Smart siren lms. ELearning Learning

US7236101B2. Multiple emergency vehicle alert system. Google Patents

Publication number US7236101B2 US7236101B2 US10/808,893 US80889304A US7236101B2 US 7236101 B2 US7236101 B2 US 7236101B2 US 80889304 A US80889304 A US 80889304A US 7236101 B2 US7236101 B2 US 7236101B2 Authority US United States Prior art keywords emergency vehicle transmitter vehicle emergency digital signal Prior art date 2003-03-31 Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.) Expired. Fee Related. expires 2024-05-14 Application number US10/808,893 Other versions US20040189490A1 ( en Inventor Richard T. Halishak Original Assignee Halishak Richard T Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.) 2003-03-31 Filing date 2004-03-25 Publication date 2007-06-26 Priority claimed from US45823903P external-priority 2004-03-25 Application filed by Halishak Richard T filed Critical Halishak Richard T 2004-03-25 Priority to US10/808,893 priority Critical patent/US7236101B2/en 2004-09-30 Publication of US20040189490A1 publication Critical patent/US20040189490A1/en 2007-06-25 Priority to US11/767,684 priority patent/US20070247332A1/en 2007-06-26 Application granted granted Critical 2007-06-26 Publication of US7236101B2 publication Critical patent/US7236101B2/en Status Expired. Fee Related legal-status Critical Current 2024-05-14 Adjusted expiration legal-status Critical

Links

• 230000001702 transmitter Effects 0.000 claims abstract description 70
• 230000000007 visual effect Effects 0.000 claims abstract description 12
• 230000001809 detectable Effects 0.000 claims abstract 6
• 241000269346 Siren Species 0.000 claims description 18
• 230000000737 periodic Effects 0.000 claims 6
• 230000005540 biological transmission Effects 0.000 description 8
• 241000269400 Sirenidae Species 0.000 description 6
• 238000010586 diagram Methods 0.000 description 4
• 238000000926 separation method Methods 0.000 description 4
• 206010019245 Hearing impaired Diseases 0.000 description 2
• 230000003213 activating Effects 0.000 description 2
• 230000004075 alteration Effects 0.000 description 2
• 230000005520 electrodynamics Effects 0.000 description 2
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Images

Classifications

• G — PHYSICS
• G08 — SIGNALLING
• G08G — TRAFFIC CONTROL SYSTEMS
• G08G1/00 — Traffic control systems for road vehicles
• G08G1/09 — Arrangements for giving variable traffic instructions
• G08G1/0962 — Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
• G08G1/0965 — Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages responding to signals from another vehicle, e.g. emergency vehicle

Abstract

A warning system for making known the presence of an emergency vehicle. A transmitter is mounted in an emergency vehicle that outputs a digital UHF/LMS signal that is detectable within a range. A receiver responds to the digital UHF/LMS signal from the transmitter and is mounted, most preferably to a dashboard of a motor vehicle. The receiver detects from the digital signal the type of emergency vehicle from which the digital signal is originating. In one embodiment the digital signal also includes a unique identifier for the transmitter rather than simply a generic discipline identifier such as police, fire, emergency etc. A visual indicator mounted to the motor vehicle is activated in response to the digital signal from the transmitter to warn a motorist in the motor vehicle of a presence of the emergency vehicle within the range of the transmitter and to warn the transmitting vehicle of the presence of other emergency vehicles within receiving range.

Description

The present application claims priority from U.S. provisional application Nos. 60/458,239 filed Mar. 31, 2003 and 60/469,857 filed May 12, 2003 each entitled Multiple Emergency Vehicle Alert System.

The present invention concerns a visual dashboard mounted alert on any vehicle (private, commercial or emergency) that is activated by short range digital radio signal of universal frequency transmitted from one or more emergency vehicles while operating the emergency vehicle’s emergency light bar with an electric siren (electrodynamic loudspeaker).

In a modern day motor vehicle, efforts have been made to soundproof the passenger compartment. One result of such soundproofing is that the driver may be unable to hear approaching emergency vehicle having its audio siren turned on. Alternatively, the playing of a radio or stereo at loud volume may make the driver unable to hear an approaching emergency vehicle with the audio siren on. A hearing impaired driver may be unable to hear an approaching emergency vehicle with its siren on. Two or more emergency vehicles of the same or different disciplines, responding to the same or to different dispatchers, approaching the same intersection may not be able to hear the other approaching emergency vehicle due to the audible sound of his or her own siren and hence is unaware of the presence of another emergency vehicle responding to the same or a different emergency call.

If the emergency vehicle is using a silent approach, motorists in the vicinity will not be aware of the presence of the emergency vehicle approaching an intersection if the siren is not turned on. An emergency vehicle (or other patient transport vehicle) transporting a patient and not using an audio siren poses a risk to other motorists who will be unaware of the approaching emergency vehicle.

The aforementioned problems are addressed by use of UHF/LMS signals for activating a motor vehicle mounted warning receiving device. Police, Fire, EMS or other emergency vehicles that are authorized to cross intersections against a stop signal will be equipped with a Multiple Emergency Vehicle Alert System (MEVAS) transmitting warning device. Such warning device will be sensed by any MEVAS receiving device mounted in any vehicle (private, commercial and emergency) within a specified range such as 1500-2000 feet.

