Control Any Sim 1.2.2

Manually setting one of the environment variables (OMP_NUM_THREADS,MKL_NUM_THREADS, OPENBLAS_NUM_THREADS, or BLIS_NUM_THREADS)will take precedence over what joblib tries to do. The total number ofthreads will be n_jobs * _NUM_THREADS. Note that setting thislimit will also impact your computations in the main process, which willonly use _NUM_THREADS. Joblib exposes a context manager forfiner control over the number of threads in its workers (see joblib docslinked below).

Control Any Sim 1.2.2

Note that scikit-learn tests are expected to run deterministically withexplicit seeding of their own independent RNG instances instead of relying onthe numpy or Python standard library RNG singletons to make sure that testresults are independent of the test execution order. However some tests mightforget to use explicit seeding and this variable is a way to control the initialstate of the aforementioned singletons.

To easily switch between simulation and reality, we need be able to modularly remove the perceptioncomponent and operate directly on simulated ground truth during development. We also need to easilyswitch between controlling just the simulated robot and streaming commands to an external robot.Moreover, the decision framework should support reactivity as a first class citizen. All of theserequirements are innately supported by Cortex as described below.

Information about the latest command and the latest articulation action (low-level joint command) iscached off in the commander and accessible by modules in the control layer for translating thosecommands to the physical robot.

The development based on RflySim generally includes the following five phases:modeling phase, controller design phase, Software-In-the-Loop (SIL) simulationtest phase, Hardware-In-the-Loop (HIL) simulation test phase, and experimentaltest phase. By using code-generation technology by MATLAB/Simulink, thecontroller can be easily uploaded to hardware automatically for the HILsimulation and real test phase.

3). UAV Swarm Simulation . We provide interfaces to perform HIL/SIL simulations for multiple Pixhawk with computers in the same local area network. Mavlink communications through serial (Radio telemetry) or network (WIFI) to control Pixhawk by Simulink or C++ programs are also supported.

6). Vision-based control . Our 3D environments based on UE4 can send real-time images for other programs (Python/C or C++/Simulink) to process the images and feedback the control signals to HIL/SIL simulation platform to achieve vision-based control.

The commercial-level RflySim platform uses a high-performance real-timesimulation computer to simulate the vehicle motion and use FPGA to simulateand replace all sensor chips on the autopilot (control) system. As shown inFig. 0.3, the goal of commercial-level RflySim platform is to build a unifiedtest framework for different types of vehicles and autopilot systems. Thehardware and software structures of the commercial-level RflySim platformare presented in Fig. 0.4 and Fig. 0.5 respectively. Compared with theeducation-level RflySim platform or other simulators in the world, it has thefollowing advantages

2). Practicability : plug and test for different autopilot products; only need to know the sensor models used by the autopilot with no requirement to access or modify the source code of control systems, then the platform can perform black-box tests for different autopilot products.

This document discusses common password authentication threats but focuses primarily on credential stuffing attacks. It recommends some technical and non-technical security controls that you can implement to prevent and mitigate credential stuffing attacks. While this document addresses GC systems, non-GC organizations can also apply the recommendations.

This document addresses security control protections against credential stuffing attacks. Although credential stuffing attacks are closely related to brute force, password spraying, or other forms of password-related attacks, the suggested controls are primarily tailored to protect against credential stuffing.

ITSP.30.031 v3 Footnote 1 provides information on recommended security controls for GC web application systems. Additionally, NIST provides guidance on how to develop and deploy secure authentication architectures in the following publications:

Identification and authentication refer to the family of security controls that enables an information system to uniquely identify and authenticate users. These include authentication activities related to guiding policies and procedures, identifying users uniquely, managing identifiers, managing authenticators, managing feedback messages, proofing identities, and re authenticating.

For a detailed description of the identification and authentication control activities, refer to the section on identification and authentication in Annex 3a of ITSG-33 IT Security Risk Management: A Lifecycle Approach Footnote 8.

Password spraying is a variant of an online brute force attack. It involves the use of a small set of common passwords to log into several user accounts. The threat actor targets multiple user accounts to evade account lockout controls and rate limiting protections.

Defending against credential stuffing attacks can be quite daunting. A web application is vulnerable not because of a security breach on its infrastructure but rather because of its users reusing login credentials. System security administrators may detect an attack in progress by monitoring and running analytics on failed authentication login records. In section 3, we discuss more security control mechanisms that you can use to mitigate and protect against credential stuffing attacks.

Defending against credential stuffing attacks requires a combination of measures, including risk-based and defense-in-depth approaches. MFA mechanisms, when implemented properly, will defeat credential stuffing attacks. However, threat actors can exploit additional vulnerabilities to bypass MFA protections. Below, we present some security control strategies that your organization should consider as recommended protections.

Your password policy should mandate that your web application developers and users follow secure password principles. The policy should set minimum requirements for the composition and complexity of user passwords on your system. Review your policy regularly to provide updated guidance. Implement appropriate system tools to support enforcement activities and rectify policy violations. The policy should define security control requirements for the storage and management of user passwords. The list below highlights essential elements to consider in your password policy:

Strengthening your authentication workflows and removing outdated algorithms requires that you use tracking application packages in your system. You should remove all vulnerable or obsolete packages. Do not allow users to bypass any step in a multi-step authentication process. Implement proper session management controls.

Dynamic rate-limiting and throttling mechanisms are effective against large-scale, automated authentication attacks. You can use security mechanisms, such as CAPTCHAs, dynamic rate limits, and timeout triggers, to reduce or block scripted attacks. Historically, CAPTCHA controls are known to have adverse effects on user experience but recent advancements in the technology have led to significant improvements. Integration of behavioural analysis features and performing the CAPTCHA test in the background with no human interaction are some examples. Most implementation suggests activating CAPTCHAs based on observed behaviours (e.g. suspicious logins, activity rate spikes, or known bot patterns). Sophisticated attacks may bypass CAPTCHA controls.

Because threat actors change infrastructure often, using threat intelligence feeds can enhance your controls. Use IP reputation or threat intelligence services to gain insights about visitors to your application. For some applications, consider deny lists to block suspicious sources for a specified period rather than permanently. You may consider geofencing network controls for web applications with no use case outside a particular geographical region and implementing permanent blocks on traffic from regions known for malicious activity. Out of region access requests can be handled through an authorization process.

Due to the sophistication and scale supported by many web applications, using anomaly detection solutions can help you identify and block suspicious logins. Assign risk scores to user requests by using user behaviour analytic solutions to capture mouse movements, screen swipes and typing patterns. Risk score outcomes that fall outside expected behaviours will trigger appropriate security control action. A block action is triggered when a malicious activity exceeds the score threshold. You can use statistical or frequency analysis to flag and prevent the use of weak passwords on your platform.

Overall, information returned from client-side fingerprinting cannot be trusted. Users may deploy tools to prevent information gathering or spoofing data to mislead the web application. You should weigh and deploy client-side fingerprinting results alongside other controls.

In some geographical jurisdictions, solutions proposed may inadvertently encounter legal constraints. You should evaluate the legal implications of the solutions you select. Your organization should understand how the controls its considering may impact users, legal requirements, and privacy risks.

Implementing MFA will protect against a vast majority of credential stuffing attacks. Traditional controls such as malicious network filtering and blocking malicious browser agents are largely becoming ineffective against new techniques. Modern authentication methods will provide alternative options for consideration. To protect your organization from credential stuffing attacks, consider the following techniques: 041b061a72