Abstract: A framework to accurately and quickly verify the ownership of remotely-deployed deep learning models is provided without affecting model accuracy for normal input data. The approach involves generating a watermark, embedding the watermark in a local deep neural network (DNN) model by learning, namely, by training the local DNN model to learn the watermark and a predefined label associated therewith, and later performing a black-box verification against a remote service that is suspected of executing the DNN model without permission. The predefined label is distinct from a true label for a data item in training data for the model that does not include the watermark. Black-box verification includes simply issuing a query that includes a data item with the watermark, and then determining whether the query returns the predefined label.

Abstract: A computer-implemented method, a computer program product, and a computer system. The computer system installs and configures a virtual imitating resource in the computer system, wherein the virtual imitating resource imitates a set of resources in the computer system. Installing and configuring the virtual imitating resource includes modifying respective values of an installed version of the virtual imitating resource for an environment of the computer system, determining whether the virtual imitating resource is a static imitating resource or a dynamic imitating resource, and comparing a call graph of the evasive malware with patterns of dynamic imitating resources on a database. The computer system returns a response from an appropriate element of the virtual imitating resource, in response to a call from the evasive malware to a real computing resource, return, by the computer system.

Abstract: Mechanisms for identifying a pattern of computing resource activity of interest, in activity data characterizing activities of computer system elements, are provided. A temporal graph of the activity data is generated and a filter is applied to the temporal graph to generate one or more first vector representations, each characterizing nodes and edges within a moving window defined by the filter. The filter is applied to a pattern graph representing a pattern of entities and events indicative of the pattern of interest, to generate a second vector representation. The second vector representation is compared to the one or more first vector representations to identify one or more nearby vectors, and one or more corresponding subgraph instances are output to an intelligence console computing system as inexact matches of the temporal graph.

Abstract: Mechanisms are provided to implement a neural flow attestation engine and perform computer model execution integrity verification based on neural flows. Input data is input to a trained computer model that includes a plurality of layers of neurons. The neural flow attestation engine records, for a set of input data instances in the input data, an output class generated by the trained computer model and a neural flow through the plurality of layers of neurons to thereby generate recorded neural flows. The trained computer model is deployed to a computing platform, and the neural flow attestation engine verifies the execution integrity of the deployed trained computer model based on a runtime neural flow of the deployed trained computer model and the recorded neural flows.

Abstract: Mechanisms are provided to implement a single input, multi-factor authentication (SIMFA) system. The SIMFA system receives a user input for authenticating a user via a single input channel and provides the user input to first authentication logic of an explicit channel of the SIMFA system, where in the first authentication logic performs a knowledge authentication operation on the user input. The SIMFA system further provides the user input to second authentication logic of one or more side channels of the SIMFA system, where the second authentication logic performs authentication on non-knowledge-based characteristics of the user input. The SIMFA system combines results of the first authentication logic and the second authentication logic to generate a final determination of authenticity of the user. The SIMFA system generates an output indicating whether the user is an authentic user or a non-authentic user based on the final determination of authenticity of the user.

Abstract: Adversarial input detection and purification (AIDAP) preprocessor and deep learning computer model mechanisms are provided. The deep learning computer model receives input data and processes it to generate a first pass output that is output to the AIDAP preprocessor. The AIDAP preprocessor determines a discriminative region of the input data based on the first pass output and transforms a subset of elements in the discriminative region to modify a characteristic of the elements and generate a transformed input data. The deep learning computer model processes the transformed input data to generate a second pass output that is output to the AIDAP preprocessor which detects an adversarial input or not based on a comparison of the first pass and second pass outputs. If an adversarial input is detected, a responsive action that mitigates effects of the adversarial input is performed.

Abstract: Mechanisms are provided for detecting abnormal system call sequences in a monitored computing environment. The mechanisms receive, from a computing system resource of the monitored computing environment, a system call of an observed system call sequence for evaluation. A trained recurrent neural network (RNN), trained to predict system call sequences, processes the system call to generate a prediction of a subsequent system call in a predicted system call sequence. Abnormal call sequence logic compares the subsequent system call in the predicted system call sequence to an observed system call in the observed system call sequence and identifies a difference between the predicted system call sequence and the observed system call sequence based on results of the comparing. The abnormal call sequence logic generates an alert notification in response to identifying the difference.