In one embodiment of the invention, a single universal UHF/LMS frequency shall be used by all Governmental Agencies which will be received by all vehicles, within range, including any other emergency vehicles. The signal transmitted will be digital and one code will be the same for each discipline (Fire, Police etc) of emergency vehicle. A second frequency code will be sent by a transmitter and will identify the transmitting vehicle with a unique ID. An omni antenna will be used with eash transmitter.

The RF transmitters will be assigned to only specific government agencies whose vehicles are authorized to cross intersections against stop lights. Specific examples are Police, Fire, Emergency Medical Services and others such as funeral escort services. Two or more radio dispatchers controlling emergency vehicles of different disciplines or radio dispatchers of different government agencies are not involved in these transmissions, thereby eliminating delay or third party human error.

The universal UHF/LMS frequency should be recognized across jurisdictional boundaries. A vehicle (private passenger or commercial) traveling intercity or interstate equipped with a MEVAS receiving unit must be able to receive the UHF/LMS signal of any emergency vehicle transmitting in its vicinity whether in the State of New York or the State of California.

These and other objects advantages and features of the invention will become apparent from a detailed description of an exemplary embodiment of the invention which is described in conjunction with the accompanying drawings.

FIG. 1 schematic diagram of a system constructed in accordance with the invention for use in an emergency vehicle; and

FIG. 2 is a schematic diagram of a receiver only system for use with a private or non-emergency vehicle.

FIGS. 1 and 2 depict representative embodiments of apparatus for use in implementing a warning system in accordance the invention. Each safety or emergency discipline (Police, Fire, EMS and other) is assigned one of four Identifying codes to be transmitted. Each of the vehicles employed by these agencies is equipped with a Multiple Emergency Vehicle Alert System 10 (MEVAS) UHF/LMS having a transmitter 12, which is activated by a switch 14 that is coupled to its emergency light bar. The system 10 for this type of vehicle also includes a MEVAS receiver 20. Each transmitter 10 shall be capable of up to 1 (one) Watt of RF output to control the range of the transmission. Each transmitter/receiver unit includes a microchip switch 30 to transmit during a millisecond transmit interval followed by a 3 to 5 seconds off interval. During the transmitting millisecond, the switch 30 blanks the receiver 20 of the transmitting vehicle during its millisecond digital output. The receiver will be available to receive another other emergency vehicle or vehicles transmissing during the 3 to 5 second period between transmissions. This cycle shall be continuously repeated as long as the emergency vehicle is using its light bar so long as the switch 14 is closed. The 3 to 5 second cycle is chosen to avoid overlap between two emergency vehicles. As an example, if one has a 4 second cycle and a second has a 4.5 second cycle, they will be out of sync immediately after the first cycle. This cycle could also be made to vary randomly each time the light bar is activated.

The transmitter shall be constructed utilizing UHF/LMS tone coded frequencies. The transmitter may be integrated within the emergency light bar electronic system of emergency vehicles. The UHF/LMS signal transmitter shall be installed in only authorized emergency vehicles.

The receiver 20 is a solid state circuit and meets all minimum industrial, FCC and EIA standards. The receiver shall be compact for operation in the UHF/LMS Band frequency and operate in vehicles with a 12 volt electrical system. The receiver includes a microprocessor 22 to read the identifier in the signal and activate the proper warning light. The receiver is a synthesized type model which allows field program changes of UHF/LMS frequencies and CTCSS tones. The system 10 shall incorporate state of the art integrated circuit technology and printed circuit board interconnections.

A stand alone Multiple Emergency Vehicle Alert System (MEVAS) receiver 40 ( FIG. 2 ) unit shall be used in vehicles (private passenger or commercial) that are not equipped with MEVAS units installed during manufacture. In factory equipped vehicles, the MEVAS receiver 40 may be integrated in the AM/FM radio receivers. This receiver 40 is similar to the receiver 20 depicted in FIG. 1. So long as it is powered by the vehicle electrical system coupled through the vehicle ignition, the receiver 40 is listening for transmitter signals and in the exemplary embodiment this is the case so long as the ignition switch is in the run position.

The MEVAS receiver is activated whenever the motor vehicle ignition switch is on (the engine need not be running) and is thereby capable of receiving the UHF/LMS signal from a MEVAS transmitter. Each receiver 20 in an emergency vehicle is muted to its own transmitted signal for the duration of its own transmission and shall then immediately be capable of receiving any other transmitter’s signal.

The receivers 20, 40 shall have the capability to read and translate the discipline I.D. code and shall make that identification on a dashboard digital display or dash board display lights 50. The display lights are treated to glow upon receipt of a signal and gradually fade to be capable of receiving the next impulse from the original transmitter or any other transmitter. The dash-board display lights in all MEVAS receivers shall have a single color light for each emergency discipline to allow the driver to identify the type of emergency approaching. Thus, for example the light 50 a is blue and corresponds to a police vehicle and the light 50 b is red and corresponds to a fire department vehicle.

