Paul M Long

US Patent:

20200293655, Sep 17, 2020

Filed:

Mar 5, 2020

Appl. No.:

16/810182

Inventors:

Paul J. Long - San Francisco CA, US
Ian L. Sayers - South Shields, GB

International Classification:

G06F 21/56
G06F 21/60
G06N 20/00

Abstract:

One embodiment of this invention describes a method and apparatus for the use of Machine Learning to efficiently detect, identify, prevent, and predict cyber-attacks on Low Power and Low Complexity Sensor (FIG. ) networks that have low data transmission requirements, something that all current Machine Learning techniques are unable to accomplish due to numerous restrictions when applied to Low Power and Low Complexity Sensors. Low Power and Low Complexity Sensors are frequently found in various Internet of Things (IOT) network architectures. The IOT is a network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators, and connectivity which enables them to connect and exchange data, providing more direct integration of the physical world into computer-based systems. However, this should not restrict the applicability of any potential embodiment of this invention as described in this patent application.A further understanding of the nature and the advantages of the particular embodiments disclosed herein may be realized by referencing the remaining portions to the specification.

US Patent:

20190199521, Jun 27, 2019

Filed:

Jul 26, 2017

Appl. No.:

15/660621

Inventors:

Ian L. Sayers - South Shields, GB
Paul J. Long - San Francisco CA, US

International Classification:

H04L 9/08
H04L 29/08
H04L 9/14
H04L 29/06

Abstract:

One embodiment of this invention describes a method and apparatus for the secure identification and validation of devices on a network using a low complexity method. In addition, any data transmitted between the devices to/from the network is secured by means of encryption techniques. The method as described herein is intended to protect and secure devices that might need to access a secured sensor or device type of network, for example an Internet of Things (IOT) network. However it could easily be adapted to other types of networks to provide comparable levels of security and protection.A further understanding of the nature and the advantages of particular embodiments disclosed herein may be realized by referencing the remaining portions of the specification and the attached drawings.

US Patent:

20190081936, Mar 14, 2019

Filed:

Apr 24, 2017

Appl. No.:

15/494993

Inventors:

Ian L. Sayers - South Shields, GB
Paul J. Long - San Francisco CA, US

International Classification:

H04L 29/06
H04W 60/00
H04L 9/08
H04W 12/08
H04W 12/04
H04W 4/80

Abstract:

One embodiment of this invention describes a method and apparatus for the secure identification and validation of low complexity sensors or devices on a network. In addition any data transmitted between the sensors/devices to/from the network is secured by means of encryption techniques. The method as described herein is intended to protect and secure sensors/devices that might transmit limited amounts of data and have reduced levels of onboard processing power. However it could easily be adapted to other types of networks to provide comparable levels of security and protection.A further understanding of the nature and the advantages of the particular embodiments disclosed herein may be realized by referencing the remaining portions to the specification and the attached drawings.

US Patent:

20190005237, Jan 3, 2019

Filed:

Jun 27, 2018

Appl. No.:

16/020573

Inventors:

Paul J. Long - San Francisco CA, US
Ian L. Sayers - South Shields, GB

International Classification:

G06F 21/56
G06N 3/08

Abstract:

One embodiment of this invention describes a method and apparatus for identifying, predicting, and preventing malicious attacks against low complexity sensors or devices (sensors/devices) on a network through the use of an Artificial Neural Network (ANN) which is defined as a connectionist system (i.e. interconnected networks) functioning as a computing system inspired by living neural networks (e.g. the human brain). An ANN employs Machine Learning computational models that, without being programmed with any task-specific rules, can “learn” capabilities such as image recognition by simply considering relevant examples. This approach is similar to how a person might learn a new task. There are many forms and types of malicious attacks that are sometimes more commonly referred to as cyber attacks. In one instance of this invention the malicious attacks are identified by organizing and classifying encryption keys and/or messages sent to or received from a sensor/device. Additionally this invention can predict emerging attack techniques against sensors/devices in order to prevent the spread of malicious attacks thereby protecting and securing critical network data. However it should be clear from the description of the invention that the method could easily be adapted to other types of networks to provide comparable levels of analysis and protection against malicious attacks.