Abstract: Advanced persistent threats to a mobile device are detected and prevented by leveraging the built-in mandatory access control (MAC) environment in the mobile operating system in a “stateful” manner. To this end, the MAC mechanism is placed in a permissive mode of operation wherein permission denials are logged but not enforced. The mobile device security environment is augmented to include a monitoring application that is instantiated with system privileges. The application monitors application execution parameters of one or more mobile applications executing on the device. These application execution parameters including, without limitation, the permission denials, are collected and used by the monitoring application to facilitate a stateful monitoring of the operating system security environment. By assembling security-sensitive events over a time period, the system identifies an advanced persistent threat (APT) that otherwise leverages multiple steps using benign components.

Abstract: An automated method for cyberattack detection and prevention in an endpoint. The technique monitors and protects the endpoint by recording inter-process events, creating an inter-process activity graph based on the recorded inter-process events, matching the inter-process activity (as represented in the activity graph) against known malicious or suspicious behavior (as embodied in a set of one or more pattern graphs), and performing a post-detection operation in response to a match between an inter-process activity and a known malicious or suspicious behavior pattern. Preferably, matching involves matching a subgraph in the activity graph with a known malicious or suspicious behavior pattern as represented in the pattern graph. During this processing, preferably both direct and indirect inter-process activities at the endpoint (or across a set of endpoints) are compared to the known behavior patterns.

Abstract: A computer-implemented method, a computer program product, and a computer system. The computer system installs and configures a virtual imitating resource in the computer system, wherein the virtual imitating resource imitates a set of resources in the computer system. Installing and configuring the virtual imitating resource includes modifying respective values of an installed version of the virtual imitating resource for an environment of the computer system, determining whether the virtual imitating resource is a static imitating resource or a dynamic imitating resource, and comparing a call graph of the evasive malware with patterns of dynamic imitating resources on a database. The computer system returns a response from an appropriate element of the virtual imitating resource, in response to a call from the evasive malware to a real computing resource, return, by the computer system.

Abstract: Deep learning training service framework mechanisms are provided. The mechanisms receive encrypted training datasets for training a deep learning model, execute a FrontNet subnet model of the deep learning model in a trusted execution environment, and execute a BackNet subnet model of the deep learning model external to the trusted execution environment. The mechanisms decrypt, within the trusted execution environment, the encrypted training datasets and train the FrontNet subnet model and BackNet subnet model of the deep learning model based on the decrypted training datasets. The FrontNet subnet model is trained within the trusted execution environment and provides intermediate representations to the BackNet subnet model which is trained external to the trusted execution environment using the intermediate representations. The mechanisms release a trained deep learning model comprising a trained FrontNet subnet model and a trained BackNet subnet model, to the one or more client computing devices.

Abstract: Mechanisms are provided for executing a trained deep learning (DL) model. The mechanisms receive, from a trained autoencoder executing on a client computing device, one or more intermediate representation (IR) data structures corresponding to training input data input to the trained autoencoder. The mechanisms train the DL model to generate a correct output based on the IR data structures from the trained autoencoder, to thereby generate a trained DL model. The mechanisms receive, from the trained autoencoder executing on the client computing device, a new IR data structure corresponding to new input data input to the trained autoencoder. The mechanisms input the new IR data structure to the trained DL model executing on the deep learning service computing system, to generate output results for the new IR data structure. The mechanisms generate an output response based on the output results, which is transmitted to the client computing device.

Abstract: Using a deep learning inference system, respective similarities are measured for each of a set of intermediate representations to input information used as an input to the deep learning inference system. The deep learning inference system includes multiple layers, each layer producing one or more associated intermediate representations. Selection is made of a subset of the set of intermediate representations that are most similar to the input information. Using the selected subset of intermediate representations, a partitioning point is determined in the multiple layers used to partition the multiple layers into two partitions defined so that information leakage for the two partitions will meet a privacy parameter when a first of the two partitions is prevented from leaking information. The partitioning point is output for use in partitioning the multiple layers of the deep learning inference system into the two partitions.

