Visible to the public Biblio

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2020-12-14
Dong, D., Ye, Z., Su, J., Xie, S., Cao, Y., Kochan, R..  2020.  A Malware Detection Method Based on Improved Fireworks Algorithm and Support Vector Machine. 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET). :846–851.
The increasing of malwares has presented a serious threat to the security of computer systems in recent years. Traditional signature-based anti-virus systems are not able to detect metamorphic and previously unseen malwares and it inspires people to use machine learning methods such as Naive Bayes and Decision Tree to identity malicious executables. Among these methods, detecting malwares by using Support Vector Machine (SVM) is one of the most effective approaches. However, the parameters of SVM have serious impacts on its classification performance. In order to find the optimal parameter combination and avoid the problem of falling into local optimal solution, many methods based on evolutionary algorithms are proposed, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE) and others. But these algorithms still face the problem of being trapped into local solution spaces in different degree. In this paper, an improved fireworks algorithm is presented and applied to search parameters of SVM: penalty factor c and kernel function parameter g. To research the performance of the proposed algorithm, numeric experiments are made and compared with some typical algorithms, the experimental results demonstrate it outperforms other algorithms.
Kyaw, A. T., Oo, M. Zin, Khin, C. S..  2020.  Machine-Learning Based DDOS Attack Classifier in Software Defined Network. 2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). :431–434.
Due to centralized control and programmable capability of the SDN architecture, network administrators can easily manage and control the whole network through the centralized controller. According to the SDN architecture, the SDN controller is vulnerable to distributed denial of service (DDOS) attacks. Thus, a failure of SDN controller is a major leak for security concern. The objectives of paper is therefore to detect the DDOS attacks and classify the normal or attack traffic in SDN network using machine learning algorithms. In this proposed system, polynomial SVM is applied to compare to existing linear SVM by using scapy, which is packet generation tool and RYU SDN controller. According to the experimental result, polynomial SVM achieves 3% better accuracy and 34% lower false alarm rate compared to Linear SVM.
Deng, M., Wu, X., Feng, P., Zeng, W..  2020.  Sparse Support Vector Machine for Network Behavior Anomaly Detection. 2020 IEEE 8th International Conference on Information, Communication and Networks (ICICN). :199–204.
Network behavior anomaly detection (NBAD) require fast mechanisms for learning from the large scale data. However, the training velocity of general machine learning approach is largely limited by the adopted training weights of all features in the NBAD. In this paper, we notice, however, that the related weights matching of NBAD features is sparse, which is not necessary for holding all weights. Hence, in this paper, we consider an efficient support vector machine (SVM) approach for NBAD by imposing 1 -norm. Essentially, we propose to use sparse SVM (S-SVM), where sparsity in model, i.e. in weights is used to interfere with special feature selection and that can achieve feature selection and classification efficiently.
Habibi, G., Surantha, N..  2020.  XSS Attack Detection With Machine Learning and n-Gram Methods. 2020 International Conference on Information Management and Technology (ICIMTech). :516–520.

Cross-Site Scripting (XSS) is an attack most often carried out by attackers to attack a website by inserting malicious scripts into a website. This attack will take the user to a webpage that has been specifically designed to retrieve user sessions and cookies. Nearly 68% of websites are vulnerable to XSS attacks. In this study, the authors conducted a study by evaluating several machine learning methods, namely Support Vector Machine (SVM), K-Nearest Neighbour (KNN), and Naïve Bayes (NB). The machine learning algorithm is then equipped with the n-gram method to each script feature to improve the detection performance of XSS attacks. The simulation results show that the SVM and n-gram method achieves the highest accuracy with 98%.

2020-10-29
Priyamvada Davuluru, Venkata Salini, Narayanan Narayanan, Barath, Balster, Eric J..  2019.  Convolutional Neural Networks as Classification Tools and Feature Extractors for Distinguishing Malware Programs. 2019 IEEE National Aerospace and Electronics Conference (NAECON). :273—278.

