No abstract

Several state-of-the-art event extraction systems employ models based on Support Vector Machines (SVMs) in a pipeline architecture, which fails to exploit the joint dependencies that typically exist among events and arguments. While there have been attempts to overcome this limitation using Markov Logic Networks (MLNs), it remains challenging to perform joint inference in MLNs when the model encodes many high-dimensional sophisticated features such as those essential for event extraction. In this paper, we propose a new model for event extraction that combines the power of MLNs and SVMs, dwarfing their limitations. The key idea is to reliably learn and process high-dimensional features using SVMs; encode the output of SVMs as low-dimensional, soft formulas in MLNs; and use the superior joint inferencing power of MLNs to enforce joint consistency constraints over the soft formulas. We evaluate our approach for the task of extracting biomedical events on the BioNLP 2013, 2011 and 2009 Genia shared task datasets. Our approach yields the best F1 score to date on the BioNLP'13 (53.61) and BioNLP'11 (58.07) datasets and the second-best F1 score to date on the BioNLP'09 dataset (58.16).

The threat of malware on mobile devices is gaining attention recently. It is important to provide security solutions to these devices before these threats cause widespread damage. However, mobile devices have severe resource constraints in terms of memory and power. Hence, even though there are well developed techniques for malware detection on the PC domain, it requires considerable effort to adapt these techniques for mobile devices. In this paper, we outline the considerations for malware detection on mobile devices and propose a signature based malware detection method. Specifically, we detail a signature matching algorithm that is well suited for use in mobile device scanning due to its low memory requirements. Additionally, the matching algorithm is shown to have high scanning speed which makes it unobtrusive to users. Our evaluation and comparison study with the well known Clam-AV scanner shows that our solution consumes less than 50% of the memory used by Clam-AV while maintaining a fast scanning rate.

Due to the extensive use of computer networks, new risks have arisen, and improving the speed and accuracy of security mechanisms has become a critical need. Although new security tools have been developed, the fast growth of malicious activities continues to be a pressing issue that creates severe threats to network security. Classical security tools such as firewalls are used as a first-line defense against security problems. However, firewalls do not entirely or perfectly eliminate intrusions. Thus, network administrators rely heavily on intrusion detection systems (IDSs) to detect such network intrusion activities. Machine learning (ML) is a practical approach to intrusion detection that, based on data, learns how to differentiate between abnormal and regular traffic. This paper provides a comprehensive analysis of some existing ML classifiers for identifying intrusions in network traffic. It also produces a new reliable dataset called GTCS (Game Theory and Cyber Security) that matches real-world criteria and can be used to assess the performance of the ML classifiers in a detailed experimental evaluation. Finally, the paper proposes an ensemble and adaptive classifier model composed of multiple classifiers with different learning paradigms to address the issue of the accuracy and false alarm rate in IDSs. Our classifiers show high precision and recall rates and use a comprehensive set of features compared to previous work.

No abstract