《娜璋带你读论文》系列主要是督促自己阅读优秀论文及听取学术讲座，并分享给大家，希望您喜欢。由于作者的英文水平和学术能力不高，需要不断提升，所以还请大家批评指正，非常欢迎大家给我留言评论，学术路上期待与您前行，加油。
前一篇从个人角度介绍英文论文模型设计（Model Design）如何撰写。这篇文章将从个人角度介绍英文论文实验评估（Evaluation）部分，即Experimental Evaluation或Experimental study，主要以入侵检测系统为例（Intrusion Detection System），详细的对比分析下篇介绍。一方面自己英文太差，只能通过最土的办法慢慢提升，另一方面是自己的个人学习笔记，并分享出来希望大家批评和指正。希望这篇文章对您有所帮助，这些大佬是真的值得我们去学习，献上小弟的膝盖~fighting！
这里选择的论文多数为近三年的CCF A和二区以上为主，尤其是顶会顶刊。当然，作者能力有限，只能结合自己的实力和实际阅读情况出发，也希望自己能不断进步，每个部分都会持续补充。可能五年十年后，也会详细分享一篇英文论文如何撰写，目前主要以学习和笔记为主。大佬还请飘过O(∩_∩)O
前文赏析：
论文如何撰写因人而异，作者仅分享自己的观点，欢迎大家提出意见。然而，坚持阅读所研究领域最新和经典论文，这个大家应该会赞成，如果能做到相关领域文献如数家珍，就离你撰写第一篇英文论文更近一步了。
在实验设计中，重点是如何通过实验说服审稿老师，赞同你的创新点，体现你论文的价值。好的图表能更好地表达你论文的idea，因此我们需要学习优秀论文，一个惊喜的实验更是论文成功的关键。注意，安全论文已经不再是对比PRF的阶段了，一定要让实验支撑你整个论文的框架。同时，多读多写是基操，共勉！
该部分回顾和参考周老师的博士课程内容，感谢老师的分享。典型的论文框架包括两种（The typical “anatomy” of a paper），如下所示：
第一种格式：理论研究
第二种格式：系统研究
实验评估介绍（Evaluation）
该部分主要是学习易莉老师书籍《学术写作原来是这样》，后面我也会分享我的想法，具体如下：
结果与方法一种是相对容易写作的部分，其内容其实就是你对收集来的数据做了什么样的分析。对于相对简单的结果（3个分析以内），按部就班地写就好。有专业文献的积累，相信难度不大。写起来比较困难的是复杂数据的结果，比如包括10个分析，图片就有七八张。这时候对结果的组织就非常重要了。老师推荐《10条简单规则》一文中推荐的 结论驱动（conclusion-driven） 方法。
个人感觉：
实验部分同样重要，但更重要是如何通过实验结果、对比实验、图表描述来支撑你的创新点，让审稿老师觉得，就应该这么做，amazing的工作。作为初学者，我们可能还不能做到非常完美的实验，但一定要让文章的实验足够详细，力争像该领域的顶级期刊或会议一样，并且能够很好的和论文主题相契合，这有这有，文章的价值也体现出来了。
在数据处理的过程中，梳理、总结自己的主要发现，以这些发现为大纲（小标题），来组织结果的写作（而不是传统上按照自己数据处理的顺序来组织）。以作者发表论文为例，他们使用了这种方法来组织结果部分，分为四个小标题，每个小标题下列出相应的分析及结果。
如果还有其他结果不能归入任何一个结论，那就说明这个结果并不重要，没有对形成文章的结论做出什么贡献，这时候果断舍弃（或放到补充材料中）是明智的选择。
另外，同一种结果可能有不同的呈现方式，可以依据你的研究目的来采用不同的方式。我在修改学生文章时遇到比较多的一个问题是采用奇怪的方式，突出了不重要的结果。举例：
示例句子：
The two groups were similar at the 2nd, 4th, and 8th trials; They differed from each other in the remaining trials.
这句话有两个比较明显的问题：(1)相似的试次并不是重点，重点是大部分的试次是有差异的，但是这个重点没有被突出，反而仅有的三个一样的试次突出了。(2)语言的模糊，differ的使用来来的模糊性（不知道是更好还是更差）。
修改如下：
Four-year-olds outperformed 3-year-olds in most trials, except the 2nd, 4th, and 8th trials, in which they performed similarly.
