TY - GEN
T1 - SmartDetect
T2 - 3rd International Conference on Smart Computing and Communications, SmartCom 2018
AU - Zhang, Zijian
AU - Li, Meng
AU - Zhu, Liehuang
AU - Li, Xinyi
N1 - Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - The rapid global spread of the web technology has led to an increase in unauthorized intrusions into computers and networks. Malicious web shell codes used by hackers can often cause extremely harmful consequences. However, the existing detection methods cannot precisely distinguish between the bad codes and the good codes. To solve this problem, we first detected the malicious web shell codes by applying the traditional data mining algorithms: Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Decision Tree, and Convolutional Neural Network. Then, we designed an ensemble learning classifier to further improve the accuracy. Our experimental analysis proved that the accuracy of SmartDetect—our proposed smart detection scheme for malicious web shell codes—was higher than the accuracy of Shell Detector and NeoPI on the dataset collected from Github. Also, the equal-error rate of the detection result of SmartDetect was lower than those of Shell Detector and NeoPI.
AB - The rapid global spread of the web technology has led to an increase in unauthorized intrusions into computers and networks. Malicious web shell codes used by hackers can often cause extremely harmful consequences. However, the existing detection methods cannot precisely distinguish between the bad codes and the good codes. To solve this problem, we first detected the malicious web shell codes by applying the traditional data mining algorithms: Support Vector Machine, K-Nearest Neighbor, Naive Bayes, Decision Tree, and Convolutional Neural Network. Then, we designed an ensemble learning classifier to further improve the accuracy. Our experimental analysis proved that the accuracy of SmartDetect—our proposed smart detection scheme for malicious web shell codes—was higher than the accuracy of Shell Detector and NeoPI on the dataset collected from Github. Also, the equal-error rate of the detection result of SmartDetect was lower than those of Shell Detector and NeoPI.
KW - Data mining
KW - Malicious web shell code
KW - Smart detection
UR - http://www.scopus.com/inward/record.url?scp=85058559903&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-05755-8_20
DO - 10.1007/978-3-030-05755-8_20
M3 - Conference contribution
AN - SCOPUS:85058559903
SN - 9783030057541
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 196
EP - 205
BT - Smart Computing and Communication - 3rd International Conference, SmartCom 2018, Proceedings
A2 - Qiu, Meikang
PB - Springer Verlag
Y2 - 10 December 2018 through 12 December 2018
ER -