International Business Machines Corporation

Twistlock Ltd.

Palo Alto Networks, Inc.

Akamai Technologies, Inc.

Ory Segal

Omer Tripp

Omri Weisman

Shaked Yosef Zin

Elad Shuster

Avraham Shulman

Adi Sharabani

Yuri Shapira

Daniel Kalman

Roi Saltzman

Benny Nissimov

Marco Pistoia

Liron Levin

Guy Podjarny

Isaac Schnitzer

Roee Hay

Yinnon Avraham Haviv

David Senecal

Andrew Kahn

Duc Nguyen

Takaaki Tateishi

Iosif Viorel Onut

Paul Ionescu

Stephen Fink

Frank Tip

Manu Sridharan

Wayne Duncan Smith

Sachiko Yoshihama

Guy-Vincent Jourdan

Gregor Von Bochmann

Shinya Kawanaka

Ariel Sakin

Orli Gan

Rob Calendino

Craig Robert Earl Conboy

Shlomi Shamir

A configuration of a cloud application exposed via a public IP address is duplicated with modifications to include a private IP address to expose the application internally. The original configuration is updated so that external network traffic sent to the application is redirected to and distributed across agents running on nodes of a cloud cluster by which web application firewalls (WAFs) are implemented. A set of agents for which the respective WAFs should inspect the redirected network traffic are selected based on cluster metrics, such as network and resource utilization metrics. The redirected network traffic targets a port allocated to the agents that is unique to the application, where ports are allocated on a per-application basis so each of the agents can support WAF protection for multiple applications. Network traffic which a WAF allows to pass is directed from the agent to the application via its private IP address.

Dynamically scalable application firewall deployment for cloud native applications

A system and methods for protecting a serverless application, the system including: (a) a serverless application firewall configured to inspect input of the serverless function so as to ascertain whether the input contains malicious, suspicious or abnormal data; and (b) a behavioral protection engine configured to monitor behaviors and actions of the serverless functions during execution thereof.

Protecting serverless applications

This disclosure describes a technique to fingerprint TLS connection information to facilitate bot detection. The notion is referred to herein as 'TLS fingerprinting.' Preferably, TLS fingerprinting herein comprises combining different parameters from the initial “Hello” packet send by the client. In one embodiment, the different parameters from the Hello packet that are to create the fingerprint (the “TLS signature”) are: record layer version, client version, ordered TLS extensions, ordered cipher list, ordered elliptic curve list, and ordered signature algorithms list. Preferably, the edge server persists the TLS signature for the duration of a session.

Bot detection in an edge network using transport layer security (TLS) fingerprint

A method for securing a serverless application including: (a) receiving a list of components which make up the serverless application and one or more intended usage flows of the serverless application; (b) creating and applying a security policy for each component of the serverless application, the security policy denying all access requests except from authorized components, wherein the authorized components are selected based on access requirements dictated by the one or more intended usage flows.

Securing application behavior in serverless computing

Based on analyzing a serverless function associated with a first role, a set of security permissions granted to the serverless function is identified based on the first role and a first attribute of the serverless function. A least privilege role indicating a set of least privilege security permissions for the serverless function is generated based, at least in part, on the first attribute. Based on comparing the least privilege role with the first role, it is determined if the set of security permissions granted to the serverless function is more permissive than the set of least privilege security permissions. Based on determining that the set of security permissions granted to the serverless function is more permissive than the set of least privilege security permissions, the first role is reported as over-permissive.

Identifying serverless functions with over-permissive roles

A system facilitates detection of malicious properties of software packages. A generic application which comprises known functionality into which a software package has been included is analyzed through a static analysis and/or dynamic analysis, which is performed based on executing the generic application in a controlled environment. The static analysis and/or dynamic analysis are performed to determine whether one or more properties associated with the software package comprise deviations from the known behavior of the generic application. Behavior deviations identified based on the static and/or dynamic analysis are associated with a score. An aggregate score is calculated for the software package based on the scores which have been assigned to the identified behavior deviations and may be adjusted based on a reputation multiplier determined based on metadata of the software package. If the aggregate score of the software package exceeds a score threshold, the software package is flagged as malicious.

Software package analysis for detection of malicious properties

A method of bot detection in a computer network leverages a machine learning system. The machine learning system receives a fingerprint derived at a server, the server having extracted a set of transport layer security parameters received from a client and processed the set parameters into the fingerprint. Based at least in part on the fingerprint, the learning system determines whether the client is likely to be a bot as opposed to a human user. The system generates and returns to the server as score having a first value when the fingerprint is determined to be associated with a good client, and having a second value when the fingerprint is determined to be associated with a bot. Based on the score received from the machine learning system, the server takes a configured action with respect to the client.

The disclosed serverless security access control system leverages static analysis information about application code and runtime information to create and assign on-the-fly transient serverless function roles. A default role can be initially assigned to serverless functions of the application. The default role allows the function to communicate with a security access broker. The access broker accesses least privilege information about an invoked serverless function and then creates and assigns a transient role to the serverless function based on that information. The short life of the role reduces and possibly eliminates the security risk of an over-permissive role. The access broker can update the least privilege information based on updated analysis of the application code and runtime information to allow flexibility and adaptation over executions.

On-the-fly creation of transient least privileged roles for serverless functions

To dynamically determine and apply WAF protections for an application deployed to the cloud, exposed entities, are identified. The identified entities are further evaluated to determine whether the application is eligible for WAF protection based on whether the application uses a protocol that is compatible with WAF protection. If the application is eligible for WAF protection, after instantiating a WAF, WAF protections that should be enabled or disabled are determined based on characteristics of the application that are identified at runtime. The WAF can then be configured based on the identified protections such that those which are pertinent to the application will be enabled, while those which are not applicable to the application and thus will not be used are disabled. As a result, security provided by the WAF for a cloud application is tailored to the application based on information about the application gathered in the cloud deployment environment.

Dynamic application firewall configuration for cloud native applications

A method, computer program product, and computer system for performing, at a computing device, an analysis of a web application. A response is annotated by the web application with coverage data based upon, at least in part, the analysis, wherein the coverage data indicates which actions have been performed on the web application and which actions have not been performed on the web application according to results of the analysis. The response that includes the coverage data is shared with one or more users.

Collaborative application testing

Growing community of inventors

Tel Aviv, Israel

Ory Segal