对于结果的呈现，作图是特别重要的，一张好图胜过千言万语。 但我不是作图方面的专家，如果你需要这方面的指导，建议你阅读《10个简单规则，创造更优图形》，文中为怎么做出一张好图提供了非常全面而有用的指导。
该部分主要是学习易莉老师书籍《学术写作原来是这样》，后面我也会分享我的想法，具体如下：
讨论是一个非常头疼的部分。先来讲讲讨论的写法，在前面强调了从大纲开始写的好处，从大纲开始写是一种自上而下的写法，在写大纲的过程中确定主题句，然后再确定其他内容。还有一种方法是自下而上地写，就是先随心所以地写第一稿，从笔记开始写，然后对这些笔记进行梳理和归纳，提炼主题句。老师通常混合两种写法，先从零星的点进行归纳（写前言时对文献观点做笔记，写讨论时对结果的发现做笔记），之后通过梳理，整理出大纲，再从大纲开始写作。
比如我对某篇文章的讨论部分做过相关笔记，然后对这些点进行梳理和归纳，再结合前沿提出来的三个研究问题形成讨论的大纲，如下：
在(1)到(4)段的讨论中，要先总结自己最重要的发现，不要忘记回顾前言中提出的实验预期，说明结果是否符合自己的预期。然后回顾前人研究与自己的研究发现是否一致，如果不一致，就可以讨论可能的原因（取样、实验方法的不同等）。
此外还需要注意，很多学生把讨论的重点放在了与前人研究不一致的结果和自己的局限性上，这些是需要写的，但是最重要的是突出自己研究的贡献。
讨论中最常出现的问题就是把结果里的话换个说法再说一遍。其实讨论部分给了我们一个从更高层面梳理和解读研究结果的机会。更重要的是，需要明确提出自己的研究贡献，进一步强调研究的重要性、意义以及创新性。因此，不要停留在就事论事的结果描述上。读者读完结果后，很容易产生“so what”的问题——“是的，你发现了这些，那又怎么样呢？”。
这时候，最重要的是告诉读者研究的启示（implication）——你的发现说明了什么，加深了对什么问题的理解，对未解决的问题提供了什么新的解决方法，揭示了什么新的机制。这也是影响稿件录用的最重要部分，所以一定要花最多时间和精力来写这个部分。
用前文提到的“机器人”文章的结论作为例子，说明如何总结和升华自己的结论。
Overall, our study contributes several promising preliminary findings on the potential involvement of humanoid robots in social rules training for children with ASD. Our results also shed light for the direction of future research, which should address whether social learning from robots can be generalized to a universal case (e.g., whether distrusting/deceiving the robot contributes to an equivalent effect on distrusting/deceiving a real person); a validation test would be required in future work to test whether children with ASD who manage to distrust and deceive a robot are capable of doing the same to a real person.
首先我们要清楚实验写作的目的，通过详细准确的数据集、环境、实验描述，仿佛能让别人模仿出整个实验的过程，更让读者或审稿老师信服研究方法的科学性，增加结果数据的准确性和有效性。
如果我们的实验能发现某些有趣的结论会非常棒；如果我们的论文就是新问题并有对应的解决方法（创新性强），则实验需要支撑对应的贡献或系统，说服审稿老师；如果上述都不能实现，我们尽量保证实验详细，并通过对比实验（baseline对比）来巩固我们的观点和方法。
切勿只是简单地对准确率、召回率比较，每个实验结果都应该结合研究背景和论文主旨进行说明，有开源数据集的更好，没有的数据集建议开源，重要的是说服审稿老师认可你的工作。同时，实验步骤的描述也非常重要，包括实验的图表、研究结论、简明扼要的描述（给出精读）等。
在时态方面，由于是描述已经发生的实验过程，一般用过去时态，也有现在时。大部分期刊建议用被动句描述实验过程，但是也有一些期刊鼓励用主动句，因此，在投稿前，可以在期刊主页上查看“Instructions to Authors”等投稿指导性文档来明确要求。一起加油喔~
下面结合周老师的博士英语课程，总结实验部分我们应该怎么表达。
图/表的十个关键点(10 key points)
说明部分要尽量把相应图表的内容表达清楚
图的说明一般在图的下边；表的说明一般在表的上边；表示整体数据的分布趋势的图不需太大；表示不同方法间细微差别的图不能太小。
几个图并排放在一起，如果有可比性，并排图的x/y轴的取值范围最好一致，利于比
较。
实验结果跟baseline在绝对数值上差别不大，用列表加黑体字；实验结果跟baseline在绝对数值上差别较大，用柱状图/折线图视觉表现力更好。
折线图要选择适当的颜色和图标，颜色选择要考虑黑白打印的效果；折线图的图标选择要有针对性，比如对比A, A+，B, B+四种方法。
同时，模型设计整体结构和写作细节补充几点：（引用周老师博士课程，受益匪浅）
该部分在实验评估环节主要作为引入，通常是介绍实验模块由哪几部分组成。同时，有些论文会直接给出实验的各个小标题，这时会省略该部分。
In this section, we employ four datasets and experimentally evaluate four aspects of WATSON: 1) the explicability of inferred event semantics; 2) the accuracy of behavior abstraction; 3) the overall experience and manual workload reduction in attack investigation; and 4) the performance overhead.