When an emergency vehicle activates its emergency light system and/or its sirens, the MEVAS transmitter 12 activates a short range pulsating digital universal UHF/LMS radio signal which shall be received by other vehicles, including other emergency vehicles, within a 1500 to 2000 foot distance. The use of one universal UHF/LMS frequency for each discipline of emergency vehicles shall provide the capability of extending beyond and shall afford a dependable alarm system regardless of present location and origin of that vehicle. The MEVAS UHF/LMS signal shall ensure that all receivers shall be capable of providing an effective alarm system even in an unfamiliar territory. The radio signal shall pulsate to serve multiple purposes. One is to attract the attention of the driver of other vehicles by its pulsating or flashing light. The second purpose is that no two Emergency Vehicles will pulse exactly concurrently and the driver of one Emergency Vehicle, with light bar/sirens on, will know that a second Emergency Vehicle, of any discipline, is approaching and within the 1500-2000 foot range with their sirens on also. Non-Emergency vehicle MEVAS receivers 40 shall identify the discipline of the transmitting emergency vehicle.

For normal maintenance, a manual switch 32 shall be provided in the transmitter to be used to verify its proper operation. This manual switch may also be used during a “silent approach” that allows the transmitter to operate even though the siren/light bar is not activated (switch 14 is open) due the transmitter 12 receiving 12 Volt power through the vehicle ignition switch

Tables 1 and 2 below list representative specifications for the transmitters and receivers shown in the drawings.

While the present invention has been described with a degree of particularity, it is the intent that the invention include alterations and modifications from the disclosed design falling within the spirit or scope of the appended claims.

Claims ( 2 )

mounting a transmitter and a receiver to an emergency vehicle that outputs a digital signal at periodic intervals that is detectable within a range;

said transmitter turning off its own signal at periodic intervals for 3-5 seconds to allow receipt by said receiver of a signal from other emergency vehicles in the vicinity;

mounting an additional receiver in a private or commercial motor vehicle that responds to the digital signal from the transmitter of a transmitting emergency vehicle to detect said digital signal;

transmitting a digital signal from the transmitter when an emergency vehicle light bar but not a siren of said emergency vehicle is actuated; and

displaying a visual warning from a visual indicator mounted to the private or commercial motor vehicle or other emergency vehicle in response by a receiver of receipt of the digital signal from an emergency vehicle transmitter to warn a motorist in the private or commercial vehicle and/or an other emergency vehicle of a presence of the emergency vehicle whose light bar has been actuated is within said range.

a transmitter in a first emergency vehicle that outputs a digital signal at periodic intervals that is detectable within a range and is periodically turned off from three to five seconds, said transmitter including means responsive to actuation of an emergency vehicle light bar for outputting said digital signal;

a receiver mounted to a private or commercial motor vehicle that responds to the digital signal from the transmitter in an emergency vehicle to detect said digital signal;

visual indicator mounted to the private or commercial motor vehicle that is activated in response to the digital signal from the transmitter of an emergency vehicle to warn a motorist in said motor vehicle of a presence of the emergency vehicle within said range; and

a receiver and visual indicator mounted within the first emergency vehicle that responds to other transmitters in other emergency vehicles during the period the transmitted signal of the first emergency vehicle is turned off regardless of the government agency to which the emergency vehicle is assigned.

US10/808,893 2003-03-31 2004-03-25 Multiple emergency vehicle alert system Expired. Fee Related US7236101B2 ( en )

Applications Claiming Priority (3)

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Cited By (3)

Patent Citations (13)

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• General information about your company, along with a description of who you are
• An executive project summary where you briefly explain what you need
• The learning goals you want or need to achieve
• Details about your learners
• The type of content and delivery method desired
• Reasons for selecting a CRM
• Your list of must-have requirements (more on this in a minute)
• Your desired integration capabilities
• How you intend to use the system, including the number of users and deployment method
• Details about the data you need to migrate (if you have an existing system
• The level of support and training your company requires
• A breakdown of your budget
• The criteria you’ll use to make selections (non-negotiable, because vendors need to know the rules of the game)
• Your criteria for assigning user roles and permissions among different job functions
• Third-party apps and external services you want to use with the platform
• Crucial training KPIs you wish to measure using the software
• Performance expectations and targets for the vendor
• A section for vendors to talk about their experience and expertise
• Submission information for the proposals
• Contact information of stakeholders in the RFP committee like unit leaders, supervisors and IT staf
• Your expected timeline for evaluation and implementation
• The proposal deadline (give vendors at least two to four weeks to put together a proposal)

You may need to include other details depending on your situation, so be sure to tailor everything based on your unique needs.

After you’ve provided all the information relevant to what you expect from the LMS, it’s time to get acquainted with the vendors. Include a section in your RFP asking vendors for key information, like industry experience, customer base, technical services and compliance policies, to help you evaluate your options in detail.

