Snort Intrusion Detection and Prevention Toolkit

本书涵盖Snort的包检查及从入侵检测到预防的技术进展。解析多种包检查方法,并深入探讨应用层面的安全漏洞,包括HTTP隧道、跨站脚本等。此外,还介绍了Snort的安装配置及性能优化策略。

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版权声明:原创作品,允许转载,转载时请务必以超链接形式标明文章原始出版、作者信息和本声明。否则将追究法律责任。 http://blog.youkuaiyun.com/topmvp - topmvp
The accompanying CD contains examples from real attacks allowing readers test their new skills. The book will begin with a discussion of packet inspection and the progression from intrusion detection to intrusion prevention. The authors provide examples of packet inspection methods including: protocol standards compliance, protocol anomaly detection, application control, and signature matching. In addition, application-level vulnerabilities including Binary Code in HTTP headers, HTTP/HTTPS Tunneling, URL Directory Traversal, Cross-Site Scripting, and SQL Injection will also be analyzed. Next, a brief chapter on installing and configuring Snort will highlight various methods for fine tuning your installation to optimize Snort performance including hardware/OS selection, finding and eliminating bottlenecks, and benchmarking and testing your deployment. A special chapter also details how to use Barnyard to improve the overall performance of Snort. Next, best practices will be presented allowing readers to enhance the performance of Snort for even the largest and most complex networks. The next chapter reveals the inner workings of Snort by analyzing the source code. The next several chapters will detail how to write, modify, and fine-tune basic to advanced rules and pre-processors. Detailed analysis of real packet captures will be provided both in the book and the accompanying CD. Several examples for optimizing output plugins will then be discussed including a comparison of MySQL and PostrgreSQL. Best practices for monitoring Snort sensors and analyzing intrusion data follow with examples of real world attacks using: ACID, BASE, SGUIL, SnortSnarf, Snort_stat.pl, Swatch, and more.
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内容概要:本文介绍了基于SMA-BP黏菌优化算法优化反向传播神经网络(BP)进行多变量回归预测的项目实例。项目旨在通过SMA优化BP神经网络的权重和阈值,解决BP神经网络易陷入局部最优、收敛速度慢及参数调优困难等问题。SMA算法模拟黏菌寻找食物的行为,具备优秀的全局搜索能力,能有效提高模型的预测准确性和训练效率。项目涵盖了数据预处理、模型设计、算法实现、性能验证等环节,适用于多变量非线性数据的建模和预测。; 适合人群:具备一定机器学习基础,特别是对神经网络和优化算法有一定了解的研发人员、数据科学家和研究人员。; 使用场景及目标:① 提升多变量回归模型的预测准确性,特别是在工业过程控制、金融风险管理等领域;② 加速神经网络训练过程,减少迭代次数和训练时间;③ 提高模型的稳定性和泛化能力,确保模型在不同数据集上均能保持良好表现;④ 推动智能优化算法与深度学习的融合创新,促进多领域复杂数据分析能力的提升。; 其他说明:项目采用Python实现,包含详细的代码示例和注释,便于理解和二次开发。模型架构由数据预处理模块、基于SMA优化的BP神经网络训练模块以及模型预测与评估模块组成,各模块接口清晰,便于扩展和维护。此外,项目还提供了多种评价指标和可视化分析方法,确保实验结果科学可信。
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