In this section, we prototype Whisper and evaluate its performance by using 42 real-world attacks. In particular, the experiments will answer the three questions:
We first describe the testbed and data sets we use in the experiment. Then we evaluate the system by comparing it with other classical intrusion detection systems on a series of critical axes such as detection rate, false alarm rate, detection time, query time and storage overhead.
In this section, we evaluate our approach with the following major goals:
In this section, we start analyzing the MUD profile of real consumer IoT devices that we have generated, and highlight attack types that can be prevented. Then, we will use traces collected in our lab, when we launched a number of volumetric attacks to four of IoT devices, to show how our system can detect these attacks using off-the-shelf IDS in an operational environment.
In this section, we present the implementation of BiDLSTM and discuss the experimental findings. We compare the model’s performance with state-of-the-art methods trained and tested on the same dataset (i.e., the NSL-KDD dataset). Also, we present a comparison of results with some recently published methods on the NSL-KDD dataset.
In this section, we performed two major experiments (named Experiment1 and Experiment2) to explore the performance of disagreement-based semi-supervised learning and our DAS-CIDS in the aspects of detection performance and alarm filtration. In this work, we use the WEKA platform (WEKA) to help extract various classifiers like J48 and Random Forest to avoid implementation variations, which is an open-source software providing a set of machine learning algorithms.
In this experimental study, we exhibit the impact of the proposed methodology and select the informative features subset from the given intrusion dataset, that can classify the network traffics into normal or attacks for the intrusion detection. Two diagnostic studies were conducted to verify the impact of the proposed method, such as precision-recall analysis and ROC-AUC analysis, which is helpful in the analysis of probabilistic prediction for binary and multi-class classification problems. The main objectives of these experiments are summarized below,
该部分主要介绍实验数据集，通常包括数据集的组成及特征分布情况，结合表格描述效果更好。同时，如果有公用数据集（AI类较多），建议多个数据集对比，并且与经典的论文方法或baselines比较；如果是自身数据集，建议开源，但其对比实验较难，怎么说服审稿人相信你的数据集是关键。
(1) Chuanpu Fu, et al. Realtime Robust Malicious Traffic Detection via Frequency Domain Analysis. CCS.
Datasets. The datasets used in our experiments are shown in Table 4. We use three recent datasets from the WIDE MAWI Gigabit backbone network [69]. In the training phase, we use 20% benign traffic to train the machine learning algorithms. We use the first 20% packets in MAWI 2020.06.10 dataset to calculate the encoding vector via solving the SMT problem (see Section 4.2). Meanwhile, we replay four groups of malicious traffic combined with the benign traffic on the testbed:
(2) Ning Wang, et al. MANDA: On Adversarial Example Detection for Network Intrusion Detection System. INFOCOM.
NSL-KDD: We use the internet traffic dataset, NSL-KDD [45] (also used in AE attacks in IDS [9], but [9] dose not consider problem-space validity), for our evaluation. In NSL-KDD, each sample contains four groups of entries including Intrinsic Characteristics, Content Characteristics, Time-based Characteristics, and Host-based Characteristics. There are four categories of intrusion: DoS, Probing, Remote-to-Local (R2L), and User-to-Root (U2R) of which each contains more attack sub-categories. There are 24 sub-categories of attacks in the training set and 38 sub-categories of attacks are in test set (i.e., 14 sub-categories of attacks are unseen in the training set). There are 125,973 training records and 22,544 testing records. In our experiments, we only show the evaluations on an IDS model for discriminating DoS attacks from normal traffic since the results for the other three attacks are similar. The total number of entries for each record is 41 (in problem-space) which are further processed into 121 numerical features as an input-space (feature-space) vector.
