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We recommend that you enable modern authentication, certificate-based authentication, and the other features that are listed in this step to lower the risk of brute force attacks. In addition to removing one of the attack vectors that are currently being used through Exchange Online, deploying modern authentication for your Office client applications enables your organization to benefit from multi-factor authentication. Modern authentication is supported by all the latest Office applications across the Windows, iOS, and Android platforms.
For more information, see How to deploy modern authentication for Office Because user name and password-based access requests will continue to be vulnerable despite our proactive and reactive defenses, organizations should plan to adopt non-password-based access methods as soon as possible.
When certificate-based authentication is used as an alternative to user name and password-based access, user accounts and access are protected in the following manner:.
Because users do not use their passwords over the Internet, those passwords are less susceptible to disclosure. User name and password endpoints can be blocked completely at the firewall.
This removes the attack vector for lockout or brute force attacks. Even if user name and password endpoints are kept available at the firewall, malicious user name and password-based requests that cause a lockout do not affect access requests that use certificates.
Therefore, the legitimate user's access is preserved. Azure MFA is another non-password-based access method that you can use in the same manner as certificate-based authentication to avoid using password and user-name endpoints completely. Windows Hello for Business is available in Windows Windows Hello for Business enables password-free access from the extranet, based on strong cryptographic keys that are tied to both the user and the device.
Disable the legacy endpoints that are used by EAS clients through Exchange Online, such as the following:. Doing this might disrupt some functionality. However, it can help reduce the surface vectors that are available for attackers to exploit.
Also, we recommend that you disable unused endpoints. If the user account is used as a service account, the latest credentials might not be updated for the service or application. If the user loses or breaks the device, the user contacts the issuer of the device and each relying party learns of the change in status. Service providers do not have to worry about managing end user credentials and devices, but simply contact the network.
In connection with the hosted fraud detection service , each party in the fraud intelligence network shares fraud intelligence e. By sharing transaction information from across the network , the fraud detection service can compare patterns of behavior across the participating sites in real time, and help detect and stop attacks that could not be detected with data from a single site. The service does not require personally identifiable information to detect fraud, but can use unique pseudonyms to identify end users across the different sites.
The service provider may query external sources to gain network level intelligence derived apart from the fraud intelligence network information. The fraud detection service is therefore able to better combat criminals on the internet who use many different mechanisms to capture personal information, such as phishing web sites, key loggers, false store fronts, and database theft. Often, criminals try to use the same information on multiple web sites, testing login information by trial and error, establishing multiple fraudulent accounts, or other malicious activities.
When a relying party requires step a credential to authenticate an end user , the user provides step a credential response associated with a credential possessed by the user to be validated step by an authentication service In one embodiment of the present invention, the user may provide the credential response to the relying party , who then checks the information with the authentication service via a backend integration. In another embodiment, the relying party may redirect the user to the authentication service to enter the credential response.
In this embodiment, the authentication service creates a digitally signed assertion—stating whether the attempt was good or bad—and encodes it into a short ASCII string that can be attached to a URL to be passed back to the relying party using an http redirect.
In yet another embodiment, the relying party may utilize AJAX Asynchronous Javascript and XML so that, instead of redirecting the user's web browser to different web pages, lavascript on the relying party's page is used to forward the credential response to the authentication service e.
The relying party then monitors step transactions associated with the user , which may include a login, purchase, click-thru, or any other activity by the user on the relying party's site, and provides information associated with the transactions to the fraud detection service to be evaluated for suspicious activity. To improve security, the fraud detection service evaluates the transaction information for suspicious activity based at least in part on other transaction information provided to the fraud detection service by the fraud intelligence network sites.
First, a user provides step login credentials to a relying party for validation step If the credentials are bad, the login is refused step , and if the credentials are good, then the relying party forwards step information associated with the user's login to a fraud detection service The fraud detection service checks step for suspicious activity, and if no suspicious activity is found, the transaction passes step , the relying party is informed of the decision, and the user is allowed step to log in.
If, on the other hand, the primary check reveals suspicious activity, then the fraud detection service proceeds step to use more sophisticated, complex, and invasive techniques to validate that the credential is legitimate.
After this secondary check, the fraud detection service decides if the transaction is fraudulent or legitimate. The primary fraud checks may be based on properties of the transaction, properties of the user account, and transaction history.
No human intervention is required; these checks may be completely automated. More importantly, no extra steps are added to the process. The secondary fraud checks add additional steps to the process.