Some things you can request include:

• A summary describing key points of the proposal
• Business history and financials
• Key differentiators
• Contact information
• Number of current customers with their industries’
• Awards and recognitions
• Data security policy
• Documentation practices
• Implementation and onboarding support

The process is more involved than jotting some notes on Evernote or a whiteboard and then throwing them into a document. It involves identifying your requirements, putting together the actual document, sending the RFP, evaluating responses and following up with vendors. We’ll cover each of those steps below.

However, the RFP isn’t the only tool at your disposal. If you picture your selection process like a chain, connecting where you are currently with where you want to be after implementation, the RFP is the middle link.

The purpose of the RFP isn’t to gather general info. It’s to target a small, well-vetted group of vendors. In order to get there, you have to start by doing a bit of leg work.

Benefits of an LMS RFP

Remote and hybrid work has become very popular in the past few years. As a result, there is a growing demand for LMS among small and big organizations alike. Besides, as more LD leaders are focusing on upskilling and reskilling their workforce to bridge the skills gap, LMSs provide an all-in-one platform to help employees hone career-relevant competencies in a continuous learning environment.

A comprehensive and needs-oriented RFP paves the way to finding the right LMS for your organization. It gives vendors a clear understanding of how you want the software to serve your unique requirements, be it for developing new skills or growing existing ones.

Here are some of the potential benefits of an LMS RFP:

• Provides a formal process for conducting all software selection-related activities.
• Facilitates prompt exchange of time-sensitive information like submission deadlines, RFP start and closing dates, and internal communications on the micro level.
• Provides a centralized information-sharing platform to all stakeholders from both sides.
• Enables you to conduct a side-by-side assessment and comparison of multiple service providers.
• Offers flexibility to ask questions and discuss proposals with vendors before selection so you can make an informed decision.

Research the Market Before Sending

You’ve identified your need to invest in a new LMS or to upgrade from your current solution. Before jumping into the RFP process, you need to do a recon of LMS software vendors.

When it comes to sending RFPs, the best practice is to aim narrow. That means you have to first figure out which vendors to target. There are a few ways to go about this.

You can conduct your own initial research, which is never a bad idea. This can take many forms, including reading through blog posts and industry forums. Posts that compare two products, such as Litmos vs Docebo and Canvas vs Blackboard, are especially helpful. As you begin building a picture of the landscape, start a list of vendors to potentially contact.

Our LMS comparison report is a quick, easy way to view key information on all the top vendors. It lets you get up to speed without spending hours wading through search terms and vendor websites.

Besides digging up your own intel, you can always go directly to vendors to find the information you need (helpful if you’ve done some preliminary searching and have a list in mind). That’s where the request for information is handy.

The Request for Information

Companies often opt to send an RFI to kick off their software search in earnest. Every situation is different, so you’ll want to weigh whether this approach works for you, but it’s a good option to consider.

If you’re not very familiar with the LMS sector, sending an LMS RFI is an excellent way to gain the information you need from a number of vendors. Even if you’ve had a learning management solution for years, it still might be wise to send an RFI to vendors to get a pulse on the current industry. Things change quickly in the software world, and information from even a year ago can be outdated.

Imagine this stage like looking for the perfect wedding venue. You have an idea of your goals and pain points, but the purpose here is to investigate as many options as you think might work. From there, you can whittle the list down until you have a handful of vendors left to target with an RFP.

Since you’re not doing a deep dive, avoid going overboard with information. We recommend including at least the following details in your LMS RFI:

• Some background info about your company
• What requirements you need to have
• What challenges you need the LMS to address
• A deadline for sending a response

Don’t skimp on the research phase. The success of your RFP will largely depend on taking the time to first build a list of vendors that may be a good fit. If you do a sloppy job of researching, you could well end up with a shortlist of vendors that can’t meet your requirements.

Creating an RFP in 4 Steps

With your initial research pass complete and a handful of vendors chosen, it’s time to create your RFP. It may be tempting to Google “LMS RFP template,” find a decent one to download and be on your merry way. But that’s putting the train before the engine.

Selecting software is hard enough without setting your process up for failure along the way. We want to help you avoid making those costly mistakes. To get the best results, we recommend following this four-step process for putting together your LMS request for proposal.

Step 1: Determine Requirements

One of the essential sections in your RFP is your list of LMS requirements, or must-have features. That makes this step critical in launching you on a trajectory toward success.

Here are a few keys to follow that will help you FOCUS on the requirements that will make a difference in your end result:

• Give everyone involved a voice. Don’t stop your requirements gathering at the C-Suite and HR managers. Ask IT how the LMS will impact your technical architecture and processes. Also get input from the end-users — their insights will be critical.
• Focus on the essential features. It’s easy to assume every feature is important. But that doesn’t mean every feature is paramount. Having all the flashy tools won’t help if you have a global workforce but can’t offer learning modules in multiple languages.
• Explain scenarios for features. It’ll be helpful for vendors to know which purposes you want each feature to serve. Go into the details about specific requirements to attract vendors that are relevant to your use case.
• List out your challenges. Is the current system too narrow in scope? Do you need a mobile option? Is your company lacking any sort of formal training process and looking to put a solution in place? Knowing your challenges will direct you toward your essential features.
• Look to the future. Implementing new software takes time and is expensive. Save your company the trouble of doing it multiple times by planning ahead for what you’ll need down the road. If part of your roadmap is to sell your training materials, for example, you’ll want an LMS that includes eCommerce capabilities or has the option to add that feature when you need it.