MNIST: We also evaluate our approach on an image dataset, MNIST [46], to demonstrate its applicability. The images in MNIST are handwritten digits from 0 to 9. The corresponding digit of an image is used as its label. Each class has 6,000 training samples and 1,000 test samples. Therefore, the whole MNIST dataset has 60,000 training samples and 10,000 test samples. All the images have the same size of 28 × 28 and are in grey-level.
(3) Mohammed A. Ambusaidi, et al. Building an Intrusion Detection System Using a Filter-Based Feature Selection Algorithm. IEEE TRANSACTIONS ON COMPUTERS.
Currently, there are only a few public datasets available for intrusion detection evaluation. Among these datasets, the KDD Cup 99 dataset, NSL-KDD dataset and Kyoto 2006+ dataset have been commonly used in the literature to assess the performance of IDSes. According to the review by Tsai et al. [43], the majority of the IDS experiments were performed on the KDD Cup 99 datasets. In addition, these datasets have different data sizes and various numbers of features which provide comprehensive tests in validating feature selection methods. Therefore, in order to facilitate a fair and rational comparison with other state-of-the-art detection approaches, we have selected these three datasets to evaluate the performance of our detection system.
The KDD Cup 99 dataset is one of the most popular and comprehensive intrusion detection datasets and is widely applied to evaluate the performance of intrusion detection systems [43]. It consists of five different classes, which are normal and four types of attack (i.e., DoS, Probe, U2R and R2L). It contains training data with approximately five million connection records and test data with about two million connection records. Each record in these datasets is labeled as either normal or an attack, and it has 41 different quantitative and qualitative features.
The NSL-KDD is a new revised version of the KDD Cup 99 that has been proposed by Tavallaee et al. in [24]. This dataset addresses some problems included in the KDD Cup 99 dataset such as a huge number of redundant records in KDD Cup 99 data. As in the case of the KDD Cup 99 dataset, each record in the NSL-KDD dataset is composed of 41 different quantitative and qualitative features.
(4) Jun Zeng, et al. WATSON: Abstracting Behaviors from Audit Logs via Aggregation of Contextual Semantics. NDSS.
We evaluate WATSON on four datasets: a benign dataset, a malicious dataset, a background dataset, and the DARPA TRACE dataset. The first three datasets are collected from ssh sessions on five enterprise servers running Ubuntu 16.04 (64-bit). The last dataset is collected on a network of hosts running Ubuntu 14.04 (64-bit). The audit log source is Linux Audit [9].
In the benign dataset, four users independently complete seven daily tasks, as described in Table I. Each user performs a task 150 times in 150 sessions. In total, we collect 17 (expected to be 4×7 = 28) classes of benign behaviors because different users may conduct the same operations to accomplish tasks. Note that there are user-specific artifacts, like launched commands, between each time the task is performed. For our benign dataset, there are 55,296,982 audit events, which make up 4,200 benign sessions.
In the malicious dataset, following the procedure found in previous works [2], [10], [30], [53], [57], [82], we simulate3 eight attacks from real-world scenarios as shown in Table II. Each attack is carefully performed ten times by two security engineers on the enterprise servers. In order to incorporate the impact of typical noisy enterprise environments [53], [57], we continuously execute extensive ordinary user behaviors and underlying system activities in parallel to the attacks. For our malicious dataset, there are 37,229,686 audit events, which make up 80 malicious sessions.
In the background dataset, we record behaviors of developers and administrators on the servers for two weeks. To ensure the correctness of evaluation, we manually analyze these sessions and only incorporate sessions without behaviors in Table I and Table II into the dataset. For our background dataset, there are 183,336,624 audit events, which make up 1,000 background sessions.
…
In general, our experimental behaviors for abstraction are comprehensive as compared to behaviors in real-world systems. Particularly, the benign behaviors are designed based upon basic system activities [84] claimed to have drawn attention in cybersecurity study; the malicious behaviors are either selected from typical attack scenarios in previous work or generated by a red team with expertise in instrumenting and collecting data for attack investigation.
(5) S. Krishnaveni, et al. Efficient feature selection and classification through ensemble method for network intrusion detection on cloud computing. Cluster Computing.
The datasets applied in this proposed work are the following: (1) Real-time Honeypot Dataset (2) Kyoto 2006+ Dataset and (3) NSL-KDD.
