IDM design and implementation remain a difficult undertaking and an unsolved research topic. Multi-dimensional irrelevant characteristics and duplicate information are included in the network dataset. To boost the effectiveness of IDM, a novel hybrid model is developed that combines Fuzzy Genetic Algorithms with Increment Import Vector Machines (FGA-I2VM), which works with huge amounts of both normal and aberrant network data with high detecting accuracy and low false alarm rates. The algorithms chosen for IDM in this stage are machine learning algorithms, which learn, find, and adapt patterns to changing situations over time. Pre-processing is the most essential stage in any IDM, and feature selection is utilized for pre-processing, which is the act of picking a collection or subset of relevant features for the purpose of creating a solution model. Information Gain (IG) is utilized in this FGA-I2VM model to pick features from the dataset for I2VM classification. To train the I2VM classifier, FGA uses three sets of operations to produce a new set of inhabitants with distinct patterns: cross over operation, selection, and finally mutation. The new population is then put into the Import Vector Machine, a strong classifier that has been used to solve a wide range of pattern recognition issues. FGA are quick, especially considering their capacity to discover global optima. Another advantage of FGA is their naturally parallel nature of assessing the individuals within a population. As a classifier, I2VM has self-tuning properties that allow patterns to attain global optimums. The FGA-efficacy I2VM model’s is complemented by information gain, which improves speed and detection accuracy while having a low computing cost

中文翻译：

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模糊与增量导入向量机的集成入侵检测

IDM 设计和实施仍然是一项艰巨的任务和未解决的研究课题。网络数据集中包含多维无关特征和重复信息。为了提高 IDM 的有效性，开发了一种将模糊遗传算法与增量导入向量机 (FGA-I2VM) 相结合的新型混合模型，该模型可处理大量正常和异常网络数据，具有高检测精度和低误报率. 在这个阶段为 IDM 选择的算法是机器学习算法，它们学习、发现和调整模式以适应随着时间变化的情况。预处理是任何IDM中最重要的阶段，特征选择用于预处理，这是为了创建解决方案模型而选择相关特征的集合或子集的行为。在这个 FGA-I2VM 模型中使用信息增益 (IG) 从数据集中挑选特征用于 I2VM 分类。为了训练 I2VM 分类器，FGA 使用三组操作来产生一组具有不同模式的新居民：交叉操作、选择和最后的变异。然后将新种群放入导入向量机中，这是一种强大的分类器，已用于解决广泛的模式识别问题。FGA 很快，特别是考虑到它们发现全局最优值的能力。FGA 的另一个优点是它们在评估群体中的个体时具有天然的平行性质。作为分类器，I2VM 具有自调整特性，允许模式达到全局最优。