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2019-08-12
Karande, Vishal, Chandra, Swarup, Lin, Zhiqiang, Caballero, Juan, Khan, Latifur, Hamlen, Kevin.  2018.  BCD: Decomposing Binary Code Into Components Using Graph-Based Clustering. Proceedings of the 2018 on Asia Conference on Computer and Communications Security. :393-398.

Complex software is built by composing components implementing largely independent blocks of functionality. However, once the sources are compiled into an executable, that modularity is lost. This is unfortunate for code recipients, for whom knowing the components has many potential benefits, such as improved program understanding for reverse-engineering, identifying shared code across different programs, binary code reuse, and authorship attribution. A novel approach for decomposing such source-free program executables into components is here proposed. Given an executable, the approach first statically builds a decomposition graph, where nodes are functions and edges capture three types of relationships: code locality, data references, and function calls. It then applies a graph-theoretic approach to partition the functions into disjoint components. A prototype implementation, BCD, demonstrates the approach's efficacy: Evaluation of BCD with 25 C++ binary programs to recover the methods belonging to each class achieves high precision and recall scores for these tested programs.

2018-11-28
Jhumka, Arshad, Bradbury, Matthew.  2017.  Deconstructing Source Location Privacy-Aware Routing Protocols. Proceedings of the Symposium on Applied Computing. :431–436.

Source location privacy (SLP) is becoming an important property for a large class of security-critical wireless sensor network applications such as monitoring and tracking. Much of the previous work on SLP have focused on the development of various protocols to enhance the level of SLP imparted to the network, under various attacker models and other conditions. Others works have focused on analysing the level of SLP being imparted by a specific protocol. In this paper, we focus on deconstructing routing-based SLP protocols to enable a better understanding of their structure. We argue that the SLP-aware routing protocols can be classified into two main categories, namely (i) spatial and (ii) temporal. Based on this, we show that there are three important components, namely (i) decoy selection, (ii) use and routing of control messages and (iii) use and routing of decoy messages. The decoy selection technique imparts the spatial or temporal property of SLP-aware routing. We show the viability of the framework through the construction of well-known SLP-aware routing protocols using the identified components.