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NEW QUESTION: 1
Which of the following types of Intrusion Detection Systems uses behavioral characteristics of a system's operation or network traffic to draw conclusions on whether the traffic represents a risk to the network or host?
A. Signature Analysis.
B. Host-based ID systems.
C. Anomaly Detection.
D. Network-based ID systems.
Answer: C
Explanation:
Explanation/Reference:
There are two basic IDS analysis methods: pattern matching (also called signature analysis) and anomaly detection.
Anomaly detection uses behavioral characteristics of a system's operation or network traffic to draw conclusions on whether the traffic represents a risk to the network or host. Anomalies may include but are not limited to:
Multiple failed log-on attempts
Users logging in at strange hours
Unexplained changes to system clocks
Unusual error messages
The following are incorrect answers:
Network-based ID Systems (NIDS) are usually incorporated into the network in a passive architecture, taking advantage of promiscuous mode access to the network. This means that it has visibility into every packet traversing the network segment. This allows the system to inspect packets and monitor sessions without impacting the network or the systems and applications utilizing the network.
Host-based ID Systems (HIDS) is the implementation of IDS capabilities at the host level. Its most significant difference from NIDS is that related processes are limited to the boundaries of a single-host system. However, this presents advantages in effectively detecting objectionable activities because the IDS process is running directly on the host system, not just observing it from the network. This offers unfettered access to system logs, processes, system information, and device information, and virtually eliminates limits associated with encryption. The level of integration represented by HIDS increases the level of visibility and control at the disposal of the HIDS application.
Signature Analysis Some of the first IDS products used signature analysis as their detection method and simply looked for known characteristics of an attack (such as specific packet sequences or text in the data stream) to produce an alert if that pattern was detected. For example, an attacker manipulating an FTP server may use a tool that sends a specially constructed packet. If that particular packet pattern is known, it can be represented in the form of a signature that IDS can then compare to incoming packets. Pattern- based IDS will have a database of hundreds, if not thousands, of signatures that are compared to traffic streams. As new attack signatures are produced, the system is updated, much like antivirus solutions.
There are drawbacks to pattern-based IDS. Most importantly, signatures can only exist for known attacks.
If a new or different attack vector is used, it will not match a known signature and, thus, slip past the IDS.
Additionally, if an attacker knows that the IDS is present, he or she can alter his or her methods to avoid detection. Changing packets and data streams, even slightly, from known signatures can cause an IDS to miss the attack. As with some antivirus systems, the IDS is only as good as the latest signature database on the system.
For additional information on Intrusion Detection Systems - http://en.wikipedia.org/wiki/ Intrusion_detection_system
Reference(s) used for this question:
Hernandez CISSP, Steven (2012-12-21). Official (ISC)2 Guide to the CISSP CBK, Third Edition ((ISC)2 Press) (Kindle Locations 3623-3625, 3649-3654, 3666-3686). Auerbach Publications. Kindle Edition.

NEW QUESTION: 2
分類モデルをトレーニングしたいデータを含むコンマ区切り値（CSV）ファイルがあります。
Azure Machine Learning Studioの自動機械学習インターフェイスを使用して、分類モデルをトレーニングしています。タスクタイプを分類に設定します。
自動機械学習プロセスが線形モデルのみを評価することを確認する必要があります。
あなたは何をするべきか？
A. 終了基準オプションをメトリックスコアのしきい値に設定します。
B. 自動フィーチャー化を実行するオプションをクリアします。
C. ディープラーニングを有効にするオプションをオフにします。
D. タスクタイプを回帰に設定します。
E. ブロックされたアルゴリズムのリストに、線形アルゴリズム以外のすべてのアルゴリズムを追加します。
Answer: B
Explanation:
Explanation
Automatic featurization can fit non-linear models.
Reference:
https://econml.azurewebsites.net/spec/estimation/dml.html
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-automated-ml-for-ml-models

NEW QUESTION: 3
What key words are contained in the application descriptor file (xs-app.json)?
There are 2 correct answers to this question.
Response:
A. routes
B. tenant-mode
C. authentication Method
D. role-templates
Answer: A,C