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NEW QUESTION: 1
You federate your on-premises Active Directory with Azure Active Directory (Azure AD) by using Active Directory Federations Services (AD FS) 2.0. You plan to secure cloud and on-premises resources by using an Azure Multi-Factor Authentication (MFA) server. You install the MFA server on the AD FS proxy server.
You configure the MFA server and successfully import all AD users into the MFA user database.
Development teams in your organization must be able to secure their non-browser based apps.
You need to document the authentication mechanisms.
For each requirement, which authentication mechanism is used. To answer, select the appropriate authentication mechanism from each list in the answer area.

Answer:
Explanation:

Explanation

Securing Azure AD resources using Azure MFA
First factor: performed on-premises using AD FS.
Second factor: phone-based method carried out using cloud authentication.
Securing Azure AD resources using Active Directory FS
First factor: performed on-premises using AD FS.
Second factor: performed on-premises by honoring the claim.
References:
https://docs.microsoft.com/en-us/azure/multi-factor-authentication/multi-factor-authentication-get-started-adfs

NEW QUESTION: 2
Why would anomaly detection IDSs often generate a large number of false positives?
A. Because normal patterns of user and system behavior can vary wildly.
B. Because they can only identify correctly attacks they already know about.
C. Because they can't identify abnormal behavior.
D. Because they are application-based are more subject to attacks.
Answer: A
Explanation:
Explanation/Reference:
Explanation:
An Anomaly-Based Intrusion Detection System, is a system for detecting computer intrusions and misuse by monitoring system activity and classifying it as either normal or anomalous. The classification is based on heuristics or rules, rather than patterns or signatures, and attempts to detect any type of misuse that falls out of normal system operation. This is as opposed to signature-based systems, which can only detect attacks for which a signature has previously been created.
In order to determine what is attack traffic, the system must be taught to recognize normal system activity.
This can be accomplished in several ways, most often with artificial intelligence type techniques. Systems using neural networks have been used to great effect. Another method is to define what normal usage of the system comprises using a strict mathematical model, and flag any deviation from this as an attack. This is known as strict anomaly detection.
Anomaly-based Intrusion Detection does have some shortcomings, namely a high false-positive rate and the ability to be fooled by a correctly delivered attack.
A cause of the high false-positive rate is that normal patterns of user and system behavior can vary wildly.
Different people do things in different ways. These can appear as 'anomalies' to the IDS and generate a false positive.
Incorrect Answers:
A: It is not true that anomaly detection IDSs can only identify correctly attacks they already know about.
This statement describes signature-based IDSs.
B: It is not true that anomaly detection IDSs are application-based and are more subject to attacks. They can be hardware-based. Furthermore, hackers attack computer systems; they don't attack IDSs.
C: It is not true that anomaly detection IDSs cannot identify abnormal behavior; that's exactly what they do.
References:
https://en.wikipedia.org/wiki/Anomaly-based_intrusion_detection_system

NEW QUESTION: 3

A. Option E
B. Option A
C. Option D
D. Option B
E. Option C
Answer: B,D
Explanation:
Explanation: + Log collection of event records from sources throughout the organization provides important forensic tools and helps to address compliance reporting requirements.
+ Normalization maps log messages from different systems into a common data model, enabling the organization to connect and analyze related events, even if they are initially logged in different source formats.
+ Correlation links logs and events from disparate systems or applications, speeding detection of and reaction to security threats.
+ Aggregation reduces the volume of event data by consolidating duplicate event records.
+ Reporting presents the correlated, aggregated event data in real-time monitoring and long-term summaries.
Source: http://www.cisco.com/c/dam/en/us/solutions/collateral/enterprise/design-zone- smart- businessarchitecture/sbaSIEM_deployG.pdf