Systems, methods, and storage media for detecting a security intrusion of a network device are disclosed. Exemplary implementations may include a method involving, in the network device including a processor, monitor a light signal associated with a security enabled port of the network device; and in response to detecting a change in the light signal, initiate a security alert.

A system and method consistent with the present disclosure provides for automated line monitoring system (LMS) baselining that enables capturing and updating of operational parameters specific to each repeater and associated undersea elements based on high loss loopback (HLLB) data. The captured operational parameters may then be utilized to satisfy queries targeting specific undersea elements in a Command-Response (CR) fashion. Therefore, command-response functionality may be achieved without the added cost, complexity and lifespan issues related to deploying undersea elements with on-board CR circuitry. As generally referred to herein, operational parameters include any parameter that may be derived directly or indirectly from HLLB data. Some example non-limiting examples of operational parameters include span gain loss, input power, output power, gain, and gain tilt.

A system and method consistent with the present disclosure provides for automated line monitoring system (LMS) baselining that enables capturing and updating of operational parameters specific to each repeater and associated undersea elements based on high loss loopback (HLLB) data. The captured operational parameters may then be utilized to satisfy queries targeting specific undersea elements in a Command-Response (CR) fashion. Therefore, command-response functionality may be achieved without the added cost, complexity and lifespan issues related to deploying undersea elements with on-board CR circuitry. As generally referred to herein, operational parameters include any parameter that may be derived directly or indirectly from HLLB data. Some example non-limiting examples of operational parameters include span gain loss, input power, output power, gain, and gain tilt.

In general, a system and method consistent with the present disclosure provides automated line monitoring using a machine learning fault classifier for determining whether a signature associated with the high loss loopback (HLLB) data matches a predetermined fault signature. The fault classifier may be applied to signatures generated in response to line monitoring signals of two different wavelengths. A fault may be reported only if the fault classifier indicates a fault in response to the signature for both wavelengths. A second fault classifier may also be used and a fault may be reported only if both the first and second fault classifiers indicate a fault in response to the signature for both wavelengths. A system consistent with the present disclosure may also, or alternatively, be configured to report the value of a pump degradation, span loss, or repeater failure fault, and may also, or alternatively, report the directionality of a span loss fault or the location of a fiber break fault.

In general, a system and method consistent with the present disclosure provides automated line monitoring using a machine learning fault classifier for determining whether a signature associated with the high loss loopback (HLLB) data matches a predetermined fault signature. The fault classifier may be applied to signatures generated in response to line monitoring signals of two different wavelengths. A fault may be reported only if the fault classifier indicates a fault in response to the signature for both wavelengths. A second fault classifier may also be used and a fault may be reported only if both the first and second fault classifiers indicate a fault in response to the signature for both wavelengths. A system consistent with the present disclosure may also, or alternatively, be configured to report the value of a pump degradation, span loss, or repeater failure fault, and may also, or alternatively, report the directionality of a span loss fault or the location of a fiber break fault.

In general, a system and method consistent with the present disclosure provides automated line monitoring using a machine learning fault classifier for determining whether a signature associated with the high loss loopback (HLLB) data matches a predetermined fault signature. The fault classifier may be applied to signatures generated in response to line monitoring signals of two different wavelengths. A fault may be reported only if the fault classifier indicates a fault in response to the signature for both wavelengths. A second fault classifier may also be used and a fault may be reported only if both the first and second fault classifiers indicate a fault in response to the signature for both wavelengths. A system consistent with the present disclosure may also, or alternatively, be configured to report the value of a pump degradation, span loss, or repeater failure fault, and may also, or alternatively, report the directionality of a span loss fault or the location of a fiber break fault.

In general, a system and method consistent with the present disclosure provides automated line monitoring using a machine learning fault classifier for determining whether a signature associated with the high loss loopback (HLLB) data matches a predetermined fault signature. The fault classifier may be applied to signatures generated in response to line monitoring signals of two different wavelengths. A fault may be reported only if the fault classifier indicates a fault in response to the signature for both wavelengths. A second fault classifier may also be used and a fault may be reported only if both the first and second fault classifiers indicate a fault in response to the signature for both wavelengths. A system consistent with the present disclosure may also, or alternatively, be configured to report the value of a pump degradation, span loss, or repeater failure fault, and may also, or alternatively, report the directionality of a span loss fault or the location of a fiber break fault.

A system and method consistent with the present disclosure provides for automated line monitoring system (LMS) baselining that enables capturing and updating of operational parameters specific to each repeater and associated undersea elements based on high loss loopback (HLLB) data. The captured operational parameters may then be utilized to satisfy queries targeting specific undersea elements in a Command-Response (CR) fashion. Therefore, command-response functionality may be achieved without the added cost, complexity and lifespan issues related to deploying undersea elements with on-board CR circuitry. As generally referred to herein, operational parameters include any parameter that may be derived directly or indirectly from HLLB data. Some example non-limiting examples of operational parameters include span gain loss, input power, output power, gain, and gain tilt.

A system and method consistent with the present disclosure provides for automated line monitoring system (LMS) baselining that enables capturing and updating of operational parameters specific to each repeater and associated undersea elements based on high loss loopback (HLLB) data. The captured operational parameters may then be utilized to satisfy queries targeting specific undersea elements in a Command-Response (CR) fashion. Therefore, command-response functionality may be achieved without the added cost, complexity and lifespan issues related to deploying undersea elements with on-board CR circuitry. As generally referred to herein, operational parameters include any parameter that may be derived directly or indirectly from HLLB data. Some example non-limiting examples of operational parameters include span gain loss, input power, output power, gain, and gain tilt.

A system and method for automatically calibrating loopback data in a line monitoring system of an optical communication system. Extra peaks in loopback data are calibrated out of the loopback data used by the system by identifying pairs of peaks in the loopback data associated with test signal transmissions through the same high loss loopback path from opposite ends of the optical transmission path.