Classifying malware programs is a research area attracting great interest for Anti-Malware industry. In this research, we propose a system that visualizes malware programs as images and distinguishes those using Convolutional Neural Networks (CNNs). We study the performance of several well-established CNN based algorithms such as AlexNet, ResNet and VGG16 using transfer learning approaches. We also propose a computationally efficient CNN-based architecture for classification of malware programs. In addition, we study the performance of these CNNs as feature extractors by using Support Vector Machine (SVM) and K-nearest Neighbors (kNN) for classification purposes. We also propose fusion methods to boost the performance further. We make use of the publicly available database provided by Microsoft Malware Classification Challenge (BIG 2015) for this study. Our overall performance is 99.4% for a set of 2174 test samples comprising 9 different classes thereby setting a new benchmark.

2020-09-28
Akaishi, Sota, Uda, Ryuya.  2019.  Classification of XSS Attacks by Machine Learning with Frequency of Appearance and Co-occurrence. 2019 53rd Annual Conference on Information Sciences and Systems (CISS). :1–6.
Cross site scripting (XSS) attack is one of the attacks on the web. It brings session hijack with HTTP cookies, information collection with fake HTML input form and phishing with dummy sites. As a countermeasure of XSS attack, machine learning has attracted a lot of attention. There are existing researches in which SVM, Random Forest and SCW are used for the detection of the attack. However, in the researches, there are problems that the size of data set is too small or unbalanced, and that preprocessing method for vectorization of strings causes misclassification. The highest accuracy of the classification was 98% in existing researches. Therefore, in this paper, we improved the preprocessing method for vectorization by using word2vec to find the frequency of appearance and co-occurrence of the words in XSS attack scripts. Moreover, we also used a large data set to decrease the deviation of the data. Furthermore, we evaluated the classification results with two procedures. One is an inappropriate procedure which some researchers tend to select by mistake. The other is an appropriate procedure which can be applied to an attack detection filter in the real environment.
2020-06-19
Keshari, Tanya, Palaniswamy, Suja.  2019.  Emotion Recognition Using Feature-level Fusion of Facial Expressions and Body Gestures. 2019 International Conference on Communication and Electronics Systems (ICCES). :1184—1189.

Automatic emotion recognition using computer vision is significant for many real-world applications like photojournalism, virtual reality, sign language recognition, and Human Robot Interaction (HRI) etc., Psychological research findings advocate that humans depend on the collective visual conduits of face and body to comprehend human emotional behaviour. Plethora of studies have been done to analyse human emotions using facial expressions, EEG signals and speech etc., Most of the work done was based on single modality. Our objective is to efficiently integrate emotions recognized from facial expressions and upper body pose of humans using images. Our work on bimodal emotion recognition provides the benefits of the accuracy of both the modalities.

2020-05-18
Lee, Hyun-Young, Kang, Seung-Shik.  2019.  Word Embedding Method of SMS Messages for Spam Message Filtering. 2019 IEEE International Conference on Big Data and Smart Computing (BigComp). :1–4.
SVM has been one of the most popular machine learning method for the binary classification such as sentiment analysis and spam message filtering. We explored a word embedding method for the construction of a feature vector and the deep learning method for the binary classification. CBOW is used as a word embedding technique and feedforward neural network is applied to classify SMS messages into ham or spam. The accuracy of the two classification methods of SVM and neural network are compared for the binary classification. The experimental result shows that the accuracy of deep learning method is better than the conventional machine learning method of SVM-light in the binary classification.
2020-02-26
Matin, Iik Muhamad Malik, Rahardjo, Budi.  2019.  Malware Detection Using Honeypot and Machine Learning. 2019 7th International Conference on Cyber and IT Service Management (CITSM). 7:1–4.

