Computer Sciences Commons^™

Load Balancing For Entity Matching Over Big Data Using Sorted Neighborhood, Yogesh Wattamwar

Master's Projects

Entity matching also known as entity resolution, duplicate identification, reference reconciliation or record linkage and is a critically important task for data cleaning and data integration. One can think of it, as the task of finding entities matching to the same entity in the real world. These entities can belong to a single source of data, or distributed data-sources. It takes structured data as an input and process includes comparison of that structured data (entity or database record) with entities present in the knowledge base. For large-scale entity, matching data has to go through some sequence of steps, which includes …

Relationship Based Entity Recommendation System, Rakhi Poonam Verma

Master's Projects

With the increase in usage of the internet as a place to search for information, the importance of the level of relevance of the results returned by search engines have increased by many folds in recent years. In this paper, we propose techniques to improve the relevance of results shown by a search engine, by using the kinds of relationships between entities a user is interested in. We propose a technique that uses relationships between entities to recommend related entities from a knowledge base which is a collection of entities and the relationships with which they are connected to other …

A Secured Cloud System Based On Log Analysis, Sindhusha Doddapaneni

Master's Projects

Now-a-days, enterprises’ acceptance over the Cloud is increasing but businesses are now finding issues related to security. Everyday, users store a large amount of data in the Cloud and user input may be malicious. Therefore, security has become the critical feature in the applications stored in the Cloud. Though there are many existing systems which provide us different encryption algorithms and security methods, there is still a possibility of attacks to applications and increasing data modifications. The idea behind this project is to find attacks and protect the applications stored in the Cloud using log analysis. The proposed solution detects …

Energy Efficiency And Quality Of Services In Virtualized Cloud Radio Access Network, Khushbu Mohta

Master's Projects

Cloud Radio Access Network (C-RAN) is being widely studied for soft and green fifth generation of Long Term Evolution - Advanced (LTE-A). The recent technology advancement in network virtualization function (NFV) and software defined radio (SDR) has enabled virtualization of Baseband Units (BBU) and sharing of underlying general purpose processing (GPP) infrastructure. Also, new innovations in optical transport network (OTN) such as Dark Fiber provides low latency and high bandwidth channels that can support C-RAN for more than forty-kilometer radius. All these advancements make C-RAN feasible and practical. Several virtualization strategies and architectures are proposed for C-RAN and it has …

A Completely Covert Audio Channel In Android, Sukanya Thakur

Master's Projects

Exfilteration of private data is a potential security threat against mobile devices. Previous research concerning such threats has generally focused on techniques that are only valid over short distances (NFC, Bluetooth, electromagnetic emanations, and so on). In this research, we develop and analyze an exfilteration attack that has no distance limitation. Specifically, we take advantage of vulnerabilities in Android that enable us to covertly record and exfilterate a voice call. This paper presents a successful implementation of our attack, which records a call (both uplink and downlink voice streams), and inaudibly transmits the recorded voice over a subsequent inaudible call, …

A Recommendation Engine Using Apache Spark, Swapna Kulkarni

Master's Projects

The volume of structured and unstructured data has grown at exponential scale in recent days. As a result of this rapid data growth, we are always inundated with plethora of choices in any product or service. It is very natural to get lost in the amazon of such choices and finding hard to make decisions. The project aims at addressing this problem by using entity recommendation. The two main aspects that the project concentrates on are implementing and presenting more accurate entity recommendations to the user and another is dealing with vast amount of data. The project aims at presenting …

Study Of Big Data Arhitecture Lambda Arhitecture, Jaideep Katkar

Master's Projects

The lambda architecture introduced by Marz is generic, scalable and fault-tolerant data processing architecture. It aims to satisfy the needs for a robust system that is faulttolerant, both against hardware failures and human mistakes, being able to serve a wide range of workloads and use cases. The architecture proposal decomposes the problem into three layers: a) the batch layer focuses on fault tolerance and optimizes for precise results b) the speed layer is optimized for short response-times and only takes into account the most recent data and c) the serving layer provides low latency views to the results of the …

