Threat Modeling Tool For Mac
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Transportation network models are central to transportation operations and planning. There are two main components: network vehicle/passenger flow and travel behavior. The flow model describes flow propagation through the network arcs/nodes. The behavioral models encapsulate travelers’ time-of-day choices on routes, departure time, parking locations and traffic modes (such as solo-driving, carpool, transit, ride-sharing etc.). The idea is to infer/learn dynamic origin-destination (O-D) demands and behavioral choice models that altogether, if input into network simulations, would produce the spatio-temporal flows in the network consistent with the real-world multi-source data.
Data-driven large-scale network simulation and optimization
ALOHA® is the hazard modeling program for the CAMEO® software suite, which is used widely to plan for and respond to chemical emergencies. ALOHA allows you to enter details about a real or potential chemical release, and then it will generate threat zone estimates for various types of hazards. Back DirectX End-User Runtime Web Installer Next DirectX End-User Runtime Web Installer Microsoft Threat Modeling Tool 2016 is a tool that helps in finding threats in the design phase of software projects. The Microsoft Threat Modeling Tool 2016 will be end-of-life on October 1st 2019. The tool can work with any data source and visualization. The tool enables you for agile development and flexible product design. It is a business intelligence platform and will serve as a service. Website: GoodData #19) Pentaho: This tool is for data integration, data mining, and information dashboards. It also provides OLAP services.
Team: Sean Qian (PI, CMU), Xidong Pi (CMU), Wei Ma (CMU)
Funding source: Traffic 21, Mobility 21 National University Transportation Center
Start/End time: 2017-2018
A CSX intermodal rail terminal is planned to open in late 2017 on a parcel of land located immediately north of the McKees Rocks Bridge in the Borough of McKees Rocks and Stowe Township, PA. The development will consist of an intermodal facility that will accommodate approximately 50,000 lifts per year opening year (2018) and 136,000 lifts per year at full buildout (2023). Access to the terminal is proposed via an improved Michael Alley to Island Avenue (SR 0051). It is expected to generate a significant number of trucks in the Borough of McKees Rocks, which adds additional burdens on the existing roadway in the Borough. The terminal may bring in heavy congestion to individual roadway drivers. A traffic impact study was conducted indicating a minor congestion increase with the new infrastructure. This research project conducts an in-depth analysis of the potential traffic impact in high temporal and spatial resolutions. Using the data collected in the traffic impact study along with other relevant data sets possessed by CMU Mobility Data Analytics Center, we simulate individual cars and trucks, and model their route choices, travel time and mixed traffic flow conditions. The result includes the travel time, travel delay, vehicle-mile-traveled and emissions for each road segment and intersection by time of day. We will also examine the effectiveness of potential traffic management strategies, specifically West Carson Street Extension and truck routing.
Publication
- Sean Qian, Jia Li, Xiaopeng Li, Michael Zhang, Haizhong Wang (2017), “Modeling heterogeneous traffic flow: a pragmatic approach.” Transportation Research Part B, Vol.99, pp. 183-204. [URL]
Team: Sean Qian (PI, CMU), Shuguan Yang (CMU)
Funding source: U.S. Department of Transportation Federal Highway Administration
Start/End Time: 2016-2018
Standardize procedures of the usage of data in travel time reliability analyses
Team: Sean Qian (PI, CMU), Alex Jacquillat (Co-PI, CMU)
Funding source: Pennsylvania Infrastructure Technology Alliance
Start/End Time: 2017-2018
As of 2013, there were over 256 million private vehicles owned and operated in the United States. Estimates of the average cost to own, maintain, insure, and park a private vehicle ranges from $460 to $913 per month, an enormous economic and environmental burden. Those vehicles altogether generate on average 37 hours’ congestion per vehicle in the year of 2013. Sharing services, such as ridesharing and on-demand taxi systems (e.g., Uber and Lyft) offer the potential to meet travel needs that are substitutional to self-driving, which leads to significant savings of energy use and flow reduction. In turn, ridesharing can mitigate the costs, congestion and environmental impact of automobile transportation. More importantly, as they grow to represent a significant fraction of network flows, ridesharing systems can influence traffic flows in urban areas in order to improve infrastructure performance. In spite of great potential, sharing services are not provided optimally. Travelers (i.e., service demand) oftentimes have difficulty finding vehicles, or do so at high costs (e.g., Uber’s surge price). On the other hand, taxi/uber/lyft drivers (i.e., service supply) also face challenges to remain profitable, and can lose significant revenue opportunities through incomplete knowledge of traveler demand. This PITA research will address the mismatching among sharing service providers and consumers by proactively predicting traveler demand and sharing demand information to service providers, resulting in better service for travelers, better revenue opportunities for drivers, and better transportation infrastructure performance. In particular, partnering with Gridwise, a Pittsburgh-based startup company, the research team aim to develop predictive models from numerous input datasets that are likely to correlate with rideshare demand. The models will be trained and validated using data obtained from CMU Mobility Data Analytics Center (MAC) and the Gridwise platform, as well as other simulated “proxies” of this demand such as rideshare surge prices.