Malware is one of the threats to information security that continues to increase. In 2014 nearly six million new malware was recorded. The highest number of malware is in Trojan Horse malware while in Adware malware is the most significantly increased malware. Security system devices such as antivirus, firewall, and IDS signature-based are considered to fail to detect malware. This happens because of the very fast spread of computer malware and the increasing number of signatures. Besides signature-based security systems it is difficult to identify new methods, viruses or worms used by attackers. One other alternative in detecting malware is to use honeypot with machine learning. Honeypot can be used as a trap for packages that are suspected while machine learning can detect malware by classifying classes. Decision Tree and Support Vector Machine (SVM) are used as classification algorithms. In this paper, we propose architectural design as a solution to detect malware. We presented the architectural proposal and explained the experimental method to be used.

2020-01-27
Zhang, Naiji, Jaafar, Fehmi, Malik, Yasir.  2019.  Low-Rate DoS Attack Detection Using PSD Based Entropy and Machine Learning. 2019 6th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/ 2019 5th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom). :59–62.
The Distributed Denial of Service attack is one of the most common attacks and it is hard to mitigate, however, it has become more difficult while dealing with the Low-rate DoS (LDoS) attacks. The LDoS exploits the vulnerability of TCP congestion-control mechanism by sending malicious traffic at the low constant rate and influence the victim machine. Recently, machine learning approaches are applied to detect the complex DDoS attacks and improve the efficiency and robustness of the intrusion detection system. In this research, the algorithm is designed to balance the detection rate and its efficiency. The detection algorithm combines the Power Spectral Density (PSD) entropy function and Support Vector Machine to detect LDoS traffic from normal traffic. In our solution, the detection rate and efficiency are adjustable based on the parameter in the decision algorithm. To have high efficiency, the detection method will always detect the attacks by calculating PSD-entropy first and compare it with the two adaptive thresholds. The thresholds can efficiently filter nearly 19% of the samples with a high detection rate. To minimize the computational cost and look only for the patterns that are most relevant for detection, Support Vector Machine based machine learning model is applied to learn the traffic pattern and select appropriate features for detection algorithm. The experimental results show that the proposed approach can detect 99.19% of the LDoS attacks and has an O (n log n) time complexity in the best case.
2019-12-30
Wang, XuMing, Huang, Jin, Zhu, Jia, Yang, Min, Yang, Fen.  2018.  Facial Expression Recognition with Deep Learning. Proceedings of the 10th International Conference on Internet Multimedia Computing and Service. :10:1–10:4.
Automatic recognition of facial expression images is a challenge for computer due to variation of expression, background, position and label noise. The paper propose a new method for static facial expression recognition. Main process is to perform experiments by FER-2013 dataset, the primary mission is using our CNN model to classify a set of static images into 7 basic emotions and then achieve effective classification automatically. The two preprocessing of the faces picture have enhanced the effect of the picture for recognition. First, FER datasets are preprocessed with standard histogram eqialization. Then we employ ImageDataGenerator to deviate and rotate the facial image to enhance model robustness. Finally, the result of softmax activation function (also known as multinomial logistic regression) is stacked by SVM. The result of softmax activation function + SVM is better than softmax activation function. The accuracy of facial expression recognition achieve 68.79% on the test set.
2019-11-12
Ferenc, Rudolf, Heged\H us, Péter, Gyimesi, Péter, Antal, Gábor, Bán, Dénes, Gyimóthy, Tibor.  2019.  Challenging Machine Learning Algorithms in Predicting Vulnerable JavaScript Functions. 2019 IEEE/ACM 7th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE). :8-14.

