商品描述
Real World AI in Cybersecurity provides hands on examples of using AI and machine learning to improve cybersecurity in systems of all sizes. It includes step-by-step guidance for using AI applications in system administration and cybersecurity. The more complex and frequent that cybersecurity attacks and data breaches become, the more cybersecurity experts will need to master tools including AI to help them spot the dangerous attacks and mitigate them.
The reader will learn to:
The reader will learn to:
- Overcome antivirus limits in threat detection, classify suspicious user activity, and use fraud detection algorithms
- Use application performance monitoring (APM) tools and improve spam detection with advanced filtering techniques
- Pick the right Python libraries for AI
- Categorize advanced persistent threats (APT), zero-days, and malware samples
- Use python tools to turn logs into datasets for analysis, predict network intrusions, and spot fake logins and fake accounts
- Apply algorithms from AI for cybersecurity including decision trees, Bayesian classification, least squares regression and more
- Use Jupyter notebooks and the key tools including MLBase.jl, cikitLearn.jl, MachineLearning.jl and Mocha.jl
- Test data using AI to assess incident response
商品描述(中文翻譯)
《實務中的人工智慧在網路安全》提供了使用人工智慧和機器學習來改善各種規模系統網路安全的實作範例。書中包含了逐步指導，教導如何在系統管理和網路安全中使用人工智慧應用。隨著網路安全攻擊和資料洩漏變得越來越複雜和頻繁，網路安全專家將需要掌握包括人工智慧在內的工具，以幫助他們識別危險攻擊並減輕其影響。
讀者將學習到：
- 克服防毒軟體在威脅檢測上的限制，分類可疑的用戶活動，並使用詐騙檢測演算法
- 使用應用程式性能監控（APM）工具，並透過先進的過濾技術改善垃圾郵件檢測
- 選擇適合人工智慧的 Python 函式庫
- 對進階持續威脅（APT）、零日漏洞和惡意軟體樣本進行分類
- 使用 Python 工具將日誌轉換為可供分析的數據集，預測網路入侵，並識別假登錄和假帳戶
- 應用人工智慧在網路安全中的演算法，包括決策樹、貝葉斯分類、最小二乘回歸等
- 使用 Jupyter notebooks 及關鍵工具，包括 MLBase.jl、cikitLearn.jl、MachineLearning.jl 和 Mocha.jl
- 使用人工智慧測試數據以評估事件響應