Abstract: Approaches to deactivating evasive malware. In an approach, a computer system installs an imitating resource in the computer system and the imitating resource creates an imitating environment of malware analysis, wherein the imitating resource causes the evasive malware to respond to the imitating environment of the malware analysis as to a real environment of the malware analysis. In the imitating environment of malware analysis, the evasive malware determines not to perform malicious behavior. In another approach, a computer system intercepts a call from the evasive malware to a resource on the computer system and returns a virtual resource to the call, wherein in the virtual resource one or more values of the resource on the computer system are modified.

Abstract: Mechanisms are provided to implement a single input, multi-factor authentication (SIMFA) system. The SIMFA system receives a user input for authenticating a user via a single input channel and provides the user input to first authentication logic of an explicit channel of the SIMFA system, where in the first authentication logic performs a knowledge authentication operation on the user input. The SIMFA system further provides the user input to second authentication logic of one or more side channels of the SIMFA system, where the second authentication logic performs authentication on non-knowledge-based characteristics of the user input. The SIMFA system combines results of the first authentication logic and the second authentication logic to generate a final determination of authenticity of the user. The SIMFA system generates an output indicating whether the user is an authentic user or a non-authentic user based on the final determination of authenticity of the user.

Abstract: Mechanisms are provided to implement an enhanced privacy deep learning system framework (hereafter “framework”). The framework receives, from a client computing device, an encrypted first subnet model of a neural network, where the first subnet model is one partition of multiple partitions of the neural network. The framework loads the encrypted first subnet model into a trusted execution environment (TEE) of the framework, decrypts the first subnet model, within the TEE, and executes the first subnet model within the TEE. The framework receives encrypted input data from the client computing device, loads the encrypted input data into the TEE, decrypts the input data, and processes the input data in the TEE using the first subnet model executing within the TEE.

Abstract: A framework to accurately and quickly verify the ownership of remotely-deployed deep learning models is provided without affecting model accuracy for normal input data. The approach involves generating a watermark, embedding the watermark in a local deep neural network (DNN) model by learning, namely, by training the local DNN model to learn the watermark and a predefined label associated therewith, and later performing a black-box verification against a remote service that is suspected of executing the DNN model without permission. The predefined label is distinct from a true label for a data item in training data for the model that does not include the watermark. Black-box verification includes simply issuing a query that includes a data item with the watermark, and then determining whether the query returns the predefined label.

Abstract: Advanced persistent threats to a mobile device are detected and prevented by leveraging the built-in mandatory access control (MAC) environment in the mobile operating system in a “stateful” manner. To this end, the MAC mechanism is placed in a permissive mode of operation wherein permission denials are logged but not enforced. The mobile device security environment is augmented to include a monitoring application that is instantiated with system privileges. The application monitors application execution parameters of one or more mobile applications executing on the device. These application execution parameters including, without limitation, the permission denials, are collected and used by the monitoring application to facilitate a stateful monitoring of the operating system security environment. By assembling security-sensitive events over a time period, the system identifies an advanced persistent threat (APT) that otherwise leverages multiple steps using benign components.

Abstract: Approaches to deactivating evasive malware. In an approach, a computer system installs an imitating resource in the computer system and the imitating resource creates an imitating environment of malware analysis, wherein the imitating resource causes the evasive malware to respond to the imitating environment of the malware analysis as to a real environment of the malware analysis. In the imitating environment of malware analysis, the evasive malware determines not to perform malicious behavior. In another approach, a computer system intercepts a call from the evasive malware to a resource on the computer system and returns a virtual resource to the call, wherein in the virtual resource one or more values of the resource on the computer system are modified.

Abstract: A system and method for achieving power isolation across different cloud tenants and workloads is provided. The system includes a model of per-workload power consumption and an approach for attributing power consumption for each container. It allows a cloud provider to detect abnormally high power usage caused by specific containers and/or tenants, and to neutralize the emerging power attacks that exploit information leakages in the public container cloud. The approach also enables the provider to bill tenants for their specific power usage. Thus, the technique herein provides a mechanism that operates to attribute power consumption data for each container to defend against emerging power attacks, as well as to make it feasible to develop a cloud billing model based on power usage. The mechanism defends against emerging power attacks in container cloud offerings by implementing in a power-based namespace workflow in an OS kernel.