Team: Sean Qian (PI, CMU)
Funding source: U.S. Department of Transportation through National University Transportation Center (T-SET: Technologies for Safe and Efficient Transportation)
Start/End Time: 2017-2018
Team: Sean Qian (PI, CMU), Michael Zhang (Co-PI, UCDavis), Ram Rajagopal (CO-PI, Stanford), Shuguan Yang (CMU)
Funding source: Cyber-physical systems program, NSF
Start/End Time: 2015-2018
Parking can take up a significant amount of the trip costs (time and money) in urban travel. As such, it can considerably influence travelers’ choices of modes, locations, and time of travel. The advent of smart sensors, wireless communication, social media and big data analytics offers a unique opportunity to tap parking’s influence on travel to make the transportation system more efficient, cleaner, and more resilient. A cyber physical social system for parking is proposed to realize parking’s potential in achieving the above goals. This CPS consists of smart parking sensors, a parking and traffic data repository, parking management systems, and dynamic traffic flow control. If successful, the results of our investigation will create a new paradigm for managing parking to reduce traffic congestion, emissions and fuel consumption and to enhance system resilience. These results will be disseminated broadly through publications, workshops and seminars.
Our research probes massive individualized and infrastructure based traffic and parking data to gain a deeper understanding of travel and parking behavior, and develops a novel reservoir-based network flow model that lays the foundation for modeling the complex interactions between parking and traffic flow in large-scale transportation networks. The theories will be investigated at different levels of granularity to reveal how parking information and pricing mechanisms affect network flow in a competitive market of private and public parking. In addition, this research proposes closed-loop control mechanisms to enhance mobility and sustainability of urban networks. Prices, access and information of publicly owned on-street and off-street parking are dynamically controlled to: a) change day-to-day behavior of all commuters through day-to-day travel experience and/or online information systems; b) change travel behavior of a fraction of adaptive travelers on the fly who are aware of time-of-day parking information and comply to the recommendations; and c) influence the market prices of privately owned parking areas through a competitive parking market.
Publication
- Zhen (Sean) Qianand Ram Rajagopal (2015), “Optimal dynamic pricing for morning commute parking”, Transpormetrica A: Transport Science, Vol. 4(11), pp 291-316. [URL]
- Zhen (Sean) Qian, Ram Rajagopal (2014), “Optimal dynamic parking pricing for morning commute considering expected cruising time”, Transportation Research Part C, Vol. 48, pp. 468-490. [URL]
- Zhen (Sean) Qian and Michael Zhang (2011), “The economics of parking provision for the morning commute”, Transportation Research Part A, Vol.45(9), pp. 861-879. [URL]
Project page: Cyber-Physical System Virtual Organization Project Page
Award: NSF Award
Team: Sean Qian (PI, CMU), Xidong Pi (CMU)
Funding source: Pennsylvania Infrastructure Technology Alliance
Start/End Time: 2014-2015
The essential idea is to fully utilize the big data in public transit to provide travelers fine-grained customizable information regarding transit service performance (efficiency, reliability and quality). By monitoring day-to-day transit service and how users respond to information provision, we can develop a better understanding of travelers’ preferences on efficiency, reliability and quality of transit service, as well as their modal choices. Big data and> Dynamic Traffic Analysis and Travel Demand Management for Center City Bridge Reconstruction Plans
Team: Sean Qian (PI, CMU), Pinchao Zhang (CMU), Wei Ma (CMU)
Funding source: PennDOT, T-SET, Traffic 21
Start/End Time: 2014-2015
The Philadelphia Metropolitan Region is traffic data rich comparing to other metropolitan areas in the U.S. Various data sets in the Philadelphia region, including traditional traffic sensors (loops, cameras, etc.) and cutting-edge sensors (Bluetooth, GPS probe, parking, etc.), are available and have been archived for a decade. The rich data sets allow us to learn travelers’ behavior accurately and develop an in-depth understanding of non-recurrent traffic in large-scale networks. This research develops a regional dynamic network model that simulates millions of trips in the Philadelphia Metropolitan Region and captures those travelers’ travel behavior. It can be applied directly to predict traffic impact of planned and unplanned incidents, and provide real-time decision making for traffic operations. The regional model will also be tested as a real-time traffic management tool for two planned incidents, I-95 closures and Center City bridge closures.