The rapid rise of cyber-crime activities and the growing number of devices threatened by them place software security issues in the spotlight. As around 90% of all attacks exploit known types of security issues, finding vulnerable components and applying existing mitigation techniques is a viable practical approach for fighting against cyber-crime. In this paper, we investigate how the state-of-the-art machine learning techniques, including a popular deep learning algorithm, perform in predicting functions with possible security vulnerabilities in JavaScript programs. We applied 8 machine learning algorithms to build prediction models using a new dataset constructed for this research from the vulnerability information in public databases of the Node Security Project and the Snyk platform, and code fixing patches from GitHub. We used static source code metrics as predictors and an extensive grid-search algorithm to find the best performing models. We also examined the effect of various re-sampling strategies to handle the imbalanced nature of the dataset. The best performing algorithm was KNN, which created a model for the prediction of vulnerable functions with an F-measure of 0.76 (0.91 precision and 0.66 recall). Moreover, deep learning, tree and forest based classifiers, and SVM were competitive with F-measures over 0.70. Although the F-measures did not vary significantly with the re-sampling strategies, the distribution of precision and recall did change. No re-sampling seemed to produce models preferring high precision, while re-sampling strategies balanced the IR measures.

2019-08-26
Shen, Shiyu, Gao, Jianlin, Wu, Aitian.  2018.  Weakness Identification and Flow Analysis Based on Tor Network. Proceedings of the 8th International Conference on Communication and Network Security. :90–94.

As the Internet technology develops rapidly, attacks against Tor networks becomes more and more frequent. So, it's more and more difficult for Tor network to meet people's demand to protect their private information. A method to improve the anonymity of Tor seems urgent. In this paper, we mainly talk about the principle of Tor, which is the largest anonymous communication system in the world, analyze the reason for its limited efficiency, and discuss the vulnerability of link fingerprint and node selection. After that, a node recognition model based on SVM is established, which verifies that the traffic characteristics expose the node attributes, thus revealing the link and destroying the anonymity. Based on what is done above, some measures are put forward to improve Tor protocol to make it more anonymous.

2019-07-01
Perez, R. Lopez, Adamsky, F., Soua, R., Engel, T..  2018.  Machine Learning for Reliable Network Attack Detection in SCADA Systems. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). :633–638.

Critical Infrastructures (CIs) use Supervisory Control And Data Acquisition (SCADA) systems for remote control and monitoring. Sophisticated security measures are needed to address malicious intrusions, which are steadily increasing in number and variety due to the massive spread of connectivity and standardisation of open SCADA protocols. Traditional Intrusion Detection Systems (IDSs) cannot detect attacks that are not already present in their databases. Therefore, in this paper, we assess Machine Learning (ML) for intrusion detection in SCADA systems using a real data set collected from a gas pipeline system and provided by the Mississippi State University (MSU). The contribution of this paper is two-fold: 1) The evaluation of four techniques for missing data estimation and two techniques for data normalization, 2) The performances of Support Vector Machine (SVM), and Random Forest (RF) are assessed in terms of accuracy, precision, recall and F1score for intrusion detection. Two cases are differentiated: binary and categorical classifications. Our experiments reveal that RF detect intrusions effectively, with an F1score of respectively \textbackslashtextgreater 99%.

2019-06-10
Basomingera, R., Choi, Y..  2019.  Route Cache Based SVM Classifier for Intrusion Detection of Control Packet Attacks in Mobile Ad-Hoc Networks. 2019 International Conference on Information Networking (ICOIN). :31–36.

For the security of mobile ad-hoc networks (MANETs), a group of wireless mobile nodes needs to cooperate by forwarding packets, to implement an intrusion detection system (IDS). Some of the current IDS implementations in a clustered MANET have designed mobile nodes to wait until the cluster head is elected before scanning the network and thus nodes may be, unfortunately, exposed to several control packet attacks by which nodes identify falsified routes to reach other nodes. In order to detect control packet attacks such as route falsification, we design a route cache sharing mechanism for a non-clustered network where all one-hop routing data are collected by each node for a cooperative host-based detection. The cooperative host-based detection system uses a Support Vector Machine classifier and achieves a detection rate of around 95%. By successfully detecting the route falsification attacks, nodes are given the capability to avoid other attacks such as black-hole and gray-hole, which are in many cases a result of a successful route falsification attack.

