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    SYSTEM AND METHOD FOR MACHINE LEARNING ASSISTED SECURITY ANALYSIS OF 5G NETWORK CONNECTED SYSTEMS
    1.
    发明公开
    SYSTEM AND METHOD FOR MACHINE LEARNING ASSISTED SECURITY ANALYSIS OF 5G NETWORK CONNECTED SYSTEMS 审中-公开

    公开(公告)号：US20230422039A1

    公开(公告)日：2023-12-28

    申请号：US18035847

    申请日：2021-11-08

    Applicant: The Trustees of Princeton University

    Inventor： Tanujay SAHA ,  Niraj K. JHA ,  Najwa AARAJ

    IPC: H04W12/122 ,  H04L9/40 ,  G06N7/01

    CPC classification number: H04W12/122 ,  H04L63/1433 ,  G06N7/01

    Abstract: According to various embodiments, a method for detecting security vulnerabilities in a fifth generation core network (5GCN) is disclosed. The method includes constructing an attack graph from a plurality of regular expressions. Each regular expression corresponds to a sequence of system level operations for a known 5GCN attack. The method further includes performing a linear search on the attack graph to determine unexploited 5GCN attack vectors where path in the attack graph that does not represent a known 5GCN attack vector represents an unexploited 5GCN attack vector. The method also includes applying a trained machine learning module to the attack graph to predict new 5GCN attacks. The trained machine learning module is configured to determine a feasibility of linking unconnected nodes in the attack graph to create a new branch representing a new 5GCN vulnerability exploit.

    INVERSE SYSTEM DESIGN FOR CONSTRAINED MULTI-OBJECTIVE OPTIMIZATION
    2.
    发明申请
    INVERSE SYSTEM DESIGN FOR CONSTRAINED MULTI-OBJECTIVE OPTIMIZATION 有权

    公开(公告)号：US20250117552A1

    公开(公告)日：2025-04-10

    申请号：US18799055

    申请日：2023-02-10

    Applicant: The Trustees of Princeton University

    Inventor： Prerit TERWAY ,  Niraj K. JHA

    IPC: G06F30/27 ,  G06F111/06

    Abstract: A design methodology and tool called INFORM are provided that use a two-phase approach for sample-efficient constrained multi-objective optimization of real-world nonlinear systems. In the first optional phase, one may modify a genetic algorithm (GA) to make the design process sample-efficient, and may inject candidate solutions into the GA population using inverse design methods. The inverse design techniques may be based on (i) a neural network verifier, (ii) a neural network, and (iii) a Gaussian mixture model. The candidate solutions for the next generation are thus a mix of those generated using crossover/mutation and solutions generated using inverse design. At the end of the first phase, one obtains a set of nondominated solutions. In the second phase, one chooses one or more solution(s) from the non-dominated solutions or another reference solution to further improve the objective function values using inverse design methods.

    SYSTEM AND METHOD FOR COMPACT, FAST, AND ACCURATE LSTMS
    3.
    发明申请
    SYSTEM AND METHOD FOR COMPACT, FAST, AND ACCURATE LSTMS 有权

    公开(公告)号：US20210133540A1

    公开(公告)日：2021-05-06

    申请号：US17058428

    申请日：2019-03-14

    Applicant: The Trustees of Princeton University

    Inventor： Xiaoliang DAI ,  Hongxu YIN ,  Niraj K. JHA

    IPC: G06N3/04 ,  G06N3/063

    Abstract: According to various embodiments, a method for generating an optimal hidden-layer long short-term memory (H-LSTM) architecture is disclosed. The H-LSTM architecture includes a memory cell and a plurality of deep neural network (DNN) control gates enhanced with hidden layers. The method includes providing an initial seed H-LSTM architecture, training the initial seed H-LSTM architecture by growing one or more connections based on gradient information and iteratively pruning one or more connections based on magnitude information, and terminating the iterative pruning when training cannot achieve a predefined accuracy threshold.