Publication
Wei Ma, Pinchao Zhang and Sean Qian (2016), “Dynamic Network Analysis and Real-time Traffic Management for Philadelphia Metropolitan Area”, the Pennsylvania Department of Transportation (PennDOT). [URL]
-->The Microsoft Threat Modeling Tool 2018 was released as GA in September 2018 as a free click-to-download. The change in delivery mechanism allows us to push the latest improvements and bug fixes to customers each time they open the tool, making it easier to maintain and use.This article takes you through the process of getting started with the Microsoft SDL threat modeling approach and shows you how to use the tool to develop great threat models as a backbone of your security process.
This article builds on existing knowledge of the SDL threat modeling approach. For a quick review, refer to Threat Modeling Web Applications and an archived version of Uncover Security Flaws Using the STRIDE Approach MSDN article published in 2006.
To quickly summarize, the approach involves creating a diagram, identifying threats, mitigating them and validating each mitigation. Here’s a diagram that highlights this process:
Starting the threat modeling process
When you launch the Threat Modeling Tool, you'll notice a few things, as seen in the picture:
Threat model section
| Component | Details |
|---|---|
| Feedback, Suggestions and Issues Button | Takes you the MSDN Forum for all things SDL. It gives you an opportunity to read through what other users are doing, along with workarounds and recommendations. If you still can’t find what you’re looking for, email tmtextsupport@microsoft.com for our support team to help you |
| Create a Model | Opens a blank canvas for you to draw your diagram. Make sure to select which template you’d like to use for your model |
| Template for New Models | You must select which template to use before creating a model. Our main template is the Azure Threat Model Template, which contains Azure-specific stencils, threats and mitigations. For generic models, select the SDL TM Knowledge Base from the drop-down menu. Want to create your own template or submit a new one for all users? Check out our Template Repository GitHub Page to learn more |
| Open a Model | Opens previously saved threat models. The Recently Opened Models feature is great if you need to open your most recent files. When you hover over the selection, you’ll find 2 ways to open models:
|
| Getting Started Guide | Opens the Microsoft Threat Modeling Tool main page |
Template section
| Component | Details |
|---|---|
| Create New Template | Opens a blank template for you to build on. Unless you have extensive knowledge in building templates from scratch, we recommend you to build from existing ones |
| Open Template | Opens existing templates for you to make changes to |
The Threat Modeling Tool team is constantly working to improve tool functionality and experience. A few minor changes might take place over the course of the year, but all major changes require rewrites in the guide. Refer to it often to ensure you get the latest announcements.
Building a model
In this section, we follow:
- Cristina (a developer)
- Ricardo (a program manager) and
- Ashish (a tester)
They are going through the process of developing their first threat model.
Ricardo: Hi Cristina, I worked on the threat model diagram and wanted to make sure we got the details right. Can you help me look it over?Cristina: Absolutely. Let’s take a look.Ricardo opens the tool and shares his screen with Cristina.
Cristina: Ok, looks straightforward, but can you walk me through it?Ricardo: Sure! /xcom-enemy-within-download-mac.html. Here is the breakdown:
- Our human user is drawn as an outside entity—a square
- They’re sending commands to our Web server—the circle
- The Web server is consulting a database (two parallel lines)
What Ricardo just showed Cristina is a DFD, short for Data Flow Diagram. The Threat Modeling Tool allows users to specify trust boundaries, indicated by the red dotted lines, to show where different entities are in control. For example, IT administrators require an Active Directory system for authentication purposes, so the Active Directory is outside of their control. Sites for downloading android applications.
