Multiple Classifier Systems for More Accurate Java']JavaScript Malware Detection

被引：0

作者：

Yi, Zibo ^{[1
]}

Ma, Jun ^{[1
]}

Luo, Lei ^{[1
]}

Yu, Jie ^{[1
]}

Wu, Qingbo ^{[1
]}

机构：

[1] Natl Univ Def Technol, Coll Comp, Changsha, Hunan, Peoples R China

来源：

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON PROMOTION OF INFORMATION TECHNOLOGY (ICPIT 2016) | 2016年 / 66卷

关键词：

machine learning; !text type='Java']Java[!/text]Script malware detection; multiple classifier system;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The researches of JavaScript malware detection focus on machine learning techniques in recent years. These works extract features from JavaScript's abstract syntax tree for the training of classifiers and achieve satisfactory detection results. However, in the training set there exist some scripts that are not so representative and may cause occasional incorrect classification. We propose multiple classifier system (MCS) to reduce this kind of misclassification. As shown in the experiments, the accuracy increases because of the MCS while training time is slightly greater than the original classifier.

引用

页码：139 / 143

页数：5

共 50 条

[41] Quantitative study on the generalization error of Multiple Classifier Systems [J].

Ng, WWY ;

Chan, APF ;

Yeung, DS ;

Tsang, ECC .

INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS, VOL 1-4, PROCEEDINGS, 2005, :889-894

[42] Comparative Study of Malware Detection Techniques for Industrial Control Systems [J].

Reid, Deborah ;

Harris, Ian ;

Petrovski, Andrei .

2021 14TH INTERNATIONAL CONFERENCE ON SECURITY OF INFORMATION AND NETWORKS (SIN 2021), 2021,

[43] Disarming visualization-based approaches in malware detection systems [J].

Fasci, Lara Saidia ;

Fisichella, Marco ;

Lax, Gianluca ;

Qian, Chenyi .

COMPUTERS & SECURITY, 2023, 126

[44] A machine learning approach to detection of Java']JavaScript-based attacks using AST features and paragraph vectors [J].

Ndichu, Samuel ;

Kim, Sangwook ;

Ozawa, Seiichi ;

Misu, Takeshi ;

Makishima, Kazuo .

APPLIED SOFT COMPUTING, 2019, 84

[45] Behavioral Malware Detection using a Language Model Classifier Trained on sys2vec Embeddings [J].

Carter, John ;

Mancoridis, Spiros ;

Protopapas, Pavlos ;

Galinkin, Erick .

PROCEEDINGS OF THE 57TH ANNUAL HAWAII INTERNATIONAL CONFERENCE ON SYSTEM SCIENCES, 2024, :7582-7591

[46] Malicious PDF Detection Model against Adversarial Attack Built from Benign PDF Containing Java']JavaScript [J].

Kang, Ah Reum ;

Jeong, Young-Seob ;

Kim, Se Lyeong ;

Woo, Jiyoung .

APPLIED SCIENCES-BASEL, 2019, 9 (22)

[47] Machine Learning Approach for Malware Detection Using Random Forest Classifier on Process List Data Structure [J].

Joshi, Santosh ;

Upadhyay, Himanshu ;

Lagos, Leonel ;

Akkipeddi, Naga Suryamitra ;

Guerra, Valerie .

2ND INTERNATIONAL CONFERENCE ON INFORMATION SYSTEM AND DATA MINING (ICISDM 2018), 2018, :98-102

[48] Learning Classifier Systems for Road Traffic Congestion Detection [J].

Sommer, Matthias ;

Haehner, Joerg .

VEHITS: PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON VEHICLE TECHNOLOGY AND INTELLIGENT TRANSPORT SYSTEMS, 2017, :142-150

[49] Design of Dynamic Multiple Classifier Systems Based on Belief Functions [J].

Han, Deqiang ;

Li, X. Rong ;

Liang, Shaoyi .

2013 16TH INTERNATIONAL CONFERENCE ON INFORMATION FUSION (FUSION), 2013, :418-425

[50] Design of Multiple Classifier Systems Based on Evidential Neural Network [J].

He, Hongshun ;

Han, Deqiang ;

Yang, Yi .

PROCEEDINGS OF THE 36TH CHINESE CONTROL CONFERENCE (CCC 2017), 2017, :5496-5501

← 1 2 3 4 5 →