    SYSTEM AND METHOD FOR GRAPHICAL RETICULATED ATTACK VECTORS FOR INTERNET OF THINGS AGGREGATE SECURITY (GRAVITAS)
    4.
    发明公开
    SYSTEM AND METHOD FOR GRAPHICAL RETICULATED ATTACK VECTORS FOR INTERNET OF THINGS AGGREGATE SECURITY (GRAVITAS) 审中-公开

    公开(公告)号：US20230328094A1

    公开(公告)日：2023-10-12

    申请号：US18027765

    申请日：2021-09-20

    Applicant: The Trustees of Princeton University

    Inventor： Jacob BROWN ,  Tanujay SAHA ,  Niraj K. JHA

    IPC: H04L9/40

    CPC classification number: H04L63/1433 ,  H04L63/1416 ,  H04L63/1425 ,  H04L63/1441

    Abstract: According to various embodiments, a system for detecting security vulnerabilities in at least one of cyber-physical systems (CPSs) and Internet of Things (IoT) devices is disclosed. The system includes one or more processors configured to construct an attack directed acyclic graph (DAG) unique to each CPS or IoT device of the devices. The processors are further configured to generate an aggregate attack DAG from a classification of each device and a location of each device in network topology specified by a system administrator. The processors are also configured to calculate a vulnerability score and exploit risk score for each node in the aggregate attack DAG. The processors are further configured to optimize placement of defenses to reduce an adversary score of the aggregate attack DAG.

    SYSTEM AND METHOD FOR INCREMENTAL LEARNING USING A GROW-AND-PRUNE PARADIGM WITH NEURAL NETWORKS
    8.
    发明申请
    SYSTEM AND METHOD FOR INCREMENTAL LEARNING USING A GROW-AND-PRUNE PARADIGM WITH NEURAL NETWORKS 有权

    公开(公告)号：US20220222534A1

    公开(公告)日：2022-07-14

    申请号：US17613284

    申请日：2020-03-20

    Applicant: The Trustees of Princeton University

    Inventor： Xiaoliang DAI ,  Hongxu YIN ,  Niraj K. JHA

    IPC: G06N3/08 ,  G06N3/04

    Abstract: According to various embodiments, a method for generating a compact and accurate neural network for a dataset that has initial data and is updated with new data is disclosed. The method includes performing a first training on the initial neural network architecture to create a first trained neural network architecture. The method additionally includes performing a second training on the first trained neural network architecture when the dataset is updated with new data to create a second trained neural network architecture. The second training includes growing one or more connections for the new data based on a gradient of each connection, growing one or more connections for the new data and the initial data based on a gradient of each connection, and iteratively pruning one or more connections based on a magnitude of each connection until a desired neural network architecture is achieved.

    SYSTEM AND METHOD FOR SYNTHESIS OF COMPACT AND ACCURATE NEURAL NETWORKS (SCANN)
    9.
    发明申请
    SYSTEM AND METHOD FOR SYNTHESIS OF COMPACT AND ACCURATE NEURAL NETWORKS (SCANN) 有权

    公开(公告)号：US20220036150A1

    公开(公告)日：2022-02-03

    申请号：US17275949

    申请日：2019-07-12

    Applicant: The Trustees of Princeton University

    Inventor： Shayan HASSANTABAR ,  Zeyu WANG ,  Niraj K. JHA

    IPC: G06N3/04 ,  G06N3/08

    Abstract: According to various embodiments, a method for generating a compact and accurate neural network for a dataset is disclosed. The method includes providing an initial neural network architecture; performing a dataset modification on the dataset, the dataset modification including reducing dimensionality of the dataset; performing a first compression step on the initial neural network architecture that results in a compressed neural network architecture, the first compression step including reducing a number of neurons in one or more layers of the initial neural network architecture based on a feature compression ratio determined by the reduced dimensionality of the dataset; and performing a second compression step on the compressed neural network architecture, the second compression step including one or more of iteratively growing connections, growing neurons, and pruning connections until a desired neural network architecture has been generated.

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