### 数学代写|理论计算机代写theoretical computer science代考| Evaluation

statistics-lab™ 为您的留学生涯保驾护航 在代写理论计算机theoretical computer science方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写理论计算机theoretical computer science代写方面经验极为丰富，各种代写理论计算机theoretical computer science相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 数学代写|理论计算机代写theoretical computer science代考|Dataset

To evaluate the proposed framework, we have fully implemented it and have run multiple experiments. We evaluate our method for two classification tasks: traffic characterization and application identification. Traffic characterization deals with determining the application category of a trace such as Chat, and the goal of application identification is to distinguish the application of a trace such as Skype. To evaluate the performance of the proposed framework, we have used Precision (Pr), Recall (Rc), and F1-Measure (F1). The mathematical formula for these metrics is provided in Eq. 4 .
$$\text { Recall }=\frac{T P}{T P+F N}, \text { Precision }=\frac{T P}{T P+F P}, F 1=\frac{2 * R c * P r}{R c+P r}$$

We have fully implemented the framework with Python 3. After filtering out the unnecessary packets, 5 -tuple which identifies each network flow along with timestamp and length are extracted for each packet of packet trace. Statistical features provided in Table 1 are extracted by Python Pandas libraries ${ }^{2}$ in the Network Unit Extraction module (Sect. 4.1). Kmeanst+ algorithm is used for the clustering purpose from the scikit-learn library ${ }^{3}$ and StandardScaler function is used for standardizing the feature sets from the same package in the Network Unit Clustering module (Sect. 4.1). To automate identifying the number of clusters of each protocol, we have leveraged KneeLocator from the Kneed library ${ }^{4}$. For implementing the Language Learner module (Sect. 4.2) we benefited from k-TSS language learner part of [11] implementation and modified it based on our purpose. Finally, we have used the grid search method to choose the best value for the framework’s hyper-parameters, including $s t, i t, f d$, stats, and $k$.

## 数学代写|理论计算机代写theoretical computer science代考|Classification Results

We have used a grid search to fine-tune the parameters $s t, f d$, it, stats, and $k$, called hyper-parameters in machine learning. Based on the average F1-Measure, consequently, the best values for the application identification task are $15 \mathrm{~s}, 5 \mathrm{~s}$, $10 \mathrm{~s}, 2$, and 3 for the hyper-parameters $s t, f d$, it, stats, and $k$, respectively, while for the traffic characterization task, they are $15 \mathrm{~s}, 15 \mathrm{~s}, 10 \mathrm{~s}, 2$, and 3 . Using the elbow method, the number of clusters for the most used protocol such as TCP is automatically set to 23 and for a less used protocol such as HTTP is set to six, resulting in 101 total number of clusters.

We have evaluated the framework on the test set with the chosen hyperparameters. The framework has gained weighted average F1-Measure of $97 \%$ for both tasks. Table 2 provides the proposed framework performance in terms of Precision, Recall, and F1-Measure in detail. In this section, we provide a comparison of our framework with other flow-based methods used for network traffic classification. For the application identification task, we have compared our work with [2]. In this comparison, we first have extracted 44 most used statistical flow-based features from the dataset, as the 111 initial features are not publicly available. Then, we have converted these instances to arff format to be used as Weka ${ }^{5}$ input. To have a fair comparison, we have used the ChiSquaredAttributeEval evaluator for feature selection and finally, applied all machine learning algorithms, including J48, Random Forest, $\mathrm{k}-\mathrm{NN}(\mathrm{k}=1)$, and Bayes Net with 10 fold cross-validation used in this work. Figure 4 a compares the proposed framework performance with [2] in terms of Precision, Recall, and F1-Measure for application identification task. In most

classes, our framework performed considerably better. Our framework gains much higher precision in identification of different applications in our dataset except for TeamViewer and JoinMe. In terms of recall, the work of [2] performed slightly better in detecting JoinMe and Ultra.

For the traffic characterization task, we made a comparison with the work of [1]. We have implemented a python code to extract time-related statistical flow-based features used by the authors. Using Weka, we have applied KNN and C4.5 in a 10-fold cross-validation setting which is the same as the one used in the paper. Figure $4 \mathrm{~b}$ compares the proposed framework performance with these

machine learning algorithms in terms of Precision, Recall, and F1-Measure for the traffic characterization task, showing that our work significantly outperforms the state-of-the-art work in application identification task.

As the proposed method in [7] is the only framework that has leveraged automata learning methods for application identification, we compare our work with it in terms of performance and time complexity (Table 3). Although the performance of [7] was computed in a two-class classification setting, NeTLang almost outperforms it in terms of Recall and F1-Measure. However, [7] has achieved higher overall precision because of its automata learning nature but owns time-consuming training and testing processes, while NeTLang considerably reduces the training time to the scale of minutes and it identifies application by observing only $4 k$ of a network trace.

## 数学代写|理论计算机代写theoretical computer science代考|Related Work

Machine Learning Based Methods in Traffic Classification. Many researchers applied the machine learning techniques in traffic classification. Time-related statistical flow-based features are used in [1] to characterize traffic in the presence/absence of a virtual private network (VPN). In this work, C4.5 decision tree and K-Nearest Neighbors are applied to the extracted features to classify flows. According to their result, the C4.5 algorithm performed better with the precision of $90 \%$ for traffic characterization.

Application identification from network flows is another traffic classification task performed in [2]. They used the CfsSubsetEval and ChiSquaredAttributeEval methods in Weka to optimize the number of features and supervised learning algorithms, including J48, Random Forest, k-NN, and Bayes Net. For the dataset they used, Random Forest and $\mathrm{k}$-NN had better performance in terms of accuracy $(90.87 \%$ and $93.94 \%$, respectively).

Deep Packet [3] is a recently proposed packet-level deep-learning-based framework that automatically extracts features for traffic classification. They used stacked autoencoders and one-dimensional CNN in their framework. This framework achieved a recall of $94 \%$ and $98 \%$ for traffic characterization and application identification tasks, respectively. The drawback of the deep-learningbased method is their time-consuming training process.

Automata Learning Based Methods in Traffic Classification. The most related work to ours is [7] in terms of utilizing a passive automata learning technique to derive the behavioral packet-level network models of applications. They provided a multi-steps algorithm consists of building an initial automaton and generalizing it by a state merging condition based on the behavior of well-known network protocols. Their fine granularity leads to produce large models, which make some steps of generalizing fulfill slowly for large models. Furthermore, the detection of an application was constrained to observing a complete trace of an application. Our method rectifies these shortcomings by the idea of learning a k-TSS language, which was inspired by [11]. Botnet detection has been studied in [8]. They passively learned automata for clusters of the dataset to characterize a communication profile. Other related work mainly use the automata learning for protocol modeling such as $[9,10]$ by active automata learning, while using the desired protocol implementation as their oracle system and alphabet of the automaton derived manually by an expert.

## 数学代写|理论计算机代写theoretical computer science代考|Dataset

记起 =吨磷吨磷+Fñ, 精确 =吨磷吨磷+F磷,F1=2∗RC∗磷rRC+磷r

## 数学代写|理论计算机代写theoretical computer science代考|Related Work

Deep Packet [3] 是最近提出的基于数据包级深度学习的框架，可自动提取特征进行流量分类。他们在他们的框架中使用了堆叠的自动编码器和一维 CNN。该框架实现了召回94%和98%分别用于流量表征和应用识别任务。基于深度学习的方法的缺点是训练过程耗时。

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