Designing A Programming Contract Library For Java, Neha Rajkumar

Master's Projects

Programmers are now developing large and complex software systems, so it’s important to have software that is consistent, efficient, and robust. Programming contracts allow developers to specify preconditions, postconditions, and invariants in order to more easily identify programming errors. The design by contract principle [1] was first used in the Eiffel programming language [2], and has since been extended to libraries in many other languages. The purpose of my project is to design a programming contract library for Java. The library supports a set of preconditions, postconditions, and invariants that are specified in Java annotations. It incorporates contract checking for …

Metamorphic Java Engine, Sailee Choudhary

Master's Projects

Malware is a software program outlined to damage or perform other unwanted actions to a computer system. Metamorphic malware is a category of malignant software programs that has the ability to change its code as it propagates. A hidden Markov model (HMM) is a statistical model where the system is assumed to be a Markov process with unseen states. An HMM is based on the use of statistics to detect patterns, and hence in metamorphic virus detection. Previous work has been done in order to create morphing engines using LLVM-bytecode format. This project includes the creation of a morphing engine …

Collaboration Prototyper: Automatic Generation Of Prototypes From Uml Collaborations, Ramya Badthody Shenoy

Master's Projects

The thesis begins with a discussion of the use of designing versus prototyping in the initial stages of software development lifecycle. We present the idea of generating a working prototype directly from a UML analysis model to overcome time spent on creating a prototype. UML diagrams can be made to give sufficient information to generate code. Collaborations form the backbone of analysis models. A collaboration can be defined as a UML class diagram together with one or more sequence diagrams. Collaborations are the input of the Collaboration Prototyper (CP). CP transforms a collaboration into a Java prototype.

Improving The Accuracy And Robustness Of Self-Tuning Histograms By Subspace Clustering, Sai Kiran Padooru

Master's Projects

Self-tuning histograms are a type of histograms very popular these days, as they allow the usage of multidimensional datasets. The main advantage of them is that they have a low computational cost due to their capacity to understand the dataset. Also, they proposed a better approach as they stay up-to-date and have adaptability to query patterns. According to the above, many researchers have worked on improving the accuracy of these type of histograms, which has led to the use of subspace clustering methods as initialization values. Following this approach in this study, a self-tuning histogram code was developed with the …

Ssct Score For Malware Detection, Srividhya Srinivasan

Master's Projects

Metamorphic malware transforms its internal structure when it propagates, making detection of such malware a challenging research problem. Previous research considered a score based on simple substitution cryptanalysis, which was applied to the metamorphic detection problem. In this research, we analyze a new score based on a combined simple substitution and column transposition (SSCT) cryptanalysis. We show that this SSCT score significantly outperforms the simple substitution score— and other malware detection scores—in many cases.

Function Call Graph Score For Malware Detection, Deebiga Rajeswaran

Master's Projects

Metamorphic malware changes its internal structure with each infection, while maintaining its core functionality. Detecting such malware is a challenging research problem. Function call graph analysis has previously shown promise in detecting such malware. In this research, we analyze the robustness of a function call graph score with respect to various code morphing strategies. We also consider modifications of the score that make it more robust in the face of such morphing.

Clustering Web Concepts Using Algebraic Topology, Harleen Kaur Ahuja

Master's Projects

In this world of Internet, there is a rapid amount of growth in data both in terms of size and dimension. It consists of web pages that represents human thoughts. These thoughts involves concepts and associations which we can capture. Using mathematics, we can perform meaningful clustering of these pages. This project aims at providing a new problem solving paradigm known as algebraic topology in data science. Professor Vasant Dhar, Editor-In-Chief of Big Data (Professor at NYU) define data science as a generalizable extraction of knowledge from data. The core concept of semantic based search engine project developed by my …

Measuring Malware Evolution, Poonkodi Ponnambalam

Master's Projects

In this research, we simulate the effect of code evolution by applying a variety of code morphing strategies. Specifically, we consider code substitution, transposition, insertion, and deletion. We then analyze the effect of these code morphing strategies relative to a variety of malware scores that have been considered in previous research. Our goal is to gain a better understanding of the strengths and weaknesses of these various malware scoring techniques. This research should prove useful in designing more robust scores for detecting malware.

