97 research outputs found
HTMLPhish: Enabling Phishing Web Page Detection by Applying Deep Learning Techniques on HTML Analysis
Recently, the development and implementation of phishing attacks require little technical skills and costs. This uprising has led to an ever-growing number of phishing attacks on the World Wide Web. Consequently, proactive techniques to fight phishing attacks have become extremely necessary. In this paper, we propose HTMLPhish, a deep learning based datadriven end-to-end automatic phishing web page classification approach. Specifically, HTMLPhish receives the content of the HTML document of a web page and employs Convolutional Neural Networks (CNNs) to learn the semantic dependencies in the textual contents of the HTML. The CNNs learn appropriate feature representations from the HTML document embeddings without extensive manual feature engineering. Furthermore, our proposed approach of the concatenation of the word and character embeddings allows our model to manage new features and ensure easy extrapolation to test data. We conduct comprehensive experiments on a dataset of more than 50,000 HTML documents that provides a distribution of phishing to benign web pages obtainable in the real-world that yields over 93% Accuracy and True Positive Rate. Also, HTMLPhish is a completely language-independent and client-side strategy which can, therefore, conduct web page phishing detection regardless of the textual language
On Markov Games Played by Bayesian and Boundedly-Rational Players
We present a new game-theoretic framework in which Bayesian players with bounded rationality engage in a Markov game and each has private but incomplete information regarding other players' types. Instead of utilizing Harsanyi's abstract types and a common prior, we construct intentional player types whose structure is explicit and induces a {\em finite-level} belief hierarchy. We characterize an equilibrium in this game and establish the conditions for existence of the equilibrium. The computation of finding such equilibria is formalized as a constraint satisfaction problem and its effectiveness is demonstrated on two cooperative domains
Iterative Online Planning in Multiagent Settings with Limited Model Spaces and PAC Guarantees
Methods for planning in multiagent settings often model other agents β possible behaviors. However, the space of these models β whether these are policy trees, finite-state controllers or inten-tional models β is very large and thus arbitrarily bounded. This may exclude the true model or the optimal model. In this paper, we present a novel iterative algorithm for online planning that consid-ers a limited model space, updates it dynamically using data from interactions, and provides a provable and probabilistic bound on the approximation error. We ground this approach in the context of graphical models for planning in partially observable multiagent settings β interactive dynamic influence diagrams. We empirically demonstrate that the limited model space facilitates fast solutions and that the true model often enters the limited model space
Look Before You Leap: Detecting Phishing Web Pages by Exploiting Raw URL And HTML Characteristics
Cybercriminals resort to phishing as a simple and cost-effective medium to
perpetrate cyber-attacks on today's Internet. Recent studies in phishing
detection are increasingly adopting automated feature selection over
traditional manually engineered features. This transition is due to the
inability of existing traditional methods to extrapolate their learning to new
data. To this end, in this paper, we propose WebPhish, a deep learning
technique using automatic feature selection extracted from the raw URL and HTML
of a web page. This approach is the first of its kind, which uses the
concatenation of URL and HTML embedding feature vectors as input into a
Convolutional Neural Network model to detect phishing attacks on web pages.
Extensive experiments on a real-world dataset yielded an accuracy of 98
percent, outperforming other state-of-the-art techniques. Also, WebPhish is a
client-side strategy that is completely language-independent and can conduct
lightweight phishing detection regardless of the web page's textual language
Learning Markov Decision Processes for Model Checking
Constructing an accurate system model for formal model verification can be
both resource demanding and time-consuming. To alleviate this shortcoming,
algorithms have been proposed for automatically learning system models based on
observed system behaviors. In this paper we extend the algorithm on learning
probabilistic automata to reactive systems, where the observed system behavior
is in the form of alternating sequences of inputs and outputs. We propose an
algorithm for automatically learning a deterministic labeled Markov decision
process model from the observed behavior of a reactive system. The proposed
learning algorithm is adapted from algorithms for learning deterministic
probabilistic finite automata, and extended to include both probabilistic and
nondeterministic transitions. The algorithm is empirically analyzed and
evaluated by learning system models of slot machines. The evaluation is
performed by analyzing the probabilistic linear temporal logic properties of
the system as well as by analyzing the schedulers, in particular the optimal
schedulers, induced by the learned models.Comment: In Proceedings QFM 2012, arXiv:1212.345
Team behavior in interactive dynamic influence diagrams with applications to ad hoc teams
Planning for ad hoc teamwork is challenging because it involves agents
collaborating without any prior coordination or communication. The focus is on
principled methods for a single agent to cooperate with others. This motivates
investigating the ad hoc teamwork problem in the context of individual decision
making frameworks. However, individual decision making in multiagent settings
faces the task of having to reason about other agents' actions, which in turn
involves reasoning about others. An established approximation that
operationalizes this approach is to bound the infinite nesting from below by
introducing level 0 models. We show that a consequence of the finitely-nested
modeling is that we may not obtain optimal team solutions in cooperative
settings. We address this limitation by including models at level 0 whose
solutions involve learning. We demonstrate that the learning integrated into
planning in the context of interactive dynamic influence diagrams facilitates
optimal team behavior, and is applicable to ad hoc teamwork.Comment: 8 pages, Appeared in the MSDM Workshop at AAMAS 2014, Extended
Abstract version appeared at AAMAS 2014, Franc
- β¦