• 5G Networks Security: Attack Detection Using the J48 and the Random Forest Tree Classifiers

      Kholidy, Hisham A.; Steele II, Bruce; Kholidy, Hisham A.; Advisor (SUNY Polytechnic Institute, 2020)
      5G is the next generation of cellular networks succeeding and improving upon the last generation of 4G Long Term Evolution (LTE) networks. With the introduction of 5G comes significant improvements over the previous generation with the ability to support new and emerging technologies in addition to the growth in the number of devices. The purpose of this report is to give a broad overview of what 5G encompasses including the architecture, underlying technology, advanced features, use cases/applications, and security, and to evaluate the security of this new networks using existing machine learning classification techniques such as The J48 Tree Classifier and the Random Forest tree classifier. The evaluation is based on the UNSW-NB15 dataset that was created at the Cyber Range Lab of the Australian Centre for Cyber Security (ACCS) at the University of New South Wales. Since 5G datasets have yet to have been created, there is no publicly available dataset for the 5G systems. However, While the UNSW-NB15 dataset is built using a standard wireless computer network, we will use it to simulate the device-to-device (D2D) connections that 5G will support. In the case with the UNSW dataset, the J48 tree classifier fits more accurately than the Random Forest classifier. The J48 tree classifier achieved an 86.422% of correctly classified instances. On the other hand, the Random Forest tree classifier achieved 85.8451% of correctly classified instances.
    • Accessible Formal Methods: A Study of the Java Modeling Language

      Rawding, Michael; Andriamanalimanana, Bruno; Advisor; Spetka, Scott; Reviewer; Vishwanathan, Roopa; Reviewer (2017-04-17)
      While formal methods offer the highest level of confidence that software behaves as intended, they are notoriously difficult to use. The Java Modeling Language and the associated OpenJML tool aim to make formal specification and verification more accessible to Java developers. This report gives an overview of JML and assesses its current status and usability. Though many common Java features have been implemented, lack of standard library support is identified as an obstacle to using JML effectively. To help address that problem, this report documents the process of adding support for a new library to OpenJML.
    • Aligning the SUNY Poly NCS Program with Nationally Recognized Accreditation

      Cook, John; Marsh, John; Adviser; Hash, Larry; Reviewer; Bull, Ronny; Reviewer (2015-01-29)
      This document is an exploration into what types of curriculum changes must be made to accommodate accreditation. In the review of program accrediting bodies, none is more authoritative or more appropriate than the Accreditation Board for Engineering and Technology (ABET). In ABET’s requirements for accreditation, computing related programs are defined and delineated. On further exploration, it can be seen that the Association for Computing Machinery (ACM) has driven the development of those definitions. The ACM further defines goals and objectives for these disciplines, as well as curriculum models. When reviewing other accreditations, not only are these ACM definitions recognized within those accreditations, goal and outcome alignment is also present. This ‘goal and outcome’ methodology is also present in institution level accreditations that SUNY Poly must comply with. After reviewing the ACM program definitions and comparing them to the NCS program, it is concluded that NCS most closely resembles an ACM IT defined program. This leads to the recommendation of adopting and aligning with ACM IT program guidelines, which provides solutions to multiple program and institution requirements as well as creating a solid pathway to accreditation.
    • An Analysis of a Signature-based Approach for an Intrusion Detection System in a Wireless Body Area Network (WBAN) using Data Mining Techniques

      Kholidy, Hisham A.; Medina, Serene Elisabeth; Kholidy, Hisham A.; Advisor (SUNY Polytechnic Institute, 2020)
      Wireless Body Area Networks (WBANs) use biosensors worn on, or in the human body, which collect and monitor a patient’s medical condition. WBANs have become increasingly more beneficial in the medical field by lowering healthcare cost and providing more useful information that medical professionals can use for a more accurate, and faster diagnosis. Due to the fact that the data collected from a WBAN is transmitted over a wireless network, there are several security concerns involved. This research looks at the various attacks, and concerns involved with WBANs. A real physiological dataset, consisting of ECG signals obtained from a 25-year-old male, was used in this research to test accuracy of various decision tree classifiers. The Weka software was used to analysis the accuracy and detection rate results of this dataset in its original form, versus a reduced dataset consisting of less, more important attributes. The results concluded that the use of decision tree classifiers using data mining, is an efficient way to test the increased accuracy on a real dataset obtained from a WBAN once it has been altered. The original dataset produced results where the ROC curve ranged from 0.313 (31%) to 0.68 (68%), meaning their accuracy is not very high and the detection rate is low. Once an attribute selection feature was used on the dataset, the newly reduced set showed ROC curves ranging from 0.68 (68%) to 0.969 (97%) amongst the three classes. As a result, decision tree models were much more accurate with a higher detection rate when used on a real dataset that was reduced to function better as a detector for a WBAN.
    • Applicability of the Julia Programming Language to Forward Error-Correction Coding in Digital Communications Systems