Graph Basesd Word Sense Disambiguation For Clinical Abbreviations Using Apache Spark, Veebha Padavkar

Master's Projects

Identification of the correct sense for an ambiguous word is one of the major challenges for language processing in all domains. Word Sense Disambiguation is the task of identifying the correct sense of an ambiguous word by referencing the surrounding context of the word. Similar to the narrative documents, clinical documents suffer from ambiguity issues that impact automatic extraction of correct sense from the document. In this project, we propose a graph-based solution based on an algorithm originally implemented by Osmar R. Zaine et al. for word sense disambiguation specifically focusing on clinical text. The algorithm makes use of proposed …

Load Balancing For Big Data Entity Matching Using Block Split, Akhilesh Kondra

Master's Projects

Entity Matching (EM) is a complex problem and has great impact on data quality. In EM we usually match all the combination of entity pairs using different similarity measures and judge if there is any match between entities. Mapreduce based parallel programing model can be used to match these entities. Even distribution of data into the map and reduce tasks will play vital role in the productivity of Mapreduce based programing model. If the dataset is large and has skewed data, then the distribution should be done effectively to achieve load balancing. In this paper, I have implemented an approach …

Extensible Authentication Protocol Vulnerabilities And Improvements, Akshay Baheti

Master's Projects

Extensible Authentication Protocol(EAP) is a widely used security protocol for Wireless networks around the world. The project examines different security issues with the EAP based protocols, the family of security protocols for Wireless LAN. The project discovers an attack on the subscriber identity module(SIM) based extension of EAP. The attack is a Denial-of-Service attack that exploits the error handling mechanism in EAP protocols. The project further proposes countermeasures for detection and a defense against the discovered attack. The discovered attack can be prevented by changing the protocol to delay the processing of protocol error messages.

Mining Concept In Big Data, Jingjing Yang

Master's Projects

To fruitful using big data, data mining is necessary. There are two well-known methods, one is based on apriori principle, and the other one is based on FP-tree. In this project we explore a new approach that is based on simplicial complex, which is a combinatorial form of polyhedron used in algebraic topology. Our approach, similar to FP-tree, is top down, at the same time, it is based on apriori principle in geometric form, called closed condition in simplicial complex. Our method is almost 300 times faster than FP-growth on a real world database using a SJSU laptop. The database …

Introducing Faceted Exception Handling For Dynamic Information Flow, Sri Tej Narala

Master's Projects

JavaScript is most commonly used as a part of web browsers, especially client- side scripts interacting with the user. JavaScript is also the source of many security problems, which includes cross-site scripting attacks. The primary challenge is that code from untrusted sources run with full privileges on the client side, thus lead- ing to security breaches. This paper develops information flow controls with proper exception handling to prevent violations of data confidentiality and integrity.

Faceted values are a mechanism to handle dynamic information flow security in a way that overcomes the limitations caused by dynamic execution, but previous work has …

Support Vector Machines And Metamorphic Malware Detection, Tanuvir Singh

Master's Projects

Metamorphic malware changes its internal structure with each infection, which makes it challenging to detect. In this research, we test several scor- ing techniques that have shown promise in metamorphic detection. We then perform a careful robustness analysis by employing morphing strategies that cause each score to fail. Finally, we show that combining scores using a Sup- port Vector Machine (SVM) yields results that are significantly more robust than we obtained using any of the individual scores.

Adding Syntax Parameters To The Sweet.Js Macro Library For Javascript, Vimal Kumar

Master's Projects

Lisp and Scheme have demonstrated the power of macros to enable programmers to evolve and craft languages. A macro is a rule or pattern that specifies how a certain input sequence should be mapped to an output sequence according to some defined procedure. Using a macro system a programmer can introduce new syntactic elements to the programming language. Macros found in a program are expanded by a macro expander and allow a programmer to enable code reuse. Mozilla Sweet.JS provides a way for developers to enrich their JavaScript code by adding new syntax to the language through the use of …

Cryptanalysis Of Classic Ciphers Using Hidden Markov Models, Rohit Vobbilisetty

Master's Projects

Cryptanalysis is the study of identifying weaknesses in the implementation of cryptographic algorithms. This process would improve the complexity of such algo- rithms, making the system secure.

