@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} } @conference {67, title = {Another free app: Does it have the right intentions?}, booktitle = {Privacy, Security and Trust (PST), 2014 Twelfth Annual International Conference on}, year = {2014}, month = {July}, abstract = {

Security and privacy holds a great importance in mobile devices due to the escalated use of smart phone applications (app). This has made the user even more vulnerable to malicious attacks than ever before. We aim to address this problem by proposing a novel framework to identify potential Android malware apps by extracting the intention and their permission requests. First, we constructed a dataset consisting of 1,730 benign apps along with 273 malware samples. Then, both datasets were subjected to source code extraction. From there on, we followed a two phase approach to identify potential malware samples. In phase 1, we constructed a machine learning model to group benign apps into different clusters based on their operations known as the task-intention. Once we trained the model, it was used to identify the task-intention of an Android app. Further, in this phase, we only used the benign apps to construct the task-intentions and none of the malware signatures were involved. Therefore, our approach does not use machine learning models to identify malware apps. Then, for each task-intention group, we extracted the permission-requests of the apps and constructed the probability mass functions (PMF). We named the shape of this PMF as Intention-Shape or I-Shape. In phase 2, we used the permission-requests, task-intentions and I-Shapes to identify potential malware apps. We compared the permission-requests of an unknown app with its corresponding I-Shape to identify the potential malware apps. Using this approach, we obtained an accuracy of 89\% in detecting potential malware samples. The novelty of our work is to perform potential malware identification without training any models with malware signatures, and utilization of I-Shapes to identify such potential malware samples. Our approach can be utilized to identify the safety of an app before it is installed as it performs static code analysis. Further, it can be utilized in pre-screening or multi-layer security sys- ems. It is also highly useful in screening malware apps when launching in Android markets.

}, keywords = {Android app task-intention identification, Android malware apps, Androids, benign apps, clusters, Feature extraction, Humanoid robots, I-Shape, intention-shape, invasive software, learning (artificial intelligence), machine learning model, malicious attacks, Malware, Mathematical model, mobile computing, permission-requests, PMF, probability mass functions, Shape, smart phone applications, smart phones, source code (software), source code extraction, static code analysis, Unsupervised learning}, doi = {10.1109/PST.2014.6890950}, author = {Fazeen, Mohamed and Ram Dantu} } @conference {17, title = {Behavior analysis of spam botnets}, booktitle = {2008 3rd International Conference on Communication Systems Software and Middleware and Workshops (COMSWARE {\textquoteright}08)}, year = {2008}, abstract = {

Compromised computers, known as bots, are the major source of spamming and their detection helps greatly improve control of unwanted traffic. In this work we investigate the behavior patterns of spammers based on their underlying similarities in spamming. To our knowledge, no work has been reported on identifying spam botnets based on spammerspsila temporal characteristics. Our study shows that the relationship among spammers demonstrates highly clustering structures based on features such as content length, time of arrival, frequency of email, active time, inter-arrival time, and content type. Although the dimensions of the collected feature set is low, we perform principal component analysis (PCA) on feature set to identify the features which account for the maximum variance in the spamming patterns. Further, we calculate the proximity between different spammers and classify them into various groups. Each group represents similar proximity. Spammers in the same group inherit similar patterns of spamming a domain. For classification into Botnet groups, we use clustering algorithms such as Hierarchical and K-means.We identify Botnet spammers into a particular group with a precision of 90\%.

}, keywords = {behavior pattern analysis, Botnets, Classification algorithms, clustering algorithm, Clustering algorithms, Correlation, Electronic mail, feature set, Filtering, information filtering, invasive software, maximum variance, pattern classification, pattern clustering, PCA, principal component analysis, spam filtering, Time frequency analysis, unsolicited e-mail, unwanted traffic control}, doi = {10.1109/COMSWA.2008.4554418}, author = {Husna, Husain and Phithakkitnukoon, Santi and Palla, Srikanth and Ram Dantu} } @article {18, title = {Fast Worm Containment Using Feedback Control}, journal = {IEEE Transactions on Dependable and Secure Computing}, volume = {4}, year = {2007}, pages = {119 - 136}, abstract = {

In a computer network, network security is accomplished using elements such as firewalls, hosts, servers, routers, intrusion detection systems, and honey pots. These network elements need to know the nature or anomaly of the worm a priori to detect the attack. Modern viruses such as Code Red, Sapphire, and Nimda spread quickly. Therefore, it is impractical if not impossible for human mediated responses to these fast-spreading viruses. Several epidemic studies show that automatic tracking of resource usage and control provides an effective method to contain the damage. In this paper, we propose a novel security architecture based on the control system theory. In particular, we describe a state-space feedback control model that detects and control the spread of these viruses or worms by measuring the velocity of the number of new connections an infected host makes. The mechanism{\textquoteright}s objective is to slow down a worm{\textquoteright}s spreading velocity by controlling (delaying) the number of new connections made by an infected host. A proportional and integral (PI) controller is used for a continuous control of the feedback loop. The approach proposed here has been verified in a laboratory setup, and we were able to contain the infection so that it affected less than 5 percent of the hosts. We have also implemented a protocol for exchanging control-specific information between the network elements. The results from the simulation and experimental setup combined with the sensitivity analysis demonstrate the applicability and accuracy of the approach.

}, keywords = {communication/networking and information technology, computer networks, Computer systems organization, control engineering computing, general, invasive software, network security, network-level security and protection, PI control, process control systems., proportional-integral controller, special-purpose and application-based systems, state feedback, state-space feedback control, state-space methods, telecommunication security, worm containment}, doi = {10.1109/TDSC.2007.1002}, author = {Ram Dantu and Cangussu, Jo{\~a}o and Patwardhan, Sudeep} }