2019-03-15
Deliu, I., Leichter, C., Franke, K..  2018.  Collecting Cyber Threat Intelligence from Hacker Forums via a Two-Stage, Hybrid Process Using Support Vector Machines and Latent Dirichlet Allocation. 2018 IEEE International Conference on Big Data (Big Data). :5008-5013.

Traditional security controls, such as firewalls, anti-virus and IDS, are ill-equipped to help IT security and response teams keep pace with the rapid evolution of the cyber threat landscape. Cyber Threat Intelligence (CTI) can help remediate this problem by exploiting non-traditional information sources, such as hacker forums and "dark-web" social platforms. Security and response teams can use the collected intelligence to identify emerging threats. Unfortunately, when manual analysis is used to extract CTI from non-traditional sources, it is a time consuming, error-prone and resource intensive process. We address these issues by using a hybrid Machine Learning model that automatically searches through hacker forum posts, identifies the posts that are most relevant to cyber security and then clusters the relevant posts into estimations of the topics that the hackers are discussing. The first (identification) stage uses Support Vector Machines and the second (clustering) stage uses Latent Dirichlet Allocation. We tested our model, using data from an actual hacker forum, to automatically extract information about various threats such as leaked credentials, malicious proxy servers, malware that evades AV detection, etc. The results demonstrate our method is an effective means for quickly extracting relevant and actionable intelligence that can be integrated with traditional security controls to increase their effectiveness.

2019-02-25
Vishagini, V., Rajan, A. K..  2018.  An Improved Spam Detection Method with Weighted Support Vector Machine. 2018 International Conference on Data Science and Engineering (ICDSE). :1–5.
Email is the most admired method of exchanging messages using the Internet. One of the intimidations to email users is to detect the spam they receive. This can be addressed using different detection and filtering techniques. Machine learning algorithms, especially Support Vector Machine (SVM), can play vital role in spam detection. We propose the use of weighted SVM for spam filtering using weight variables obtained by KFCM algorithm. The weight variables reflect the importance of different classes. The misclassification of emails is reduced by the growth of weight value. We evaluate the impact of spam detection using SVM, WSVM with KPCM and WSVM with KFCM.UCI Repository SMS Spam base dataset is used for our experimentation.
2018-12-10
Kumar, S., Singh, C. Bhim Bhan.  2018.  A Zero-Day Resistant Malware Detection Method for Securing Cloud Using SVM and Sandboxing Techniques. 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT). :1397–1402.

Cloud nowaday has become the backbone of the IT infrastructure. Whole of the infrastructure is now being shifted to the clouds, and as the cloud involves all of the networking schemes and the OS images, it inherits all of the vulnerabilities too. And hence securing them is one of our very prior concerns. Malwares are one of the many other problems that have ever growing and hence need to be eradicated from the system. The history of mal wares go long back in time since the advent of computers and hence a lot of techniques has also been already devised to tackle with the problem in some or other way. But most of them fall short in some or other way or are just too heavy to execute on a simple user machine. Our approach devises a 3 - phase exhaustive technique which confirms the detection of any kind of malwares from the host. It also works for the zero-day attacks that are really difficult to cover most times and can be of really high-risk at times. We have thought of a solution to keep the things light weight for the user.

2018-06-20
Searles, R., Xu, L., Killian, W., Vanderbruggen, T., Forren, T., Howe, J., Pearson, Z., Shannon, C., Simmons, J., Cavazos, J..  2017.  Parallelization of Machine Learning Applied to Call Graphs of Binaries for Malware Detection. 2017 25th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP). :69–77.