In this research, we apply Hidden Markov Models (HMMs) to classic cryptanaly- sis problems. We show that with sufficient ciphertext, an HMM can be used to break a simple substitution cipher. We also show that when limited ciphertext is avail- able, using multiple random restarts for the HMM increases our chance of successful decryption.

Malware Detection Using Dynamic Analysis, Swapna Vemparala

Master's Projects

In this research, we explore the field of dynamic analysis which has shown promis- ing results in the field of malware detection. Here, we extract dynamic software birth- marks during malware execution and apply machine learning based detection tech- niques to the resulting feature set. Specifically, we consider Hidden Markov Models and Profile Hidden Markov Models. To determine the effectiveness of this dynamic analysis approach, we compare our detection results to the results obtained by using static analysis. We show that in some cases, significantly stronger results can be obtained using our dynamic approach.

A Comparison Of Clustering Techniques For Malware Analysis, Swathi Pai

Master's Projects

In this research, we apply clustering techniques to the malware detection problem. Our goal is to classify malware as part of a fully automated detection strategy. We compute clusters using the well-known �-means and EM clustering algorithms, with scores obtained from Hidden Markov Models (HMM). The previous work in this area consists of using HMM and �-means clustering technique to achieve the same. The current effort aims to extend it to use EM clustering technique for detection and also compare this technique with the �-means clustering.

Clustering Versus Svm For Malware Detection, Usha Narra

Master's Projects

Previous work has shown that we can effectively cluster certain classes of mal- ware into their respective families. In this research, we extend this previous work to the problem of developing an automated malware detection system. We first compute clusters for a collection of malware families. Then we analyze the effectiveness of clas- sifying new samples based on these existing clusters. We compare results obtained using �-means and Expectation Maximization (EM) clustering to those obtained us- ing Support Vector Machines (SVM). Using clustering, we are able to detect some malware families with an accuracy comparable to that of SVMs. One …

Optimization Of Scheduling And Dispatching Cars On Demand, Vu Tran

Master's Projects

Taxicab is the most common type of on-demand transportation service in the city because its dispatching system offers better services in terms of shorter wait time. However, the shorter wait time and travel time for multiple passengers and destinations are very considerable. There are recent companies implemented the real-time ridesharing model that expects to reduce the riding cost when passengers are willing to share their rides with the others. This model does not solve the shorter wait time and travel time when there are multiple passengers and destinations. This paper investigates how the ridesharing can be improved by using the …

Firefox Add-On For Metamorphic Javascript Malware Detection, Sravan Kumar Reddy Javaji

Master's Projects

With the increasing use of the Internet, malicious software has more frequently been designed to take control of users computers for illicit purposes. Cybercriminals are putting a lot of efforts to make malware difficult to detect. In this study, we demonstrate how the metamorphic JavaScript malware can effect a victim’s machine using a malicious or compromised Firefox add-on. Following the same methodology, we develop another add-on with malware static detection technique to detect metamorphic JavaScript malware.

Index Strategies For Efficient And Effective Entity Search, Huy T. Vu

Master's Projects

The volume of structured data has rapidly grown in recent years, when data-entity emerged as an abstraction that captures almost every data pieces. As a result, searching for a desired piece of information on the web could be a challenge in term of time and relevancy because the number of matching entities could be very large for a given query. This project concerns with the efficiency and effectiveness of such entity queries. The work contains two major parts: implement inverted indexing strategies so that queries can be searched in minimal time, and rank results based on features that are independent …

Cheating Detection In Online Examinations, Gaurav Kasliwal

Master's Projects

In this research, we develop and analyze a tool that monitor student browsing activity during online examination. Our goal is to detect cheating in real time. In our design, a server capture packets using KISMET and detects cheating based on either a whitelist or blacklist of URLs. We provide implementation details and give experimental results, and we analyze various attack strategies. Finally, we show that the system is practical and lightweight in comparison to other available tools.