Select the priority of your requirements on SelectHub’s easy-to-use platform.

As you look at the LMS market, you’ll find a lot of vendors offer solutions that solve many of the same problems. That’s why it’s important to get specific based on your challenges, needs and goals. Only then will you be in a position to find out how vendors can solve your particular pain points.

Keep in mind that your requirements list can extend beyond specific software capabilities.

• Will your team need training? If so, you need to look at vendors who include training.
• Does the vendor offer support? Your IT department will want to know what technical expertise the vendor will or won’t bring to the table.

It’s also helpful to think of your requirements as more than a laundry list of items that require a checkmark. Sure, that approach tells you if a certain feature is included. But you’ll get a lot farther if you dig into the how.

Provide specific situations, such as developing a learning path for users, and give vendors space to explain how their product accomplishes that task. That will provide you with more insight than a simple checklist, and the more detail you get, the better.

Here are some feature-specific questions you can ask the vendors:

Course and Content Management

• Do you offer built-in authoring tools?
• Can I upload courses to the platform from third-party sources?
• How can I track course performance?
• How can I customize courses?
• Do you provide an online content repository?
• Which types of reporting formats does the software support?
• How can I share reports with other users?
• Can I generate employee performance reports?
• Can I automate report delivery based on a fixed schedule?
• Is it possible to generate visual reports?

• Which collaborative tools do you offer?
• Does the software have an built-in video conferencing tool?
• Does it work with popular video conferencing applications? (Zoom, Google Meet, Teams)
• Can I schedule real-time notifications?
• Does the system offer private and public messaging channels?

• Which types of assessment formats does the software support?
• Can I add tests and assignments to courses?
• Does the system come with game-based elements?
• Can I add feedback forms at the end of assessments?
• Can I automatically share progress reports with learners?

Step 2: Create Your RFP

No brainer, right? After all, it can’t be that hard to put together an RFP. While that’s true to some extent, you still need to give this step your full attention. Otherwise, you risk sending out an unprofessional, confusing RFP.

Your goal should be to make it clear how you want vendors to respond to your request. If vendors can’t figure out what you want or how best to respond to your requirements, chances are you’ll end up sorting through messy proposals that don’t explain the specific ways a vendor can meet your needs.

Best practices for an LMS RFP include giving vendors a structure to follow that doesn’t leave room for questions on what information you want and how it should be formatted.

But before you draw up your format rules, think about how they’ll affect vendor responses. Example: if you give them a spreadsheet, it’s best to frame everything to accommodate Yes/No responses. Expecting lengthy explanations with that layout isn’t reasonable.

Using an online platform is a Smart way to manage this step. It will help you avoid errors and provide you with a pre-built framework for sending clean, organized, professional RFPs.

That’s why we built the SelectHub platform. The workflow simplifies the process of selecting your requirements, and you can create and send professional RFPs using best practices. It also drastically speeds up the process, cutting the time it takes for a start-to-finish RFP by as much as 50%.

Step 3: Identify Vendors and Send Your RFP

If you did your research ahead of time, this step will be straightforward. If not, now’s the time to develop a vendor shortlist.

Creating and sending RFPs is no small task — it takes time and commitment. The research phase is where you go broad. By this point, your FOCUS should be specific and narrow.

Your shortlist should be just that — short. Only include the vendors who look promising. Select no more than five candidates to send your RFP to. And if only two vendors look like they’re worth pursuing, don’t send out extra RFPs simply because you can.

Step 4: Evaluate Responses

You did it! Your LMS RFP is out in the world. But that doesn’t mean your job is done.

You still need to vet the responses. That process in and of itself can take a long time. It’s where your evaluation team closely examines each vendor proposal to determine whether the product is what you’ll need. Like choosing the perfect wedding venue, a lot rides on this decision.

You have two options when it comes to the evaluation phase. Either wait until you’ve received all responses — which could take several weeks — or start working through each proposal as it comes in.

View potential vendors and invite the feedback of your team with SelectHub’s collaborative platform.

The latter is a better choice. It will speed up the process and make the potential back-and-forth with vendors go more smoothly.

This is where you’ll want to give vendors the chance to ask questions and get any clarifications you need based on that conversation. Working through those questions as they arise will streamline the entire process and prevent it from bogging down.

A management platform such as our RequirementsHub will make this step a lot easier to manage. It provides a central place to track responses and follow up with vendors, rather than fighting through a crowded email inbox to find what you’re looking for.

