ai人工智能的本質和未來

Chinese philosophy yin and yang represent how the seemingly opposite poles can complement each other and achieve harmony.

中國的陰陽哲學代表著看似相反的兩極如何相互補充，實現和諧。

In cybersecurity, this ancient philosophy perfectly represents the relationship between supervised and unsupervised machine learning. For example, monitored machine learning processes can be used for detection, while unsupervised machine learning uses clustering. In the case of cybersecurity and data security research and development, monitored machine learning is often implemented in the form of machine learning algorithms.

在網絡安全中，這種古老的哲學完美地代表了有監督和無監督機器學習之間的關系。 例如，受監視的機器學習過程可用于檢測，而無監督的機器學習則使用聚類。 在網絡安全和數據安全研究與開發的情況下，受監視的機器學習通常以機器學習算法的形式實現。

It is not easy to describe Artificial Intelligence (AI). It has no clear definition. Most of the existing definitions try to express AI as a computer process that mimics human intelligence and behavior and acts intelligently. But this situation brings more questions such as what is intelligence? Do people always act smart and logical? Is the desired achievement for AI, human intelligence? Or can a computer perform better than a human? The definition of approaches that base AI on rational behavior refers to a computer doing things that are difficult to do. In this article, however, a pragmatic approach is adopted to simplify the issue and AI is defined as a scientific area responsible for producing computer-based solutions to the complex problems that human beings have difficulty in finding solutions.

描述人工智能(AI)并不容易。 它沒有明確的定義。 現有的大多數定義都試圖將AI表達為模仿人類智力和行為并以智能方式行事的計算機過程。 但是這種情況帶來了更多的問題，例如什么是智力？ 人們總是表現得聰明而合乎邏輯嗎？ 人工智能是人類的理想成就嗎？ 還是計算機的性能要比人類好？ 將AI建立在理性行為基礎上的方法的定義是指計算機執行難以完成的事情。 但是，在本文中，采用了務實的方法來簡化問題，并且AI被定義為負責為人類難以找到解決方案的復雜問題提供基于計算機的解決方案的科學領域。

AI Technology Landscape人工智能技術格局

The use of AI in cybersecurity is relatively new. While some cybersecurity experts argue that the answer to cybersecurity is machine learning to detect sophisticated breaches and that cybersecurity will only continue to succeed if the IT environment is secured by the help of AI-based solutions. Others argue that while machine learning is very good at finding similarities, it is not good enough at detecting anomalies and is therefore not suited to cybersecurity.

在網絡安全中使用AI相對較新。 盡管一些網絡安全專家認為，網絡安全的答案是機器學習來檢測復雜的漏洞，并且只有在基于AI解決方案的幫助下確保IT環境安全的情況下，網絡安全才會繼續取得成功。 其他人則認為，盡管機器學習非常善于發現相似之處，但它不足以檢測異常，因此不適合網絡安全。

Beyond these discussions, it is a fact that machine-learning has taken great steps in recent years, from autonomous tools to virtual assistants, from chatbots to face/object recognition. As we move towards a future where cybersecurity is much more integrated into our daily life, it is important to be aware of different approaches based on machine and deep learning in order to better defend the network and data security against increasingly complex and advanced attacks.

除了這些討論之外，事實上，近年來，機器學習已邁出了重要的一步，從自主工具到虛擬助手，從聊天機器人到人臉/物體識別。 隨著我們邁向將網絡安全與我們的日常生活更加融合的未來，重要的是要意識到基于機器和深度學習的不同方法，以便更好地保護網絡和數據安全免受日益復雜和高級的攻擊。

As you already may know, there are four types of machine learning algorithms to train a machine neural network: Supervised Learning, Unsupervised Learning, Semi-supervised Learning (also known as active learning), Reinforcement Learning. Supervised learning is about learning from a training data set, while unsupervised machines learn from the data itself that is limited in its ability to detect threats, as it only looks for details it has seen and flagged before, while unsupervised learning constantly scans the network and finds anomalies. Unsupervised learning, however, does not require labeled training data and is better suited to detecting suspicious activity, including detecting attacks that have never been observed before.