Cristina: Looks right to me. What about the threats?Ricardo: Let me show you.
Analyzing threats
Once he clicks on the analysis view from the icon menu selection (file with magnifying glass), he is taken to a list of generated threats the Threat Modeling Tool found based on the default template, which uses the SDL approach called STRIDE (Spoofing, Tampering, Info Disclosure, Repudiation, Denial of Service and Elevation of Privilege). The idea is that software comes under a predictable set of threats, which can be found using these 6 categories.
This approach is like securing your house by ensuring each door and window has a locking mechanism in place before adding an alarm system or chasing after the thief.
Ricardo begins by selecting the first item on the list. Here’s what happens:
First, the interaction between the two stencils is enhanced
Second, additional information about the threat appears in the Threat Properties window
The generated threat helps him understand potential design flaws. The STRIDE categorization gives him an idea on potential attack vectors, while the additional description tells him exactly what’s wrong, along with potential ways to mitigate it. He can use editable fields to write notes in the justification details or change priority ratings depending on his organization’s bug bar.
Azure templates have additional details to help users understand not only what’s wrong, but also how to fix it by adding descriptions, examples and hyperlinks to Azure-specific documentation.
The description made him realize the importance of adding an authentication mechanism to prevent users from being spoofed, revealing the first threat to be worked on. A few minutes into the discussion with Cristina, they understood the importance of implementing access control and roles. Ricardo filled in some quick notes to make sure these were implemented.
As Ricardo went into the threats under Information Disclosure, he realized the access control plan required some read-only accounts for audit and report generation. He wondered whether this should be a new threat, but the mitigations were the same, so he noted the threat accordingly.He also thought about information disclosure a bit more and realized that the backup tapes were going to need encryption, a job for the operations team.
Threats not applicable to the design due to existing mitigations or security guarantees can be changed to “Not Applicable” from the Status drop-down. There are three other choices: Not Started – default selection, Needs Investigation – used to follow up on items and Mitigated – once it’s fully worked on.
Reports & sharing
Once Ricardo goes through the list with Cristina and adds important notes, mitigations/justifications, priority and status changes, he selects Reports -> Create Full Report -> Save Report, which prints out a nice report for him to go through with colleagues to ensure the proper security work is implemented.
If Ricardo wants to share the file instead, he can easily do so by saving in his organization’s OneDrive account. Once he does that, he can copy the document link and share it with his colleagues.
Threat modeling meetings
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When Ricardo sent his threat model to his colleague using OneDrive, Ashish, the tester, was underwhelmed. Seemed like Ricardo and Cristina missed quite a few important corner cases, which could be easily compromised. His skepticism is a complement to threat models.
In this scenario, after Ashish took over the threat model, he called for two threat modeling meetings: one meeting to synchronize on the process and walk through the diagrams and then a second meeting for threat review and sign-off.
In the first meeting, Ashish spent 10 minutes walking everyone through the SDL threat modeling process. He then pulled up the threat model diagram and started explaining it in detail. Within five minutes, an important missing component had been identified.
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A few minutes later, Ashish and Ricardo got into an extended discussion of how the Web server was built. It was not the ideal way for a meeting to proceed, but everyone eventually agreed that discovering the discrepancy early was going to save them time in the future.
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In the second meeting, the team walked through the threats, discussed some ways to address them, and signed off on the threat model. They checked the document into source control and continued with development.
Thinking about assets
Some readers who have threat modeled may notice that we haven't talked about assets at all. We've discovered that many software engineers understand their software better than they understand the concept of assets and what assets an attacker may be interested in.
If you're going to threat model a house, you might start by thinking about your family, irreplaceable photos or valuable artwork. Perhaps you might start by thinking about who might break in and the current security system. Or you might start by considering the physical features, like the pool or the front porch. These are analogous to thinking about assets, attackers, or software design. Any of these three approaches work.
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The approach to threat modeling we've presented here is substantially simpler than what Microsoft has done in the past. We found that the software design approach works well for many teams. We hope that include yours.
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Next Steps
Send your questions, comments and concerns to tmtextsupport@microsoft.com. Download the Threat Modeling Tool to get started.