Processed_ADFA_LD dataset

The Processed_ADFA_LD dataset is a preprocessed version of the ADFA Linux Dataset (ADFA-LD), which was originally developed by the Australian Defence Force Academy (ADFA) for research in host-based intrusion detection systems (HIDS). The dataset focuses on detecting malicious behavior at the operating system level by analyzing system call traces generated by Linux processes. Original ADFA-LD Dataset Overview The original ADFA-LD dataset contains system call sequences collected from a Linux environment under two main conditions: Normal behavior: Legitimate system activities generated by standard user operations. Attack behavior: System call traces produced during various cyber-attacks, including privilege escalation, denial-of-service, and remote exploits. Unlike older datasets (e.g., KDD Cup 99), ADFA-LD was designed to reflect modern Linux systems and more realistic attack scenarios. Processing and Transformation The Processed_ADFA_LD dataset refers to the cleaned and transformed version of the original dataset to make it suitable for machine learning and deep learning models. Common preprocessing steps include: System call encoding (e.g., integer mapping or frequency-based encoding) Sequence normalization or padding to handle variable-length traces Feature extraction, such as: n-grams of system calls statistical features (frequency, entropy, transition probabilities) Labeling, typically: 0 → Normal behavior 1 → Attack behavior Train-test splitting for supervised learning experiments Data Characteristics Data type: Sequential / time-series data Features: Encoded system call sequences or derived statistical features Labels: Binary (Normal vs. Attack) or multiclass (depending on processing) Domain: Cybersecurity, Host-Based Intrusion Detection Operating System: Linux Applications The Processed_ADFA_LD dataset is widely used for: Intrusion detection system (IDS) evaluation Anomaly detection research Benchmarking machine learning and deep learning models such as: LSTM / GRU CNN-based sequence models Autoencoders Traditional classifiers (SVM, Random Forest, k-NN) Advantages Reflects realistic modern attack behavior Avoids outdated network-focused features Well-suited for sequence-based learning models Limitations Host-specific (Linux-only) Limited attack diversity compared to large-scale enterprise datasets Requires careful preprocessing due to variable-length sequencesProcessed_ADFA_LD 数据集是ADFA Linux数据集（ADFA-LD）的预处理版本，该数据集最初由澳大利亚国防学院（Australian Defence Force Academy, ADFA）研发，用于基于主机的入侵检测系统（Host-based Intrusion Detection Systems, HIDS）领域的研究。本数据集通过分析Linux进程生成的系统调用追踪数据，聚焦于操作系统层面的恶意行为检测。 原始ADFA-LD数据集概览 原始ADFA-LD数据集包含在两类典型场景下从Linux环境中采集的系统调用序列： 正常行为：标准用户操作产生的合法系统活动。 攻击行为：各类网络攻击过程中生成的系统调用追踪数据，涵盖权限提升、拒绝服务、远程漏洞利用等攻击类型。 与KDD Cup 99等老旧数据集不同，ADFA-LD旨在贴合现代Linux系统与更具真实性的攻击场景。 预处理与转换 Processed_ADFA_LD 数据集是原始数据集经清洗与转换后的版本，可适配机器学习与深度学习模型的训练需求。常用预处理步骤包括： 系统调用编码（例如整数映射或基于频率的编码方式） 序列归一化与填充操作，以处理长度不均的追踪数据 特征提取，例如： 系统调用的n元语法（n-grams） 统计特征（频率、熵、转移概率） 标签标注规则通常为： 0 → 正常行为 1 → 攻击行为 用于监督学习实验的训练集与测试集划分 数据特征 数据类型：序列/时序数据 特征：编码后的系统调用序列或衍生统计特征 标签：二分类（正常 vs 攻击）或多分类（具体分类方式取决于预处理方案） 应用领域：网络安全、基于主机的入侵检测 适用操作系统：Linux 应用场景 Processed_ADFA_LD 数据集被广泛应用于： 入侵检测系统（Intrusion Detection System, IDS）评估 异常检测研究 机器学习与深度学习模型的基准测试，例如： 长短期记忆网络（Long Short-Term Memory, LSTM）/门控循环单元（Gated Recurrent Unit, GRU） 基于卷积神经网络（Convolutional Neural Network, CNN）的序列模型 自编码器（Autoencoders） 传统分类器（支持向量机（Support Vector Machine, SVM）、随机森林、k近邻（k-Nearest Neighbor, k-NN）） 优势 贴合现实中的现代攻击行为 摒弃了过时的网络聚焦型特征 适配基于序列的学习模型 局限性 主机专属（仅适用于Linux系统） 与大规模企业级数据集相比，攻击多样性有限 由于序列长度不均，需进行精细化预处理