Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- Creators: Yang, Yezhou
Description
Rapid growth of internet and connected devices ranging from cloud systems to internet of things have raised critical concerns for securing these systems. In the recent past, security attacks on different kinds of devices have evolved in terms of complexity and diversity. One of the challenges is establishing secure communication in the network among various devices and systems. Despite being protected with authentication and encryption, the network still needs to be protected against cyber-attacks. For this, the network traffic has to be closely monitored and should detect anomalies and intrusions. Intrusion detection can be categorized as a network traffic classification problem in machine learning. Existing network traffic classification methods require a lot of training and data preprocessing, and this problem is more serious if the dataset size is huge. In addition, the machine learning and deep learning methods that have been used so far were trained on datasets that contain obsolete attacks. In this thesis, these problems are addressed by using ensemble methods applied on an up to date network attacks dataset. Ensemble methods use multiple learning algorithms to get better classification accuracy that could be obtained when the corresponding learning algorithm is applied alone. This dataset for network traffic classification has recent attack scenarios and contains over fifteen attacks. This approach shows that ensemble methods can be used to classify network traffic and detect intrusions with less training times of the model, and lesser pre-processing without feature selection. In addition, this thesis also shows that only with less than ten percent of the total features of input dataset will lead to similar accuracy that is achieved on whole dataset. This can heavily reduce the training times and classification duration in real-time scenarios.
ContributorsPonneganti, Ramu (Author) / Yau, Stephen (Thesis advisor) / Richa, Andrea (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2019
Description
A complex social system, whether artificial or natural, can possess its macroscopic properties as a collective, which may change in real time as a result of local behavioral interactions among a number of agents in it. If a reliable indicator is available to abstract the macrolevel states, decision makers could use it to take a proactive action, whenever needed, in order for the entire system to avoid unacceptable states or con-verge to desired ones. In realistic scenarios, however, there can be many challenges in learning a model of dynamic global states from interactions of agents, such as 1) high complexity of the system itself, 2) absence of holistic perception, 3) variability of group size, 4) biased observations on state space, and 5) identification of salient behavioral cues. In this dissertation, I introduce useful applications of macrostate estimation in complex multi-agent systems and explore effective deep learning frameworks to ad-dress the inherited challenges. First of all, Remote Teammate Localization (ReTLo)is developed in multi-robot teams, in which an individual robot can use its local interactions with a nearby robot as an information channel to estimate the holistic view of the group. Within the problem, I will show (a) learning a model of a modular team can generalize to all others to gain the global awareness of the team of variable sizes, and (b) active interactions are necessary to diversify training data and speed up the overall learning process. The complexity of the next focal system escalates to a colony of over 50 individual ants undergoing 18-day social stabilization since a chaotic event. I will utilize this natural platform to demonstrate, in contrast to (b), (c)monotonic samples only from “before chaos” can be sufficient to model the panicked society, and (d) the model can also be used to discover salient behaviors to precisely predict macrostates.
ContributorsChoi, Taeyeong (Author) / Pavlic, Theodore (Thesis advisor) / Richa, Andrea (Committee member) / Ben Amor, Heni (Committee member) / Yang, Yezhou (Committee member) / Liebig, Juergen (Committee member) / Arizona State University (Publisher)
Created2020