The Final Stage: Post-Evaluation and Selection

After completing your evaluation, you may have a clear-cut winner. likely, however, you still need to do some extra investigation. Here are several good ways to learn more about each solution:

• Attend a demo: A live demonstration is an excellent way for vendors to show off what the software can do. And it gives you a chance to look at the interface as well as see the navigation.
• Check references: It’s never a good idea to base your selection decision solely on what a vendor says. References provide firsthand information that you can’t always get by asking questions and listing requirements.
• Ask for sandbox access: A sandbox is an environment meant to simulate how your fully implemented system will look and function — aka a real-world test drive. It won’t have every feature available, but it’s a chance for admins and users to play around inside the platform, looking for areas of concern and getting familiar with the user experience.
• View a proof of concept presentation: This is similar to a demo but typically limited to showing how the system can address a specific task rather than a range of functions. However, some vendors may offer it as a step up from a sandbox, so keep that in mind.
• Free trial: This won’t have the extent of features as a sandbox, but it will give you an idea of how the LMS will fit into your operations.

Not every vendor will offer every opportunity mentioned above, and some of them may not be free. So be sure to check and use the options that make the most sense for your company.

Review your RFP with vendors, request demos and discuss pricing.

The final research card in your deck is the request for quote (RFQ).

The Request for Quote

Companies send an RFQ at the end of the process. You’ve seen the vendor proposal, viewed a demo and checked references. But what will the impact to your bottom line be?

You may have put a budget line in your RFP, but now that you and your vendors have set the expectations, an RFQ moves the discussion from generalities to exact figures.

Sending an RFQ will shed light on the final piece of the puzzle and may help you choose the product that checks both your requirements and affordability boxes.

The RFQ, unlike its name suggests, is more than you saying, “What’s the final bill?” You can use this step to find out other final details like if or how the vendor has implementation services and what support they offer, based on the information in the RPFs.

Special Considerations

There are a couple important factors to keep in mind in addition to what we’ve covered. Let’s quickly look at what they are.

RFPs Aren’t Always Required

RFPs are great. After all, we wrote a post that will help you nail your LMS RFP. But that doesn’t mean you’ll always need one.

If your business is smaller and doesn’t require a ton of functionality from an LMS, or your price range is on the low end, going through the arduous RFP process might not be worth it. This may also be true if you’re purchasing your first LMS, in which case your needs — and therefore the selection process — might not be as complex.

Be Wary of Templates

Templates can be helpful and cut down on the amount of work you need to do. But don’t fall prey to the siren call of a pre-made option. It’s too easy to download a template and use the entire thing — because if it’s in the template, it must be necessary!

In reality, you’re much better off using a template as a starting point (if you use one at all). No template can capture your exact needs because every company is unique.

So while templates, like ours for requirements, are helpful, always use them with this caveat: customize them to reflect your special situation. Our template lets you add and remove features as necessary, so it’s a good choice if you don’t want to start from scratch while having full control over the requirements you choose.

Next Steps

The process of picking a new LMS demands effort, but it’s well worth the time and money you’ll spend. Following this LMS RFP template and guide will help your organization end up with a solution that users love and that fits your needs as perfectly as the Iron Man suit fits Tony Stark.

As you compare vendors, gather requirements and manage the RFP process, keep this thought at the forefront of what you do: “how can this system meet our exact, unique needs?” Answer that, and you’ll be on your way to finding a solution that will take your learning management to the next level.

Are you facing any hurdles in the process of managing your LMS RFP? Share your challenges with us in the Комментарии и мнения владельцев — we’d love to help out!

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Zero-Day Exploit Detection Using Machine Learning

Code injection is an attack technique widely used by threat actors to launch arbitrary code execution on victim machines through vulnerable applications. In 2021, the Open Web Application Security Project (OWASP) ranked it as third in the top 10 web application security risks.

Given the popularity of code injection in exploits, signatures with pattern matches are commonly used to identify the anomalies in network traffic (mostly URI path, header string, etc.). However, injections can happen in numerous forms, and a simple injection can easily evade a signature-based solution by adding extraneous strings. Therefore, signature-based solutions will often fail on the variants of the proof of concept (PoC) of Common Vulnerabilities and Exposures (CVEs). In this blog, we explore how deep learning models can help provide more flexible coverage that is more robust to attempts by attackers to avoid traditional signatures.

Palo Alto Networks Next-Generation Firewall customers receive protections from such types of attacks through Cloud-Delivered Security Services including Intrusion Prevention capabilities in Advanced Threat Prevention, as well as through WildFire.

Why Intrusion Prevention System Signatures Aren’t Sufficient – How Machine Learning Can Help

Intrusion Prevention System (IPS) signatures have long been proven to be an efficient solution for cyberattacks. Depending on predefined signatures, IPS can accurately detect known threats with few or no false positives. However, creating IPS rules involves proof of concept or technical analysis of certain vulnerabilities, so it is challenging for IPS signatures to detect unknown attacks due to a lack of knowledge. For example, remote code execution exploits are often crafted with vulnerable URI/parameters and malicious payloads, and both parts should be identified to ensure threat detection. On the other hand, in zero-day attacks, both parts can be either unknown or obfuscated, making it difficult to have the needed IPS signature coverage. In our experience, we found the following set of challenges faced by threat researchers:

• False negatives. Variations and zero-day attacks are seen every day, and IPS cannot have full coverage for all of them due to a lack of attack details beforehand.
• False positives. To address variants and zero-day attacks, generic rules with loose conditions are created, which inevitably brings the risk of false alarm.
• Latency. The time lag between vulnerability disclosure, security vendors rolling out protections and customers applying security patches represents a significant window for attackers to exploit the end user.