Malicious applications have become increasingly numerous. This demands adaptive, learning-based techniques for constructing malware detection engines, instead of the traditional manual-based strategies. Prior work in learning-based malware detection engines primarily focuses on dynamic trace analysis and byte-level n-grams. Our approach in this paper differs in that we use compiler intermediate representations, i.e., the callgraph representation of binaries. Using graph-based program representations for learning provides structure of the program, which can be used to learn more advanced patterns. We use the Shortest Path Graph Kernel (SPGK) to identify similarities between call graphs extracted from binaries. The output similarity matrix is fed into a Support Vector Machine (SVM) algorithm to construct highly-accurate models to predict whether a binary is malicious or not. However, SPGK is computationally expensive due to the size of the input graphs. Therefore, we evaluate different parallelization methods for CPUs and GPUs to speed up this kernel, allowing us to continuously construct up-to-date models in a timely manner. Our hybrid implementation, which leverages both CPU and GPU, yields the best performance, achieving up to a 14.2x improvement over our already optimized OpenMP version. We compared our generated graph-based models to previously state-of-the-art feature vector 2-gram and 3-gram models on a dataset consisting of over 22,000 binaries. We show that our classification accuracy using graphs is over 19% higher than either n-gram model and gives a false positive rate (FPR) of less than 0.1%. We are also able to consider large call graphs and dataset sizes because of the reduced execution time of our parallelized SPGK implementation.

Petersen, E., To, M. A., Maag, S..  2017.  A novel online CEP learning engine for MANET IDS. 2017 IEEE 9th Latin-American Conference on Communications (LATINCOM). :1–6.

In recent years the use of wireless ad hoc networks has seen an increase of applications. A big part of the research has focused on Mobile Ad Hoc Networks (MAnETs), due to its implementations in vehicular networks, battlefield communications, among others. These peer-to-peer networks usually test novel communications protocols, but leave out the network security part. A wide range of attacks can happen as in wired networks, some of them being more damaging in MANETs. Because of the characteristics of these networks, conventional methods for detection of attack traffic are ineffective. Intrusion Detection Systems (IDSs) are constructed on various detection techniques, but one of the most important is anomaly detection. IDSs based only in past attacks signatures are less effective, even more if these IDSs are centralized. Our work focuses on adding a novel Machine Learning technique to the detection engine, which recognizes attack traffic in an online way (not to store and analyze after), re-writing IDS rules on the fly. Experiments were done using the Dockemu emulation tool with Linux Containers, IPv6 and OLSR as routing protocol, leading to promising results.

2018-05-01
Lin, H., Zhao, D., Ran, L., Han, M., Tian, J., Xiang, J., Ma, X., Zhong, Y..  2017.  CVSSA: Cross-Architecture Vulnerability Search in Firmware Based on Support Vector Machine and Attributed Control Flow Graph. 2017 International Conference on Dependable Systems and Their Applications (DSA). :35–41.

Nowadays, an increasing number of IoT vendors have complied and deployed third-party code bases across different architectures. Therefore, to avoid the firmware from being affected by the same known vulnerabilities, searching known vulnerabilities in binary firmware across different architectures is more crucial than ever. However, most of existing vulnerability search methods are limited to the same architecture, there are only a few researches on cross-architecture cases, of which the accuracy is not high. In this paper, to promote the accuracy of existing cross-architecture vulnerability search methods, we propose a new approach based on Support Vector Machine (SVM) and Attributed Control Flow Graph (ACFG) to search known vulnerability in firmware across different architectures at function level. We employ a known vulnerability function to recognize suspicious functions in other binary firmware. First, considering from the internal and external characteristics of the functions, we extract the function level features and basic-block level features of the functions to be inspected. Second, we employ SVM to recognize a little part of suspicious functions based on function level features. After the preliminary screening, we compute the graph similarity between the vulnerability function and suspicious functions based on their ACFGs. We have implemented our approach CVSSA, and employed the training samples to train the model with previous knowledge to improve the accuracy. We also search several vulnerabilities in the real-world firmware images, the experimental results show that CVSSA can be applied to the realistic scenarios.

Xie, T., Zhou, Q., Hu, J., Shu, L., Jiang, P..  2017.  A Sequential Multi-Objective Robust Optimization Approach under Interval Uncertainty Based on Support Vector Machines. 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). :2088–2092.

