Abstract: A system for performing an assessment of the robustness and resilience of an examined original ML model against model extraction attacks includes a computerized device having at least one processor, which is adapted to: train multiple candidate models MC with the external dataset D for each of the specified candidate learning algorithms a in Alg, where each candidate substitute model is trained on a subset of D corresponding to the evaluated ith query limit of the query budget constraint Q; evaluate the performance of each substitute model MC according to different evaluation methods ?Evaluation; and calculate the robustness of each substitute model, where smaller difference or high agreement/similarity rate between the performance of the original model and the substitute model indicates that the original and substitute models are similar to each other.

Abstract: A security assessment system is configured to provide a duplicated environment which duplicates an assessment target system comprising a plurality of physical components. The security assessment system includes a duplicated environment design circuitry and a duplicated environment construction circuitry. The duplicated environment design circuitry is configured to select a duplication level based on constraints specified by a user in order to design the duplicated environment to produce a designed result indicative of a duplicated environment design. The duplication level is indicative of any one of a simulation sub-module, an emulation sub-module, and a physical sub-module which are for reproducing the physical components of the assessment target system. The duplicated environment construction circuitry is configured to construct the duplicated environment based on the designed result. The duplicated environment includes components which are duplicated by one of the duplication level.

Abstract: A security assessment system is configured to provide a duplicated environment which duplicates an assessment target system comprising a plurality of physical components. The security assessment system includes a duplicated environment design circuitry and a duplicated environment construction circuitry. The duplicated environment design circuitry is configured to select a duplication level based on constraints specified by a user and effects associated with the physical components in order to design the duplicated environment to produce a designed result indicative of a duplicated environment design. The duplication level is indicative of any one of a simulation sub-module, an emulation sub-module, and a physical sub-module which are for reproducing the physical components of the assessment target system. The duplicated environment construction circuitry is configured to construct the duplicated environment based on the designed result.

Abstract: The knowledge generation apparatus (2000) obtains a plural pieces of attack result information (100), which includes a configuration of an attack performed on the computer environment, a configuration of the computer environment attacked, and a result of the attack. By comparing the obtained attack result information (100), the knowledge generation apparatus (2000) detects environment conditions, which is regarding the configuration of the computer environment that are necessary for the success of the attack. The knowledge generation apparatus (2000) performs selection on the detected environment conditions based on a selection rule (200), and generates the knowledge information (300) that includes the selected environment conditions. The selection rule represents a rule for determining whether to include the environment condition in the knowledge information (300), with respect to a feature of a set of attacks that are affected by the environment condition.

Abstract: A system for analyzing and clustering darknet traffic streams with word embeddings, comprising a data processing module which collects packets that are sent to non-existing IP addresses that belong to darknet's taps (blackholes) that are deployed over the internet: a port embedding module for performing port sequence embeddings by using a word embedding algorithm on the port sequences extracted from the data processing module while transforming the port sequences into a meaningful numerical feature vectors: a clustering module for performing temporal clustering of the feature vectors over time; and an alert logic and visualization module visualizes the data and provides alerts regarding a cluster that an analyst classified as malicious in the past.

Abstract: An optimized limited-area WiFi network includes a plurality of Internet connectable devices, a wireless router for facilitating Internet connectivity, and a dynamically positionable signal coverage enhancer configured with an onboard processor, a signal coverage enhancing element, and mobile means. The signal coverage enhancer is repositionable, by the mobile means in response to a command signal transmitted by the processor, to a determined location of the limited-area WiFi network that is sufficiently close to one or more of the devices, such that an amplified signal produced by the signal coverage enhancing element which amplifies a wireless signal transmitted by the router maintains uninterrupted Internet operation of the to one or more devices.

Abstract: An attack information processing apparatus (10) includes an extraction unit (11) configured to extract first and second attack knowledge pieces indicating conditions of a cyber attack from first and second attack information pieces including descriptions of the cyber attack, a determination unit (12) configured to determine similarity between the first and second attack information pieces, and a complementing unit (13) configured to complement the first attack knowledge piece with the second attack knowledge piece based on the determined similarity.

Abstract: Provided herein are a method and device for detection of anomalous instructions sent from a controller of a medical device, to be received by a medical device. The method and the device utilize a dual layer architecture including a first, unsupervised detection layer and a second, supervised detection layer, wherein the layers are applied to the received instructions in series to efficiently detect anomalous instruction prior to the instructions reaching the medical device.

Abstract: A system and methods are provided for determining a vehicle action during a phantom projection attack, including processing a received image to identify a traffic object, and creating from the received image multiple processed images that are applied to respective neural network (NN) models. Latent representations of the multiple processed images from each of the NN models are then fed to a combiner model trained to determine whether the latent representations indicate a phantom projection attack, and, responsively to a determination of a phantom projection attack, issuing a phantom projection indicator.

Abstract: Systems and methods are provided for detecting anomalous messages on a multipoint serial communications bus by extracting features from a first and a second message, including a time delay between the first and the second messages and, for each message, a sender address, a recipient address, a bus number, and a word count. A message transition pattern including the extracted features is generated. A probability of occurrence of the message transition pattern is determined by comparing the message transition pattern to a pattern dictionary, and the second message is determined to be anomalous when the probability is less than a predetermined threshold.