While these problems are innate to the nature of IPS signatures, machine learning techniques can address these shortcomings. Based on real-world zero days and benign traffic, we trained machine learning models to address common attacks such as remote code execution and SQL injection. From our recent research, presented in this blog, we find that these models can be very helpful in zero-day exploit detection, being both more robust and quicker to respond than traditional IPS methods.

In the following sections, we’ll share some case studies and insights into how machine learning models can be incorporated into exploit detection modules, and how effective this can be.

Detection Case Studies on Zero-Day Exploits

Case Study 1: Command Injection Detection

Command injection has long been a major threat in network security. Due to their easy-to-exploit nature and severe impact, command injection vulnerabilities have the potential to bring tremendous damage to affected organizations, especially when patches come late. Last year, vulnerabilities in commonly used software such as Log4Shell and SpringShell placed hundreds of millions of Java-based servers and web applications at risk. Meanwhile, vendors were busy updating IPS signatures to cover constantly evolving attack patterns derived from the original exploit in a frustrating cat-and-mouse chase, and we still see obfuscated attacks attempted today.

Generally, for those vulnerabilities which include specific paths or parameters, IPS signatures are a good idea since attacks can be accurately filtered out by the URI and suspicious payload. However, some exploits of critical vulnerabilities can be flexible due to the nature of HTTP protocols. For example, the Log4Shell vulnerability can be triggered through all kinds of user inputs. over, the complexity of HTTP encoding methods allows attackers to evade normal detection using partial or mixed encoding. In such situations, machine learning methods can more accurately identify abnormal traffic, yielding corresponding verdicts with the knowledge of previously seen malicious sample payloads.

We trained a state-of-the-art Convolutional Neural Network (CNN) with cutting edge deep learning technologies loosely based on previous academic research on Temporal Convolutional Networks. While variable length inputs suggest that a recurrent model structure such as a Recurrent Neural Network (RNN) or a Long Short-Term Memory (LSTM) Network may be suitable, research shows that a simple convolutional architecture often outperforms recurrent models. Our model has learned more generalizable common patterns in command injection exploits while also being specific enough to avoid false positives. In the following sections, we discuss case studies of command injection exploits and how our new machine learning model is able to accurately detect them.

Atlassian Confluence vulnerability (CVE-2022-26134)

Atlassian Confluence is a web-based corporate wiki tool used to help teams to collaborate and share knowledge efficiently. One recent remote code execution vulnerability, CVE-2022-26134, targets Confluence versions 1.3.0-7.4.17, 7.13.0-7.13.7, 7.14.0-7.14.3, 7.15.0-7.15.2, 7.16.0-7.16.4, 7.17.0-7.17.4 and 7.18.0-7.18.1. We have observed successful exploitation leveraging this vulnerability to perform Cerber Ransomware attacks.

Malicious but arbitrary commands can be inserted in the payload to perform various activities. The machine learning model can easily distinguish between benign and malicious activities and block the attacks using different commands without knowing the full context of the application.

Unknown IoT Zero-Day Attack

Sometimes we see alerts from our internal threat hunting research platform when processing real-world traffic. After filtering out false positives, these types of detections usually indicate that a zero-day attack has been captured. For example, on April 29, 2022, we saw the HTTP request shown in Figure 2.

The command and control (C2) server was down shortly after we got the traffic, so it is difficult to verify details of the exploit and payload. However, according to our threat intelligence, this could be attributed to a previously unknown attack targeting certain MIPS-based Smart devices.

With traditional IPS technologies, it’s possible to miss such attacks since the vulnerable URI and parameters have never been seen before; it’s hard to determine if the requested data is benign or suspicious. In this specific case, our IPS with a default configuration did not result in an alert, but our machine learning model successfully identified the attack with a high confidence score.

Tenda AC18 Router Vulnerability (CVE-2022-31446)

The Tenda AC18 router is prone to a remote code execution vulnerability, allowing attackers to execute arbitrary commands on the device. Not long after the vulnerability was published, a Palo Alto Networks researcher discovered an exploit in the wild targeting this specific CVE, as shown in Figure 3.

Similar to the zero-day IoT attack mentioned above, it’s difficult for traditional IPS solutions to detect such attacks due to their inherent limitations. However, our machine learning model detected the exploit with high confidence. The machine learning model identifies that requests in the POST body are highly suspicious and suggests the IP address shown in Figure 3 should be further investigated with correlated malicious samples.

Case study 2: SQL Injection Detection

SQL injections are another notorious and challenging threat in network security. In this type of attack, threat actors alter SQL queries and inject malicious code by exploiting vulnerabilities. SQL injections may result in information modification, sensitive data leakage and unauthorized command executions in underlying database systems. Due to the serious potential impact of SQL injection vulnerabilities, their prompt detection and zero-day exploit prevention on the network side are critical to fortifying an organization’s assets.

