device may provide information to the user interface regarding the anomaly, for review by a user. Conversely, if the reporting score is below the reporting threshold, the device may prevent the anomaly from being reported or at least on a push-basis. Procedure800then ends at step835.

The techniques described herein, therefore, allow for optimized reporting of detected anomalies in a network by taking into account both the relevance and diversity of the events. In some aspects, a supervisory device may selectively report the anomalies detected by any number of deployed anomaly detectors in the network, while still providing a consistent and non redundant aggregate view to the user of all events in the system.

While there have been shown and described illustrative embodiments that provide for the selection of anomalies for purposes of reporting, 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 anomaly 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 BGP, other suitable protocols may be used, accordingly.