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NEW QUESTION: 1
内部監査人は、費用効果の高い方法で監査理論をテストするためにデータをサンプリングしたいと考えています。次のサンプリング戦略のうちどれを使用するべきですか？
A. 非統計的サンプリングのみ
B. 統計的サンプリングのみ
C. 統計的サンプリングと非統計的サンプリングの両方の組み合わせ。
D. 監査理論をテストするためのどちらのアプローチも費用対効果が高くありません。
Answer: A

NEW QUESTION: 2
Which setting should you increase if a process engine runs out of memory during testing?
A. Activation Limit
B. Thread Count
C. Max Jobs
D. Maximum Heap Size
E. Flow Limit
F. Step Limit
Answer: D

NEW QUESTION: 3
サービスとしてのソフトウェア（SaaS）企業は、ドキュメント管理のためのクラウドソリューションを民間の法律事務所と公共部門に提供しています。地方自治体のクライアントは最近、機密性の高いドキュメントを国外に保管することはできないと義務付けました。会社のCIOは、ソリューションアーキテクトに、アプリケーションがこの新しい要件に適応できることを確認するよう依頼しています。バックアップは現在実行されていないため、CIOはこれらのドキュメントの適切なバックアップ計画も求めています。これらの要件を満たすソリューションはどれですか。
A. Amazon S3でドキュメントに通常または秘密のタグを付けます。同じAWSアカウントとAWSリージョンに個別のS3バックアップバケットを作成します。別のAWSアカウントにクロスリージョンS3バケットを作成します。適切なIAMロールを設定して、S3バケットへのクロスリージョン権限を許可します。 Amazon CloudWatchのスケジュールされたイベントによってトリガーされるAWS Lambda関数を設定して、シークレットとしてタグ付けされたオブジェクトをS3バックアップバケットにコピーし、通常としてタグ付けされたオブジェクトをクロスリージョンS3バケットにコピーします
B. Amazon S3で機密性の高いドキュメントに秘密のタグを付けます。同じAWSアカウントとAWSリージョンに個別のS3バックアップバケットを作成します。オブジェクトタグに基づくS3選択的クロスリージョンレプリケーションを使用して、通常のドキュメントを別のAWSリージョンに移動するシークレットとしてタグ付けされたS3オブジェクトのAmazon CloudWatchイベントルールを作成して、AWS Lambda関数をトリガーし、同じAWSの別のバケットにオブジェクトを複製します領域。
C. Amazon S3でドキュメントに通常または秘密のタグを付けます。同じAWSアカウントとAWSリージョンに個別のS3バックアップバケットを作成します。オブジェクトタグに基づくS3選択的クロスリージョンレプリケーションを使用して、通常のドキュメントを別のAWSリージョンのS3バケットに移動します。新しいS3オブジェクトがメインバケットに作成されたときにトリガーするAWS Lambda関数を構成して、同じAWSリージョンのS3バケットにシークレットとしてタグ付けされたドキュメントのみを複製します
D. Amazon S3では通常、機密性が高くないドキュメントにタグを付けます。ユーザーごとに個別のS3バケットを作成する各ユーザーのバケットにオブジェクトをアップロードします。これらのバケットからのS3バケットのレプリケーションを、別のAWSアカウントとAWSリージョンの中央のS3バケットに設定します。 Amazon CloudWatchのスケジュールされたイベントによってトリガーされるAWS Lambda関数を設定して、S3バックアップバケットでシークレットとしてタグ付けされたオブジェクトを削除します。
Answer: B

NEW QUESTION: 4
Behavioral-based systems are also known as?
A. Misuse detective systems
B. Profile-based systems
C. Pattern matching systems
D. Rule-based IDS
Answer: B
Explanation:
There are two complementary approaches to detecting intrusions, knowledge-based approaches and behavior-based approaches. This entry describes the second approach. It must be noted that very few tools today implement such an approach, even if the founding Denning paper {D. Denning, An Intrusion Detection Model, IEEE transactions on software engineering} recognizes this as a requirement for IDS systems.
Behavior-based intrusion detection techniques assume that an intrusion can be detected by observing a deviation from normal or expected behavior of the system or the users. The model of normal or valid behavior is extracted from reference information collected by various means. The intrusion detection system later compares this model with the current activity. When a deviation is observed, an alarm is generated. In other words, anything that does not correspond to a previously learned behavior is considered intrusive. Therefore, the intrusion detection system might be complete (i.e. all attacks should be caught), but its accuracy is a difficult issue (i.e. you get a lot of false alarms).
Advantages of behavior-based approaches are that they can detect attempts to exploit new and unforeseen vulnerabilities. They can even contribute to the (partially) automatic discovery of these new attacks. They are less dependent on operating system-specific mechanisms. They also help detect 'abuse of privileges' types of attacks that do not actually involve exploiting any security vulnerability. In short, this is the paranoid approach: Everything which has not been seen previously is dangerous.
The high false alarm rate is generally cited as the main drawback of behavior-based techniques
because the entire scope of the behavior of an information system may not be covered during the
learning phase. Also, behavior can change over time, introducing the need for periodic online
retraining of the behavior profile, resulting either in unavailability of the intrusion detection system
or in additional false alarms. The information system can undergo attacks at the same time the
intrusion detection system is learning the behavior. As a result, the behavior profile contains
intrusive behavior, which is not detected as anomalous.
Herve Debar
IBM Zurich Research Laboratory
The following answers are incorrect:
Pattern matching systems are signature-based (e.g. Anti-virus).
Misuse detection systems is another name for signature-based IDSs.
Rule-based IDS is a distractor.
The following reference(s) were/was used to create this question:
Shon Harris AIO - 4th edition, Page 254
and
http://www.sans.org/security-resources/idfaq/behavior_based.php