      Quinn, Ryan; Andriamanalimanana, Bruno R.; Advisor; Sengupta, Saumendra; Reviewer; Spetka, Scott; Reviewer (2018-05)
      Traditionally SDR has been implemented in C and C++ for execution speed and processor efficiency. Interpreted and high-level languages were considered too slow to handle the challenges of digital signal processing (DSP). The Julia programming language is a new language developed for scientific and mathematical purposes that is supposed to write like Python or MATLAB and execute like C or FORTRAN. Given the touted strengths of the Julia language, it bore investigating as to whether it was suitable for DSP. This project specifically addresses the applicability of Julia to forward error correction (FEC), a highly mathematical topic to which Julia should be well suited. It has been found that Julia offers many advantages to faithful implementations of FEC specifications over C/C++, but the optimizations necessary to use FEC in real systems are likely to blunt this advantage during normal use. The Julia implementations generally effected a 33% or higher reduction in source lines of code (SLOC) required to implement. Julia implementations of FEC algorithms were generally not more than 1/3 the speed of mature C/C++ implementations.While Julia has the potential to achieve the required performance for FEC, the optimizations required to do so will generally obscure the closeness of the implementation and specification. At the current time it seems unlikely that Julia will pose a serious challenge to the dominance of C/C++ in the field of DSP.
    • BGP Routing Protocol

      Parasa, Sai Kiran; Hash, Larry; Advisor (2016-08)
      Border Gateway Protocol is the protocol which makes the Internet work. It is used at the Service provider level which is between different Autonomous Systems (AS). An Autonomous System is a single organization which controls the administrative part of a network. Routing with in an Autonomous System is called as Intra-Autonomous routing and routing between different Autonomous Systems is called as Inter-Autonomous System routing. The routing protocols used within an Autonomous System are called Interior Gateway Protocols (IGP) and the protocols used between the Autonomous Systems are called Exterior Gateway Protocols. Routing Information Protocol (RIP), Open Short Path First (OSPF) and Enhanced Interior Gateway Routing Protocol (EIGRP) are the examples for IGP protocols and Border Gateway Protocol (BGP) is the example for EGP protocols. Every routing protocol use some metric to calculate the best path to transfer the routing information. BGP rather than using a particular metric, it uses BGP attributes to select the best path. Once it selects the best path, then it starts sending the updates in the network. Every router implementing BGP in the network, configures this best path in its Routing Information Base. Only one best route is selected and forwarded to the whole network. [17] Due to the tremendous increase in the size of the internet and its users, the convergence time during link failure in the protocol is very high.
    • Botnet Campaign Detection on Twitter

      Fields, Jeremy; Sengupta, Saumendra; Adviser; White, Joshura; Reviewer; Spetka, Scott; Reviewer (2016-08)
      The goal of this thesis is to investigate and analyze botnet activity on social media networks. We first start by creating an algorithm and scoring method for “likely bots,” and analyze them in conjunction with their neighboring messages to determine whether there is a likely group of bots, or botnet. Chapters 1 & 2 cover the overview of the work, and the previous research done by others. Multiple datasets were collected from Twitter, over different time frames, including random samples, and targeted topics. Chapters 3 & 4 cover the methodology and results of the approach using these datasets. The method is shown to have high accuracy.
    • A Case Study on Apache HBase