Abstract: A signature verification system, which comprises a plurality of worn devices of signing users, each provided with one or more motion sensors, and a processor for receiving motion signals from the sensors, the processor is adapted to define a set of features that describe a signature and distinguish one signature from another; perform a training phase by obtaining motion signals from one or more motion sensors of the worn devices; training a machine learning classifier using the instances and labels; obtain motion signals from motion sensors of the a worn device, the motion being of an allegedly genuine signature of one of the users; scale and domain transform the allegedly genuine signature; calculate values of the features describing the allegedly genuine signature with respect to scaled and transformed reference signatures of the one of the users; and apply the trained classifier on the feature values, thereby classifying the allegedly genuine signature as genuine or forged.

Abstract: A system for bias estimation in Artificial Intelligence (AI) models using a pre-trained unsupervised deep neural network, comprising a bias vector generator implemented by at least one processor that executes an unsupervised DNN with a predetermined loss function. The bias vector generator is adapted to store a given ML model to be examined, with predetermined features; store a test-set of one or more test data samples being input data samples; receive a feature vector consisting of one or more input samples; output a bias vector indicating the degree of bias for each feature, according to said one or more input samples. The system also comprises a post-processor which is adapted to receive a set of bias vectors generated by said bias vector generator; process said bias vectors; calculate a bias estimation for every feature of said ML model, based on predictions of said ML model; provide a final bias estimation for each examined feature.

Abstract: A system for the assessment of robustness and fairness of AI-based ML models, comprising a data/model profiler for creating an evaluation profile in the form of data and model profiles, based on the dataset and the properties of the ML model; a test recommendation engine that receives data and model profiles from the data/model profiler and recommends the relevant tests to be performed; a test repository that contains all the tests that can be examined; a test execution environment for gathering data related to all the tests that were recommended by the test recommendation engine; a final fairness score aggregation module for aggregating the executed tests results into a final fairness score of the examined model and dataset.

Abstract: A method of monitoring network traffic in a communication network with a sentinel module to detect malicious activity is described. A gateway sentinel module receives network traffic directed through a gateway installed for a local distribution of the network, the gateway connecting the local distribution of the network to a core of the network. Malicious activity in the local distribution is detected based on a combination of: a local machine-learning model for identifying malicious activity in the local distribution, the local machine-learning model modelling network traffic from the local distribution; and a global machine-learning model. The global machine-learning model models network traffic from a plurality of local distributions of the network based training data from a plurality of local sentinel modules executed on a respective plurality of computing nodes. The computing nodes respectively receive network traffic from the plurality of location distributions.

Abstract: A system and method for identifying if a drone is illegitimately video streaming a location, object or person. An interceptor intercepts radio transmissions and identifies if a radio transmission is an FPV video transmission. A watermarker is used for emitting a periodic physical stimulus at a given frequency for a given duration. The system then verifies if there is a correlation between the video transmission and the periodic physical stimulus; and issues a privacy invasion attack notification if such a correlation is determined between the video transmission and the periodic physical stimulus.

Abstract: A system for testing Machine Learning (ML) and deep learning models for robustness, and durability against adversarial bias and privacy attacks, comprising a Project Repository for storing metadata of ongoing projects each of which having a defined project policy, and created ML models and data sources being associated with the ongoing projects; a Secure Data Repository, for storing training and testing datasets and models used in each project for evaluating the robustness of the each project; a Data/Model Profiler for creating a profile, based on the settings and configurations of the datasets and the models; a Test Recommendation Engine for recommending the relevant and most indicative attacks/tests for each examined model and for creating indicative and effective test suites; a Test/Attack Ontology module for storing all attacks/tests with their metadata and mapping the attacks/tests to their corresponding settings and configurations; an Attack Repository for storing the implemented tests/attacks.

Abstract: A security assessment system is configured to provide a duplicated environment which duplicates an assessment target system comprising a plurality of physical components. The security assessment system includes a duplicated environment design circuitry and a duplicated environment construction circuitry, The duplicated environment design circuitry is configured to select a duplication level based on constraints specified by a user in order to design the duplicated environment to produce a designed result indicative of a duplicated environment design. The duplication level is indicative of any one of a simulation sub-module, an emulation sub-module, and a physical sub-module which are for reproducing the physical components of the assessment target system. The duplicated environment construction circuitry is configured to construct the duplicated environment based on the designed result.

Abstract: The invention relates to a system for protecting IoT devices from malicious code, which comprises: (a) a memory extracting module at each of said IoT devices, for extracting a copy of at least a portion of the memory content from the IoT device, and sending the same to an in-cloud server; and (b) an in-cloud server for receiving said memory content, and performing an integrity check for a possible existance of malicious code within said memory content.

Abstract: An information collection system includes: a side-channel information processing unit that derives load information to estimate an impact on the availability of a target machine for active scanning by using side-channel data; an allow list generation unit that generates an allow list including a scan timing and a scan range in which the availability is not affected, the allow list generation unit generating the allow list based on the load information; and an allow list storage unit that stores the allow list. An active scan of the target machine is executed by referencing the allow list stored in the allow list storage unit to collect asset information of the target machine.

Abstract: A method and a system are provided for determining a likelihood of a metabolic pathway existing in an organism, including: calculating a feature vector for each metabolic pathway of metabolic pathways known to exist and not known to exist in the organism, wherein elements of the feature vectors are network properties of the metabolic pathways mapped to a metabolite concentration correlation network (CN); training a supervised machine learning model for classifying metabolic pathways as existing or not existing in the organism, according to the known and unknown feature vectors; determining, based on mapping to the CN, a feature vector of a proposed metabolic pathway, and feeding the feature vector of the proposed metabolic pathway to the trained SML model, to determine a likelihood of the proposed metabolic pathway existing in the organism.