您可能已經知道，有四種類型的機器學習算法可以訓練機器神經網絡：監督學習，無監督學習，半監督學習(也稱為主動學習)，強化學習。 監督學習是關于從訓練數據集中學習，而不受監督的機器則是從數據本身中學習，這種數據在檢測威脅方面受到限制，因為它僅查找以前已經看到并標記過的細節，而不受監督的學習則不斷地掃描網絡和網絡。發現異常。 但是，無監督學習不需要標記的訓練數據，更適合于檢測可疑活動，包括檢測以前從未觀察到的攻擊。

Supervised learning is about learning from a training dataset. Supervised machines learn from the data itself, which is limited only by its ability to detect threats when searching for details that it has previously seen and marked. For unattended learning, tagged training data is not required and is more suitable for detecting suspicious activity, including detecting attacks that have never been observed before. Unsupervised learning constantly scans the network and finds anomalies.

監督學習是關于從訓練數據集中學習。 受監控的機器從數據本身中學習，這僅受其在搜索先前已查看和標記的詳細信息時檢測威脅的能力的限制。 對于無人值守的學習，不需要標記的訓練數據，它更適合于檢測可疑活動，包括檢測以前從未觀察到的攻擊。 無監督學習會不斷掃描網絡并發現異常情況。

Machine Learning Algorithms機器學習算法

Machine learning is already used to reduce the load that attack detection and prevention tools can handle as part of cybersecurity systems. AI algorithms similar to real human decision mechanisms try to model a decision mechanism.

機器學習已被用來減少攻擊檢測和防御工具可以作為網絡安全系統的一部分處理的負載。 與真實人類決策機制相似的AI算法嘗試對決策機制進行建模。

There have been a number of attempts to override unattended machine learning security solutions, resulting in a host of untested solutions to a variety of security problems. Many of these early attempts had difficulty generating enough data to effectively detect complex breaches such as identity fraud and advanced cyberattacks.

已經進行了許多嘗試來覆蓋無人看管的機器學習安全性解決方案，從而導致了許多未經測試的解決方案，可以解決各種安全問題。 這些早期嘗試中的許多嘗試都難以生成足夠的數據以有效檢測復雜的漏洞，例如身份欺詐和高級網絡攻擊。

By contrast, unsupervised machine learning is about finding and describing the hidden structures in the data. This problem is related to the problem of defining distance functions, since most, if not all, cluster algorithms are based on numerical and non-categorical data, and therefore we hear as much about cluster algorithms as we do about classification.

相比之下，無監督機器學習是關于發現和描述數據中的隱藏結構。 這個問題與定義距離函數的問題有關，因為大多數(即使不是全部)聚類算法都基于數值和非分類數據，因此，與聚類一樣，我們對聚類算法的了解也很多。

In the context of cybersecurity, AI tries to defend the system by weighing behavior patterns that indicate a threat to the systems. From this point of view, machine learning is the process of learning patterns that lead to malicious behavior.

在網絡安全的背景下，人工智能試圖通過權衡表明對系統構成威脅的行為模式來保護系統。 從這個角度來看，機器學習是導致惡意行為的學習模式的過程。

AI solutions are generally analyst-oriented and unsupervised machine learning-focused in information security. Using unsupervised machine learning to detect rare or abnormal patterns can increase the detection of new attacks. However, it can also trigger more false positives and warnings. This requires a significant amount of analysis effort to investigate the accuracy of these false positives. Such false alarms can cause alarm fatigue and insecurity and, over time, lead to its return to analytical-focused solutions and the resulting weaknesses. Three major challenges facing the information security industry, each of which can be addressed by machine learning solutions, have been identified as follows [2]:

人工智能解決方案通常面向分析師，面向信息安全的無監督機器學習。 使用無監督機器學習來檢測稀有或異常模式可以增加對新攻擊的檢測。 但是，它也可能觸發更多的誤報和警告。 這需要大量分析工作來調查這些誤報的準確性。 此類錯誤警報可能會導致警報疲勞和不安全感，并隨著時間的流逝，導致其返回到以分析為中心的解決方案，并因此而導致缺陷。 信息安全行業面臨的三個主要挑戰可以通過機器學習解決方案來解決，這些挑戰如下：[2]：

• Missing or Lack of Tagged Data: Many organizations lack the ability to use tagged examples and supervised learning models of previous attacks.
  標記數據的丟失或缺失：許多組織缺乏使用標記示例和監督先前攻擊的學習模型的能力。
• Continuously Evolving Attacks: Even though controlled learning models are possible, attackers can change their behavior and override them.
  不斷發展的攻擊：即使可以控制學習模型，攻擊者也可以更改其行為并覆蓋它們。
• Limited Time and Budget for Research or Investigation: Applying to analysts to investigate attacks is costly and time-consuming.
  研究或調查的時間和預算有限：向分析人員申請調查攻擊既昂貴又費時。

As the industry is still experimenting with the technology as a proof-of-concept, however, the idea of trust is ideal where the security solution is machine learning. It can help to improve the fight against cybercrime, and while AI can boost human efforts by automating the pattern-recognition process. Machine learning systems report useful data based on categories, while analysts talk openly about how machine learning can be a black box solution for security, where CISOs are not quite sure what is under the hood.

但是，由于業界仍在嘗試將該技術用作概念驗證，因此在安全解決方案是機器學習的情況下，信任的想法非常理想。 它可以幫助改善與網絡犯罪的斗爭，而人工智能可以通過使模式識別過程自動化來促進人類的努力。 機器學習系統會根據類別報告有用的數據，而分析師則公開談論機器學習如何成為安全性的黑匣子解決方案，而CISO對此不太確定。

Today, AI is not ready to replace humans, but by automating the pattern-recognition process, it can enhance human efforts. There is a truth here that cannot be denied because machine learning has very different uses in cyber defense.

如今，人工智能尚未準備好替代人類，但是通過使模式識別過程自動化，它可以增強人類的努力。 這里有一個不可否認的真相，因為機器學習在網絡防御中有非常不同的用途。

Considering all usage areas, it is possible to evaluate the use of AI in cyberspace in two categories; the use of artificial intelligence for cyber defense and the use of artificial intelligence for the cyber offense.

考慮到所有使用領域，有可能在兩類方面評估AI在網絡空間中的使用： 人工智能在網絡防御中的使用以及人工智能在網絡犯罪中的使用。

In part II, we will talk about the use of artificial intelligence for cyber defense…

在第二部分中，我們將討論如何將人工智能用于網絡防御……

Sources

資料來源

[1] K.R. Chowdhary, “Fundamentals of Artificial Intelligence,” Springer India, 2020.

[1] KR Chowdhary，“人工智能基礎”，印度Springer，2020年。

[2] K. Veeramachaneni, I. Arnaldo, A. Cuesta-Infante, V. Korrapati, C. Bassias, K. Li, “AI2: Training a Big Data Machine to Defend”, IEEE International Conference on Big Data Security in New York City, 2016.

[2] K. Veeramachaneni，I。Arnaldo，A。Cuesta-Infante，V。Korrapati，C。Bassias，K。Li，“ AI2：培訓大數據機以捍衛”，IEEE國際大數據安全新會議紐約，2016。

翻譯自: https://towardsdatascience.com/is-the-future-of-cyber-security-in-the-hands-of-artificial-intelligence-ai-1-2b4bd8384329

ai人工智能的本質和未來

總結

以上是生活随笔為你收集整理的ai人工智能的本质和未来_是人工智能手中的网络安全的未来AI 1的全部內容，希望文章能夠幫你解決所遇到的問題。

如果覺得生活随笔網站內容還不錯，歡迎將生活随笔推薦給好友。