Real-time honeypot dataset. In this research work, honeypots were set up on the AWS public cloud. The real-time data was collected during the period August 19th, 2018 to September 19th, 2018. And then, log data was collected for further analysis, resulting in over 5,195,499 attacker’s log entries. The proposed Honeynet system demonstrates the system configuration of container-based honeypots that can investigate and discover the attacks on a cloud system [27].
NSL-KDD dataset. The KDD Cup99 dataset is a popular dataset used for network-based intrusion detection. It has the drawback of several redundant records, which will affect the effectiveness of the evaluated systems. Pervez et al. [28] have presented a new built form of KDD99 named as NSLKDD for overcoming these issues. The KDDTrain+ and KDDTest+ sets of NSL KDD dataset have approximately 125,973 and 22,544 connection records correspondingly. Similar to KDD99, each record in this data is unique as it is labeled with attack or normal based on the 41-feature set. NSL-KDD dataset includes the same four categories of attacks as the original KDD-99 Dataset.
Kyoto dataset. Kyoto dataset was anticipated by Song et al. [29] This dataset is created on real-time 3 years of network traffic data from regular servers and honeypots. The data was utilized for further analysis of approximately 257,673 records. Each connection in the dataset was seen to be unique with 24 features and 14 statistical features from KDD Cup 99 dataset, also 10 features from their networks, were extracted by the authors. The statistics of the intrusion datasets were utilized for the experiments is displayed in Table 1.
(6) Neha Gupta, et al. LIO-IDS: Handling class imbalance using LSTM and improved one-vs-one technique in intrusion detection system. Computer Networks.
This section discusses the three intrusion detection datasets that have been used in this paper for experimentation purposes. This includes NSL-KDD, CIDDS-001, and CICIDS2017 datasets.
The NSL-KDD (Network Socket Layer – Knowledge Discovery in Databases) dataset was developed in 2009 as the successor of the KDD 1999 dataset [46]. The NSL-KDD dataset overcame the drawbacks of the KDD dataset by removing several redundant and duplicate samples in training and testing datasets. It was created to maximize prediction difficulty, and this characteristic makes it a preferred choice by researchers even today [47]. NSL-KDD consists of separate training and testing datasets containing network traffic samples represented by 41 attributes. Each instance has a label corresponding to the normal class or one of the 22 attack types. These attack types are grouped into four major attack classes, namely Denial of Service (DoS), Probe, Remote to Local (R2L), and User to Root (U2R). Table 3 shows the number of samples present in various classes of the NSL-KDD dataset. The uneven distribution of samples in different classes of this dataset makes it an appropriate choice for testing the proposed LIO-IDS.
The CICIDS2017 dataset was developed by Sharafaldin et al. [49] by generating and capturing network traffic for a duration of five days. The dataset consists of normal traffic samples and traffic samples generated from fourteen different types of attacks. The authors utilized the B-profile system to imitate benign human activities on the web and generate normal traffic from HTTP, HTTPS, FTP, and SSH protocols. Different categories of attacks were generated using various tools available on the Internet. The original CICIDS2017 dataset consists of eight CSV files containing 22,73,097 normal samples and 5,57,646 attack samples. Each traffic sample consists of 80 features that were captured using the CICFlowMeter tool. Due to the huge size of the original dataset, a subset of the CICIDS2017 dataset was selected for experimentation in this paper. The details of the selected subsets have been shown in Table 5.
The intrusion detection datasets selected in this paper consist of categorical as well as numerical attribute values. To bring these values in a uniform format, dataset pre-processing was performed on both of them. This process has been explained in the following sub-section.
(7) Yakubu Imrana, et al. A bidirectional LSTM deep learning approach for intrusion detection. Expert Systems With Applications.
The NSL-KDD dataset (Tavallaee et al., 2009; UNB, 2009) is one of the bench-marked datasets for evaluating Intrusion Detection Systems (IDS). It is an enhanced form of the KDDCup ’99 dataset (Dua & Graff, 2017). The dataset comprises a training set (KDDTrain+) with 125,973 traffic samples and two separate test sets (i.e., KDDTest+ and KDDTest−21). The KDDTest+ has 22,544 traffic samples, and the KDDTest−21 has 11,850 samples. Additionally, to make the intrusion detection more realistic, the test datasets include many attacks that do not appear in the training set (see Table 2). Thus, adding to the 22 types of attacks in the training set, 17 more different attack types exist in the test set.
The NSL-KDD dataset contains 41 features, including 3 non-numeric (i.e.,