Interval uncertainty can cause uncontrollable variations in the objective and constraint values, which could seriously deteriorate the performance or even change the feasibility of the optimal solutions. Robust optimization is to obtain solutions that are optimal and minimally sensitive to uncertainty. In this paper, a sequential multi-objective robust optimization (MORO) approach based on support vector machines (SVM) is proposed. Firstly, a sequential optimization structure is adopted to ease the computational burden. Secondly, SVM is used to construct a classification model to classify design alternatives into feasible or infeasible. The proposed approach is tested on a numerical example and an engineering case. Results illustrate that the proposed approach can reasonably approximate solutions obtained from the existing sequential MORO approach (SMORO), while the computational costs are significantly reduced compared with those of SMORO.

Tran, D. T., Waris, M. A., Gabbouj, M., Iosifidis, A..  2017.  Sample-Based Regularization for Support Vector Machine Classification. 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA). :1–6.

In this paper, we propose a new regularization scheme for the well-known Support Vector Machine (SVM) classifier that operates on the training sample level. The proposed approach is motivated by the fact that Maximum Margin-based classification defines decision functions as a linear combination of the selected training data and, thus, the variations on training sample selection directly affect generalization performance. We show that the exploitation of the proposed regularization scheme is well motivated and intuitive. Experimental results show that the proposed regularization scheme outperforms standard SVM in human action recognition tasks as well as classical recognition problems.

Kong, L., Huang, G., Wu, K..  2017.  Identification of Abnormal Network Traffic Using Support Vector Machine. 2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT). :288–292.

Network traffic identification has been a hot topic in network security area. The identification of abnormal traffic can detect attack traffic and helps network manager enforce corresponding security policies to prevent attacks. Support Vector Machines (SVMs) are one of the most promising supervised machine learning (ML) algorithms that can be applied to the identification of traffic in IP networks as well as detection of abnormal traffic. SVM shows better performance because it can avoid local optimization problems existed in many supervised learning algorithms. However, as a binary classification approach, SVM needs more research in multiclass classification. In this paper, we proposed an abnormal traffic identification system(ATIS) that can classify and identify multiple attack traffic applications. Each component of ATIS is introduced in detail and experiments are carried out based on ATIS. Through the test of KDD CUP dataset, SVM shows good performance. Furthermore, the comparison of experiments reveals that scaling and parameters has a vital impact on SVM training results.

2018-04-11
Gulmezoglu, Berk, Eisenbarth, Thomas, Sunar, Berk.  2017.  Cache-Based Application Detection in the Cloud Using Machine Learning. Proceedings of the 2017 ACM on Asia Conference on Computer and Communications Security. :288–300.

Cross-VM attacks have emerged as a major threat on commercial clouds. These attacks commonly exploit hardware level leakages on shared physical servers. A co-located machine can readily feel the presence of a co-located instance with a heavy computational load through performance degradation due to contention on shared resources. Shared cache architectures such as the last level cache (LLC) have become a popular leakage source to mount cross-VM attack. By exploiting LLC leakages, researchers have already shown that it is possible to recover fine grain information such as cryptographic keys from popular software libraries. This makes it essential to verify implementations that handle sensitive data across the many versions and numerous target platforms, a task too complicated, error prone and costly to be handled by human beings. Here we propose a machine learning based technique to classify applications according to their cache access profiles. We show that with minimal and simple manual processing steps feature vectors can be used to train models using support vector machines to classify the applications with a high degree of success. The profiling and training steps are completely automated and do not require any inspection or study of the code to be classified. In native execution, we achieve a successful classification rate as high as 98% (L1 cache) and 78$\backslash$% (LLC) over 40 benchmark applications in the Phoronix suite with mild training. In the cross-VM setting on the noisy Amazon EC2 the success rate drops to 60$\backslash$% for a suite of 25 applications. With this initial study we demonstrate that it is possible to train meaningful models to successfully predict applications running in co-located instances.