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Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
Graph Classification With Kernels, Embeddings And Convolutional Neural Networks, Monica Golahalli Seenappa, Katerina Potika, Petros Potikas
Graph Classification With Kernels, Embeddings And Convolutional Neural Networks, Monica Golahalli Seenappa, Katerina Potika, Petros Potikas
Faculty Publications, Computer Science
In the graph classification problem, given is a family of graphs and a group of different categories, and we aim to classify all the graphs (of the family) into the given categories. Earlier approaches, such as graph kernels and graph embedding techniques have focused on extracting certain features by processing the entire graph. However, real world graphs are complex and noisy and these traditional approaches are computationally intensive. With the introduction of the deep learning framework, there have been numerous attempts to create more efficient classification approaches. We modify a kernel graph convolutional neural network approach, that extracts subgraphs (patches) …
Black Box Analysis Of Android Malware Detectors, Guruswamy Nellaivadivelu, Fabio Di Troia, Mark Stamp
Black Box Analysis Of Android Malware Detectors, Guruswamy Nellaivadivelu, Fabio Di Troia, Mark Stamp
Faculty Publications, Computer Science
If a malware detector relies heavily on a feature that is obfuscated in a given malware sample, then the detector will likely fail to correctly classify the malware. In this research, we obfuscate selected features of known Android malware samples and determine whether these obfuscated samples can still be reliably detected. Using this approach, we discover which features are most significant for various sets of Android malware detectors, in effect, performing a black box analysis of these detectors. We find that there is a surprisingly high degree of variability among the key features used by popular malware detectors.
Bootbandit: A Macos Bootloader Attack, Armen Boursalian, Mark Stamp
Bootbandit: A Macos Bootloader Attack, Armen Boursalian, Mark Stamp
Faculty Publications, Computer Science
Historically, the boot phase on personal computers left systems in a relatively vulnerable state. Because traditional antivirus software runs within the operating system, the boot environment is difficult to protect from malware. Examples of attacks against bootloaders include so‐called “evil maid” attacks, in which an intruder physically obtains a boot disk to install malicious software for obtaining the password used to encrypt a disk. The password then must be stored and retrieved again through physical access. In this paper, we discuss an attack that borrows concepts from the evil maid. We assume exploitation can be used to infect a bootloader …
Community Detection Via Neighborhood Overlap And Spanning Tree Computations, Ketki Kulkarni, Aris Pagourtzis, Katerina Potika, Petros Potikas, Dora Souliou
Community Detection Via Neighborhood Overlap And Spanning Tree Computations, Ketki Kulkarni, Aris Pagourtzis, Katerina Potika, Petros Potikas, Dora Souliou
Faculty Publications, Computer Science
Most social networks of today are populated with several millions of active users, while the most popular of them accommodate way more than one billion. Analyzing such huge complex networks has become particularly demanding in computational terms. A task of paramount importance for understanding the structure of social networks as well as of many other real-world systems is to identify communities, that is, sets of nodes that are more densely connected to each other than to other nodes of the network. In this paper we propose two algorithms for community detection in networks, by employing the neighborhood overlap metric …