The fraud detection service may require a telephone, email, or SMS confirmation of the user's identity. The purpose of these checks is to provide additional information to validate the user's identity. If the secondary checks succeed, the fraud check succeeds step , and the user is allowed to log in step If the secondary checks fail, the fraud check fails step and the fraud detection service reports the failure to the relying party The relying party may refuse step the login request, and may choose to refer the customer to customer service for resolution.
In an embodiment of the present invention, information associated with a refused login based on step may also be sent to the fraud detection service by the relying party In order to check for suspicious activity, the fraud detection service may be fed information about each transaction. Using a scoring model or rules, the service outputs a decision. Each decision, and all transaction details, may then be saved to a transaction log.
Periodically, an Extraction Translation and Loading ETL process may be used to calculate some information for a transaction history database Some details about recent transactions may also be saved directly to the transaction history database e.
The scoring engine is designed to distinguish between good and bad authentication attempts. There are two types of login transactions: legitimate authentication attempts and fraudulent authentication attempts.
In order to distinguish between the two, the engine attempts to learn whether a login does not fit a pattern of other legitimate attempts, and whether a login fits the pattern of other fraudulent attempts. Over time, a good picture of what a legitimate login attempt looks like for each account can be developed by the engine.
For example, suppose that a user in Minnesota uses a token to access his trading account. Over time, it might become evident that he usually logs in only during market hours, only logs in times per week, and only logs in from an IP address in the Midwest.
Any deviation from this pattern is a sign of suspicious activity. For example, the engine will deem suspicious transaction information that shows twenty attempted authentications at midnight EST from Russia. The engine characterizes the usual usage pattern and then looks for deviations from that pattern.
Other examples of baseline behavior could be based upon the known geolocation of the user, which can be compared to actual location data obtained from a GPS system associated with the user e.
Any unusual discrepancy between his known location and these locations could indicate fraud. Furthermore, any discrepancy among these data could indicate fraud. A picture of fraudulent login attempts can also be developed by the engine over time. For example, it might become evident that many fraudulent login attempts are through anonymous IP proxies, or from Eastern European countries. It might become evident that fraudulent attempts try to use a token at the wrong web site, or that dictionary attacks are made against a token e.
Rules can be coded to detect and counter practically any kind of fraudulent behavior. In order to detect fraud, the fraud detection service needs to collect enough information about each transaction to make a reasonable guess. In order to capture broad enough information, the following fields can be used:.
The fraud detection service also needs to capture deep enough information. This means producing a historical record of transactions, going back at least 90 days and preferably for a year, for example. Over time, summary information could be built such as the average number of logins per month that could be used to look for suspicious activity. The fraud detection service analyzes a transaction by a policy engine and, depending on the policy, is passed through an anomaly engine which answer with a status anomaly or not and a confidence factor how much the engine is confident in its decision that is processed back by the policy engine.
The following provides an embodiment of the data flow process:. Each rule may have a condition and a list of actions. For example, a condition can be that an event occurred, data in a graph changed, fact value changed, etc.
An action can be to change data in a graph, set a fact value, send an email, run a script, etc. The rules may be grouped into policies. Policies may be logically stored in directories. In addition to providing functionality around setting filters based around how anomalous and how confident the engine is in the decision, the fraud detection service may use its determinations for further increasing accuracy.
In order to achieve this unsupervised learning, the fraud detection service may utilize clustering algorithms in its anomaly engine to decide which of the user's actions correspond to natural behavior and which are exceptional, without any assistance.
The clustering algorithm may be based on the ROCK hierarchical clustering algorithm RObust Clustering using linKs , which is an agglomerative hierarchical clustering algorithm based on the notion of neighbors and links as follows.
Two data elements are considered as neighbors if our similarity upon a domain expert or similarity matrix exceeds a certain threshold. At first, all n data elements are mapped to n clusters respectively. Then, with each iteration, the engine merges between the two closest clusters such that both clusters fulfill the maximum value of Link C i ,C j , for any pair of clusters C i and C j.
Therefore it is usually no problem roaming between different WLANs, unless the device's session expires and it has to auto log in on a foreign network. Simply log in to the account manager using your webbrowser. Navigate to the login history, it will show a list of failed attempts. Confirm that these were yours and you are good to go. In this case, use DummyDroid to confirm it has better error reporting than Raccoon.
However, there are a few things you can try to improve your chances for getting yan account unblocked:. In case you still run into an endless loop, give up. The account is lost for good. Google just avoids telling you so because a final decision would mean opening the way for a legal path.
For yet unknown reasons, Google sometimes fails to recognize Raccoon as a genuine Android device. In this case, the workaround is to pose as a legacy email client. NOTE: you have to enable two factor authentication for doing this. English English. The thing to understand about Google accounts is that they are dual use: to track user activity what websites are watched, what apps are used, where is the user,…?
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