Unfortunately, the task is challenging with traditional IPS systems due to time limitations and the need for technical expertise. Traditional systems require properly composing and testing customized signatures to cover zero-day SQL exploitations, such as exploits targeting, for example, CVE-2022-0332 and CVE-2022-34265. Even worse, attackers may utilize readily available hacking tools such as sqlmap to generate SQL injection exploitations that are very difficult to cover with IPS signatures. In this case, machine learning solutions can effectively classify malicious SQL injection payloads from benign traffic by examining carefully selected features covering a variety of SQL injection exploitations. The following vulnerability case studies demonstrate the effectiveness and efficiency of the machine learning solutions we have developed.

Moodle vulnerability (CVE-2022-0332)

Moodle is a free and open source learning management system with more than 300 million users. However, Moodle versions 3.11 to 3.11.4 have a vulnerability (CVE-2022-0332) in the server.php file due to the lack of user input sanitization, making it possible to use the union operator to query unexpected data. When given the following payload, vulnerable versions of Moodle will query the SQLite engine version with the function sqlite_version and return it to the user. Our machine learning solution effectively derives features from capturing the union-select related SQL injection code snippet and flexibly detects exploitations of CVE-2022-0332.

After decoding, the PoC of CVE-2022-0332 is shown in Figure 5.

Django vulnerability (CVE-2022-34265）

Django is a widely used framework to build websites, including Instagram, Disqus. etc. CVE-2022-34265 is an issue affecting the Django framework. This vulnerability is caused by an improper check on parameter values for the Trunc and Extract functions, which may lead to unexpected SQL statement execution. Two PoCs for CVE-2022-34265 are shown in Figures 6 and 7. Both payloads use a boolean injection sub-payload followed by a stack injection sub-payload. When a payload is appended to the predefined SQL statement, the first statement split by the semicolon will always be true because of the or 1=1. The second part will lead to a sleep of five seconds by the program, which, on the browser side, leads to a five second waiting time. The five second delay on the front end can indicate the successful SQL statement execution – which also indicates the existence of the SQL injection vulnerability. Our machine learning solution can also effectively detect the SQL injection patterns as or 1=1 statements, which can help us effectively prevent the exploitation of such vulnerabilities.

sqlmap-generated exploitation

sqlmap is an open source tool used in penetration testing to detect and exploit SQL injection flaws, which can automate the process of crafting exploitations of SQL injection vulnerabilities. While the tool can be used for legitimate purposes, it can also be abused by attackers.

Figure 8 shows a PoC of SQL injection from sqlmap. After decoding, we can observe the snippet and 1043=1043. which is a widely used pattern for blind SQL exploitation. The attacker can leverage the statement to sniff the vulnerabilities of web services and database systems. The pattern is similar to or 1=1 (see our discussion of CVE-2022-34265), but sqlmap can generate polymorphic SQL injection exploitations as long as the statement is always true after and.

These types of patterns are challenging to detect via IPS signatures. While a traditional signature might only be able to match one and 1=1 case, our machine learning solution can properly cover the exploitation with dedicated features for all similar and 1=1 cases.

Machine Learning Test Results

For detecting zero day exploits, we trained two machine learning models: one for detecting SQL injection attacks, and one for detecting command injection attacks. We prioritize a low false positive rate in order to minimize adverse effects of deploying these models for detection. For both models, we train on HTTP GET and POST requests. To generate these datasets, we combined multiple sources, including tool-generated malicious traffic, live traffic, internal IPS data sets and more.

From ~1.15 million benign and ~1.5 million malicious samples containing SQL queries, our SQL model achieved a 0.02% false positive rate and a 90% true positive rate.

From ~1 million benign and ~2.2 million malicious samples containing web searches and possible command injections, our command injection model achieves a 0.011% false positive rate and a 92% true positive rate.

These detections are particularly useful because they can provide protections against new zero-day attacks, while being resistant to small modifications that might evade traditional IPS signatures.

Conclusion

Command injection and SQL injection attacks continue to be some of the most common and most concerning threats affecting web applications. While traditional signature-based solutions remain effective against out-of-the-box exploits, they often fail to detect variants; a motivated adversary can make minimum modifications and evade such solutions.

To combat these ever-evolving threats, we developed a context-based deep learning model that proved to be effective in detecting the latest high profile attacks. Our models were able to successfully detect zero-day exploits such as the Atlassian Confluence vulnerability, the Moodle vulnerability and the Django vulnerability. These types of flexible detections will prove to be critical in providing comprehensive defense in an ever-evolving malware landscape.

To protect our customers, the Palo Alto Networks Next-Generation Firewall uses a combined inline and Cloud solution. Our traditional IPS solutions remain effective for protecting against a significant portion of existing exploits, including SQL injections and command injections. In addition, the machine learning models we explored in this blog have the potential to provide even more robust protections beyond IPS signatures.

Additional Resources

Case study 1 updated Nov. 8, 2022, to remove some outdated results information.

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