As science attempts to close the gap between man and machine by building systems capable
of learning, we must embrace the importance of the unknown. The ability to differentiate …

Typically, most network intrusion detection systems use supervised learning techniques to
identify network anomalies. A problem exists when identifying the unknowns and …

Mobile network is currently evolving into data centric architecture. Long Term Evolution (LTE)
based next generation 4G technology is being deployed by cellular operators around the …

In real-world deployments, machine learning applications find challenges when accessing
ever-increasing volumes of data - the real world is open and often presents data from classes …

Abstract Machine learning is continuously expanding as a sub-field of computer science
which evolved from pattern recognition and computational learning theory. Advancements in …

Understanding malware behavior will help in implementing robust intrusion detection and
prevention systems. In this paper, we studied the behavioral characteristics of different …

Malware, a short name for malicious software is an emerging cyber threat. Various researchers
have proposed ways to build advanced malware detectors that can mitigate threat actors …

… the coupling values 𝑒𝑠𝑖 𝑗 and local fields ℎ𝑠𝑖 from F𝑠, and define the threshold 𝑡𝑠 as
the 95th percentile of the energies of samples in F𝑠, calculated using 𝑒𝑠𝑖 𝑗 and ℎ𝑠𝑖 . To …

… James Henrydoss AT&T and University of Colorado, USA Krzysztof Walkowiak Wroclaw
University of Technology, Poland Murugan D. Manonmaniam Sundaranar University, India …

With the emergence of new 4G LTE network, many new security threats have also appeared.
Unlike 2G and 3G, 4G uses IP based communication. Due to the excessive internet usage …