@conference {100, title = {Unintentional bugs to vulnerability mapping in Android applications}, booktitle = {Intelligence and Security Informatics (ISI), 2015 IEEE International Conference on}, year = {2015}, month = {May}, abstract = {

The intention of an Android application, determined by the source code analysis is used to identify potential maliciousness in that application (app). Similarly, it is possible to analyze the unintentional behaviors of an app to identify and reduce the window of vulnerabilities. Unintentional behaviors of an app can be any developmental loopholes such as software bugs overlooked by a developer or introduced by an adversary intentionally. FindBugsTM and Android Lint are a couple of tools that can detect such bugs easily. A software bug can cause many security vulnerabilities (known or unknown) and vice-versa, thus, creating a many-to-many mapping. In our approach, we construct a matrix of mapping between the bugs and the potential vulnerabilities. A software bug detection tool is used to identify a list of bugs and create an empirical list of the vulnerabilities in an app. The many-to-many mapping matrix is obtained by two approaches - severity mapping and probability mapping. These mappings can be used as tools to measure the unknown vulnerabilities and their strength. We believe our study is the first of its kind and it can enhance the security of Android apps in their development phase itself. Also, the reverse mapping matrix (vulnerabilities to bugs) could be used to improve the accuracy of malware detection in Android apps.

}, keywords = {Android (operating system), Android applications, Android Lint, Androids, bugs, Computer bugs, Conferences, development phase, developmental loopholes, FindBugsTM, Humanoid robots, Indexes, invasive software, malware detection, many-to-many mapping matrix, mapping, matrix algebra, probability, probability mapping, program debugging, reverse mapping matrix, Security, security vulnerabilities, severity mapping, Software, software bug detection tool, software bugs, software tools, source code (software), source code analysis, unintentional behavior analysis, unintentional bugs, vulnerability, vulnerability mapping}, doi = {10.1109/ISI.2015.7165966}, author = {Bajwa, Garima and Fazeen, Mohamed and Ram Dantu and Tanpure, Sonal} } @article {282, title = {Socioscope: Human Relationship and Behavior Analysis in Social Networks}, journal = {IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans}, volume = {41}, year = {2011}, month = {Nov}, pages = {1122-1143}, abstract = {

In this paper, we propose a socioscope model for social-network and human-behavior analysis based on mobile-phone call-detail records. Because of the diversity and complexity of human social behavior, no one technique will detect every attribute that arises when humans engage in social behaviors. We use multiple probability and statistical methods for quantifying social groups, relationships, and communication patterns and for detecting human-behavior changes. We propose a new index to measure the level of reciprocity between users and their communication partners. This reciprocity index has application in homeland security, detection of unwanted calls (e.g., spam), telecommunication presence, and product marketing. For the validation of our results, we used real-life call logs of 81 users which contain approximately 500 000 h of data on users{\textquoteright} location, communication, and device-usage behavior collected over eight months at the Massachusetts Institute of Technology (MIT) by the Reality Mining Project group. Also, call logs of 20 users collected over six months by the University of North Texas (UNT) Network Security team are used. The MIT and UNT data sets contain approximately 5000 callers. The experimental results show that our model is effective.

}, keywords = {behavioural sciences computing, Change points, communication pattern, Data models, human relationship analysis, human social behavior, human-behavior change detection, Humans, IEEE, Massachusetts Institute of Technology, Mobile handsets, probability, Reality Mining Project group, reciprocity index, Social factors, social group, social groups, social network analysis, Social network services, social networking (online), Social networks, social relationship, social relationships, social sciences computing, socioscope, Statistical analysis, statistical method, University of North Texas}, issn = {1083-4427}, doi = {10.1109/TSMCA.2011.2113335}, author = {Zhang, Huiqi and Ram Dantu and Cangussu, Jo{\~a}o} } @article {26, title = {An Impatient Evolutionary Algorithm With Probabilistic Tabu Search for Unified Solution of Some NP-Hard Problems in Graph and Set Theory via Clique Finding}, journal = {IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics)}, volume = {38}, year = {2008}, pages = {645 - 666}, abstract = {

Many graph- and set-theoretic problems, because of their tremendous application potential and theoretical appeal, have been well investigated by the researchers in complexity theory and were found to be NP-hard. Since the combinatorial complexity of these problems does not permit exhaustive searches for optimal solutions, only near-optimal solutions can be explored using either various problem-specific heuristic strategies or metaheuristic global-optimization methods, such as simulated annealing, genetic algorithms, etc. In this paper, we propose a unified evolutionary algorithm (EA) to the problems of maximum clique finding, maximum independent set, minimum vertex cover, subgraph and double subgraph isomorphism, set packing, set partitioning, and set cover. In the proposed approach, we first map these problems onto the maximum clique-finding problem (MCP), which is later solved using an evolutionary strategy. The proposed impatient EA with probabilistic tabu search (IEA-PTS) for the MCP integrates the best features of earlier successful approaches with a number of new heuristics that we developed to yield a performance that advances the state of the art in EAs for the exploration of the maximum cliques in a graph. Results of experimentation with the 37 DIMACS benchmark graphs and comparative analyses with six state-of-the-art algorithms, including two from the smaller EA community and four from the larger metaheuristics community, indicate that the IEA-PTS outperforms the EAs with respect to a Pareto-lexicographic ranking criterion and offers competitive performance on some graph instances when individually compared to the other heuristic algorithms. It has also successfully set a new benchmark on one graph instance. On another benchmark suite called Benchmarks with Hidden Optimal Solutions, IEA-PTS ranks second, after a very recent algorithm called COVER, among its peers that have experimented with this suite.

}, keywords = {Algorithm design and analysis, Clique finding, combinatorial complexity, Complexity theory, computational complexity, evolutionary algorithm, evolutionary algorithms (EAs), evolutionary computation, genetic algorithm, Genetic algorithms, graph theory, Heuristic algorithms, maximum clique finding, metaheuristic global optimization method, NP-hard problem, NP-hard problems in set and graph theory, optimisation, Pareto analysis, Performance analysis, probabilistic tabu search, probability, search problems, set theory, simulated annealing}, doi = {10.1109/TSMCB.2008.915645}, author = {Guturu, Parthasarathy and Ram Dantu} }