802 Secure, Inc.

Garry Drummond

Konrad Grutzmacher

Clifford Fernandez

Michael Raggo

Paul Vilevac

Ashok Madanahalli

A threat monitoring and vulnerability management system is disclosed. The system includes one or more sensors configured to scan a frequency spectrum of a project 25 (P25) network and to collect data on the P25 network. The system further includes a server coupled to the sensors and configured to receive the collected data from the plurality of sensors, compare the collected data with previously stored historical data to determine whether an anomaly exists within data patterns of the collected data, responsive to determining that the anomaly exists, determine at least one of: whether use of a cloned radio that mimics an authorized connection occurs, whether jamming of a radio frequency (RF) communication occurs, or whether jamming of a voice communication occurs within the P25 network by comparing the collected data with preset thresholds, and send a real-time alert to a dispatch and control console unit coupled to the server and the P25 network in response to determining that some of the collected data exceeds at least one of the preset thresholds, such that the dispatch and control console unit provides one or more corrective actions to the P25 network.

Systems and methods for distributed P25 LMR radio vulnerability management and real-time interference monitoring

Methods and systems for threat monitoring and analysis are disclosed. Data is collected, over a protected network, from a data stream provided by at least one data source connecting to the protected network. Machine learning (ML) models are trained for the data stream utilizing the collected data. The ML models include a first ML model to establish a baseline value, an allowed threshold value range, and a threshold value for the data from the data source, and a second ML model to identify outlier data. The outlier data is outside of the allowed threshold value range from the baseline value and exceeds the threshold value in the data stream. The ML models are used to determine whether one or more anomalies indicating a cyber threat exist within the collected data. Responsive to determining that the anomalies exist, it is determined whether to provide a response action.

Methods and systems to monitor groups of sensory data for malicious behaviors through statistical analysis

A threat monitoring and vulnerability management system is disclosed. The system includes one or more sensors configured to scan a frequency spectrum of a project 25 (P25) network and to collect data on the P25network. The system further includes a server coupled to the sensors and configured to receive the collected data from the plurality of sensors, compare the collected data with previously stored historical data to determine whether an anomaly exists within data patterns of the collected data, responsive to determining that the anomaly exists, determine at least one of: whether use of a cloned radio that mimics an authorized connection occurs, whether jamming of a radio frequency (RF) communication occurs, or whether jamming of a voice communication occurs within the P25 network by comparing the collected data with preset thresholds, and send a real-time alert to a dispatch and control console unit coupled to the server and the P25 network in response to determining that some of the collected data exceeds at least one of the preset thresholds, such that the dispatch and control console unit provides one or more corrective actions to the P25 network.

Security is provided for enterprise local area networks (LANs) by pre-vetting and identifying the security characteristic and actions of any new wireless networks that tries to connect to a secure LAN network. The disclosure herein provides for identification and classification of IEEE 802.11 wireless networks by using monitoring sensor system within and managed by a centralized cloud. The monitoring sensors interrogate the network mimicking the behavior of known platforms, such as an end-user's workstation or mobile device followed by random actions simulating a human person. The response characteristics of the wireless network including the behavior patterns relating to the LAN system and human behavior are collected. The security system uses methods of classification provided through analysis of the collected information using methods including data science approaches, correlation analysis, and machine learning classification methods to determine the security classification of the newly detected wireless network.

Systems and methods for providing wireless access security by interrogation

A system and method are provided that enable identifying and trapping cyber security attacks via wireless connectivity on enterprise and corporate networks. A deceptive network emulation of a specific customer network is used to invite and draw in possible attackers. The attacker is also enticed to initiate attack on the emulated deceptive network. Packet header inspection and deep packet evaluation of the attack are used for generating possible signatures of the attacker and the attack. The information from deep packet inspection combined with the understanding of attack modality derived from enablement of attack on the deceptive network are used to generate detailed defensive methodologies, response capabilities and attack signatures, so that various types of cyber attacks including zero-day attacks from the attacker can be identified, prevented or addressed and responded to.

Growing community of inventors

Pleasanton, CA, United States of America

Garry Drummond