Browsing by Author "Parameshwarappa, Pooja"
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Item Clustering Approaches for Anonymizing High-Dimensional Sequential Activity Data(2020-01-01) Parameshwarappa, Pooja; Chen, Zhiyuan; Koru, Gunes; Information Systems; Information SystemsIn the current IoT era, collection of activity data such as physical and daily activity data has become ubiquitous. Publishing activity data can facilitate personal and population health management and promote reproducible health care research. However, publishing such data can also bring high privacy risks including re-identification of individuals in the data set. Therefore, there is a growing need for anonymizing the data before publishing. One of the challenges in anonymizing sequential data such as activity data is its high-dimensional nature. Although existing techniques work sufficiently for cross-sectional data, they result in low run-time performance when applied directly to sequential data. In this research, we propose Multi-level Clustering (MC) based anonymization approaches that apply k-anonymity, differential privacy, and l-diversity privacy models. The proposed MC step improves the performance of the anonymization approaches by reducing the clustering time drastically. Results show that the proposed approaches in addition to being more efficient than the existing approaches, also preserve the utility of the data as much as the existing approaches.Item Extending Signature-based Intrusion Detection Systems With Bayesian Abductive Reasoning(2019-03-28) Ganesan, Ashwinkumar; Parameshwarappa, Pooja; Peshave, Akshay; Chen, Zhiyuan; Oates, TimEvolving cybersecurity threats are a persistent challenge for system administrators and security experts as new malwares are continually released. Attackers may look for vulnerabilities in commercial products or execute sophisticated reconnaissance campaigns to understand a target’s network and gather information on security products like firewalls and intrusion detection / prevention systems (network or host-based). Many new attacks tend to be modifications of existing ones. In such a scenario, rule-based systems fail to detect the attack, even though there are minor differences in conditions / attributes between rules to identify the new and existing attack. To detect these differences the IDS must be able to isolate the subset of conditions that are true and predict the likely conditions (different from the original) that must be observed. In this paper, we propose a probabilistic abductive reasoning approach that augments an existing rule-based IDS (snort [29]) to detect these evolved attacks by (a) Predicting rule conditions that are likely to occur (based on existing rules) and (b) able to generate new snort rules when provided with seed rule (i.e. a starting rule) to reduce the burden on experts to constantly update them. We demonstrate the effectiveness of the approach by generating new rules from the snort 2012 rules set and testing it on the MACCDC 2012 dataset.Item A Multi-level Clustering Approach for Anonymizing Large-Scale Physical Activity Data(2019-08-21) Parameshwarappa, Pooja; Chen, Zhiyuan; Koru, GüneṣPublishing physical activity data can facilitate reproducible health-care research in several areas such as population health management, behavioral health research, and management of chronic health problems. However, publishing such data also brings high privacy risks related to re-identification which makes anonymization necessary. One of the challenges in anonymizing physical activity data collected periodically is its sequential nature. The existing anonymization techniques work sufficiently for cross-sectional data but have high computational costs when applied directly to sequential data. This paper presents an effective anonymization approach, Multi-level Clustering based anonymization to anonymize physical activity data. Compared with the conventional methods, the proposed approach improves time complexity by reducing the clustering time drastically. While doing so, it preserves the utility as much as the conventional approaches.