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NEW QUESTION: 1
Why would anomaly detection IDSs often generate a large number of false positives?
A. Because they can't identify abnormal behavior.
B. Because normal patterns of user and system behavior can vary wildly.
C. Because they can only identify correctly attacks they already know about.
D. Because they are application-based are more subject to attacks.
Answer: B
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: 2
A. QUOTAWARNINGMESSAGE
B. QUOTAWARNING
C. QUOTAWARNINGTEXT
D. QUOTAWARNINGURL
Answer: C
Explanation:
Reference:http://publib.boulder.ibm.com/infocenter/domhelp/v8r0/index.jsp?topic=%2Fcom.ibm.he
lp.domino.admin85.doc%2FH_CUSTOMIZING_MAIL_QUOTA_WARNING_TEXT_USING_A_NO
TES_INI_FILE_SETTING_STEPS.html
NEW QUESTION: 3
Amazon S3の構造は次のとおりです。S3：//BUCKET/FOLDERNAME/FILENAME.zip 1秒間に何千ものPUTリクエストでパフォーマンスを最適化するS3のベストプラクティスはどれですか。
A. ファイル名の前にタイムスタンプを付けます。たとえば、s3：// BUCKET / FOLDERNAME / 2013-26-05-15-00-です。
00-FILENAME.zip
B. ファイル名の先頭にランダムな16進ハッシュを付けます。例えば、s3：// BUCKET / FOLDERNAME / 23a6- FILENAME.zipのようになります。
C. フォルダ名の先頭にランダムな16進ハッシュを付ける。例えば、s3：// BUCKET / 23a6-FOLDERNAME / FILENAME.zipのようになります。
D. フォルダ名の前にユーザーIDを付けます。例えば、s3：//BUCKET/2013-FOLDERNAME/FILENAME.zipのようになります。
Answer: C
Explanation:
Explanation
Refer AWS documentation - S3 Performance
Amazon S3 maintains an index of object key names in each AWS region. Object keys are stored in UTF-8 binary ordering across multiple partitions in the index. The key name dictates which partition the key is stored in. Using a sequential prefix, such as time stamp or an alphabetical sequence, increases the likelihood that Amazon S3 will target a specific partition for a large number of your keys, overwhelming the I/O capacity of the partition. If you introduce some randomness in your key name prefixes, the key names, and therefore the I/O load, will be distributed across more than one partition.
If you anticipate that your workload will consistently exceed 100 requests per second, you should avoid sequential key names. If you must use sequential numbers or date and time patterns in key names, add a random prefix to the key name. The randomness of the prefix more evenly distributes key names across multiple index partitions. Examples of introducing randomness are provided later in this topic.
One way to introduce randomness to key names is to add a hash string as prefix to the key name. For example, you can compute an MD5 hash of the character sequence that you plan to assign as the key name. From the hash, pick a specific number of characters, and add them as the prefix to the key name. The following example shows key names with a four-character hash