      Nalla, Rohit Reddy; Sengupta, Sam; Adviser; Novillo, Jorge; Reviewer; Rezk, Mohamed; Reviewer (2015-05-16)
      Apache HBase is an open-source, non-relational and a distributed data base system built on top of HDFS (Hadoop Distributed File system). HBase was designed post Google’s Big table and it is written in Java. It was developed as a part of Apache’s Hadoop Project. It provides a kind of fault – tolerant mechanism to store minor amounts of non-zero items caught within large amounts of empty items. HBase is used when we require real-time read/write access to huge data bases. HBase project was started by the end of 2006 by Chad Walters and Jim Kellerman at Powerset.[2] The main purpose of HBase is to process large amounts of data. Mike Cafarella worked on code of the working system initially and later Jim Kellerman carried it to the next stage. HBase was first released as a part of Hadoop 0.15.0 in October 2007[2]. The project goal was holding of very large tables like billions of rows X millions of columns. In May 2010, HBase advanced to a major project and it became an Apache Top Level Project. Several applications like Adobe, Twitter, Yahoo, Trend Micro etc. use this data base. Social networking sites like Facebook have implemented its messenger application using HBase. This document helps us to understand how HBase works and how is it different from other data bases. This document highlights about the current challenges in data security and a couple of models have been proposed towards the security and levels of data access to overcome the challenges. This document also discusses the workload challenges and techniques to overcome. Also an overview has been given on how HBase has been implemented in real time application Facebook messenger app.
    • Cloud-SCADA Penetrate: Practical Implementation for Hacking Cloud Computing and Critical SCADA Systems

      Kholidy, Hisham A. (SUNY Polytechnic Institute, 2020)
      In this report, we discuss some of our hacking and security solutions that we developed at our Advanced Cybersecurity Research Lab (ACRL). This report consists of the following five main experimental packages: 1) Exploiting the cloud computing system using a DDoS attack and developing a distributed deployment of a cloud based Intrusion Detection System (IDS) solution. 2) Hacking SCADA systems components. 3) Hacking Metasploitable machines. 4) Hacking Windows 7 system. 5) Windows Post Exploitation.
    • Comparison of Network Switch Architectures by CISCO

      Vemula, Veera Venkata Satyanarayana; Hash, Larry; Advisor (2016-02-01)
      This project is targeted to compare two major switching architectures provided by CISCO. CISCO is a network device manufacturer who has contributed to networking world by inventing many networking protocols which are used to improve the network performance and network health. In this document the switching architectures CATALYST and NEXUS are compared. All the available features in each architectures are listed and working of the supported protocols is explained in detail. The document also considers three network scenarios and explains which architecture is best suited and explains why in detail.
    • Cyber Security Advantages of Optical Communications in SATCOM Networks

      Kholidy, Hisham A.; Baker, Cameron; Kholidy, Hisham A.; Advisor (SUNY Polytechnic Institute, 2020-12)
      Space-based communications, whether it is ground-to-space or inter-satellite communications, have so far been primarily within the RF spectrum. With the increase in space missions and the need for larger amounts of data being sent to and from satellites, the near infrared or optical spectrum has started to become more widely used instead of RF. Higher bandwidth is not the only advantage of using optics for communications over RF, there is also an inherent security advantage as well. Currently, there is far too little enforcement of security standards for space communications networks, and the use of RF only worsens the problem due to its very large beam spread when compared to optics. This paper will seek to prove that optics is a far more superior technology to be used for space communications networks from a security standpoint as well as providing an increase in available bandwidth. These points will be proven by first introducing the technology by examining current Free Space Optics (FSO) systems and space optics systems being provided by manufacturers. Secondly, this paper will discuss the current state of space communications security, and issues space communications networks are facing using RF with the recent advancement into low-cost SmallSat operations that threaten existing space vehicles, and the lack of standard security practices within these networks. Lastly, this paper will provide evidence into why optics communications can improve the security of spaced based communications due to its lower beam spread and the ability to incorporate quantum key distribution into the communications channel.
    • Data Mining and Bi Data Warehousing Based Implementation for a Random Film Studio

