@article{(Open Science Index):https://publications.waset.org/pdf/9997533,
	  title     = {Incorporating Multiple Supervised Learning  Algorithms for Effective Intrusion Detection},
	  author    = {Umar Albalawi and  Sang C. Suh and  Jinoh Kim},
	  country	= {},
	  institution	= {},
	  abstract     = {As internet continues to expand its usage with an 
enormous number of applications, cyber-threats have significantly 
increased accordingly. Thus, accurate detection of malicious traffic in 
a timely manner is a critical concern in today’s Internet for security. 
One approach for intrusion detection is to use Machine Learning (ML) 
techniques. Several methods based on ML algorithms have been 
introduced over the past years, but they are largely limited in terms of 
detection accuracy and/or time and space complexity to run. In this 
work, we present a novel method for intrusion detection that 
incorporates a set of supervised learning algorithms. The proposed 
technique provides high accuracy and outperforms existing techniques 
that simply utilizes a single learning method. In addition, our 
technique relies on partial flow information (rather than full 
information) for detection, and thus, it is light-weight and desirable for 
online operations with the property of early identification. With the 
mid-Atlantic CCDC intrusion dataset publicly available, we show that 
our proposed technique yields a high degree of detection rate over 99% 
with a very low false alarm rate (0.4%). 

	    journal   = {International Journal of Computer and Systems Engineering},
	  volume    = {8},
	  number    = {2},
	  year      = {2014},
	  pages     = {290 - 294},
	  ee        = {https://publications.waset.org/pdf/9997533},
	  url   	= {https://publications.waset.org/vol/86},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 86, 2014},