      Bonthi, Sneha; Andriamanalimanana, Bruno; Adviser; Rezk, Mohamed; Reviewer; Reale, Michael; Reviewer (2016-12-01)
      The purpose of this report is to study a dataset of movies and analyse the possibility and feasibility of implementing a data warehousing or a data mining application to improve analytics and decision making. The project report talks about the raw data originating from the data collection centres and box offices which can be modelled and transformed into a specific format and structure that would help the business analysts in identifying patterns and trends so as to take important business decisions. The report explores the benefits of extracting, transforming and loading this raw data into a dimensional model. According to the proposed implementation, one can create a reporting layer to perform aggregations and grouping them by various attributes like date, genre, actor and country and present them using dashboards and reports to enable better decision making. This single point of data, which is the result of data mining activity, can be shared and brainstorming sessions can then be carried out to infer priceless market information and effectively utilize time and efforts to maximize profits.
    • Data Mining: Privacy Preservation in Data Mining Using Perturbation Techniques

      Patel, Nikunjkumar; Sengupta, Sam; Adviser; Andriamanalimanana, Bruno; Reviewer; Novillo, Jorge; Reviewer (2015-05-06)
      In recent years, data mining has become important player in determining future business strategies. Data mining helps identifying patterns and trends from large amount of data, which can be used for reducing cost, increasing revenue and many more. With increased use of various data mining technologies and larger storage devices, amount of data collected and stored is significantly increased. This data contains personal information like credit card details, contact and residential information, etc. All these reasons have made it inevitable to concentrate on privacy of the data. In order to alleviate privacy concerns, a number of techniques have recently been proposed to perform the data mining in privacy preserving way. This project briefs about various data mining models and explains in detail about perturbation techniques. Main objective of this project is to achieve two things. First, preserve the accuracy of the data mining models and second, preserve the privacy of the original data. The discussion about transformation invariant data mining models has shown that multiplicative perturbations can theoretically guarantee zero loss of accuracy for a number of models.
    • De-anonymizing Social Network Neighborhoods Using Auxiliary and Semantic Information

      Morgan, Steven Michael; Novillo, Jorge; Adviser; Andriamanalimanana, Bruno; Reviewer; Reale, Michael; Reviewer (2015-12-11)
      The increasing popularity of social networks and their progressively more robust uses provides an interesting intersection of data. Social graphs have been rigorously studied for de-anonymization. Users of social networks will provide feedback to pages of interest and will create a vibrant profile. In addition to user interests, textual analysis provides another feature set for users. The user profile can be viewed as a classical relational dataset in conjunction with graph data. This paper uses semantic information to improve the accuracy of de-anonymizing social network data.
    • The Deep Space Network - A Technology Case Study and What Improvements to the Deep Space Network are Needed to Support Crewed Missions to Mars?

      Falke, Prasad; Hash, Larry; Advisor; Marsh, John; Reviewer; White, Joshua; Reviewer; Climek, David; Reviewer; Kwiat, Kevin; Reviewer (2017-05-28)
      The purpose of this thesis research is to find out what experts and interested people think about Deep Space Network (DSN) technology for the crewed Mars mission in the future. The research document also addresses possible limitations which need to be fix before any critical missions. The paper discusses issues such as: data rate, hardware upgrade and new install requirement and a budget required for that, propagation delay, need of dedicated antenna support for the mission and security constraints. The Technology Case Study (TCS) and focused discussion help to know the possible solutions and what everyone things about the DSN technology. The public platforms like Quora, Reddit, StackExchange, and Facebook Mars Society group assisted in gathering technical answers from the experts and individuals interested in this research.
    • Detection of Brain Tumor in Magnetic Resonance Imaging (MRI) Images using Fuzzy C-Means and Thresholding

      Andriamanalimanana, Bruno; Kalakuntla, Shashank; Andriamanalimanana, Bruno R.; First Reader; Novillo, Jorge E.; Second Reader; Spetka, Scott; Third Reader (SUNY Polytechnic Institute, 2020-08)
      Although many clinical experts or radiologists are well trained to identify tumors and other abnormalities in the brain, the identification, detection and segmentation of the affected area in the brain is observed to be a tedious and time consuming task. MRI has been a conventional and resultant image processing technique to visualize structures of the human body. It is very difficult to visualize abnormal structures of the brain using simple imaging techniques. MRI technique uses many imaging modalities that scan and capture the internal structure of the human brain. Even with the use of these techniques, it is a difficult and tedious task for a human eye to be always sophisticated in detecting brain tumors from these images. With emerging technology, we can provide a way to ease the process of detection. This project focuses on identification of brain tumor in MR images, it involves in removing noise using noise removal technique AMF followed by enhancing the images using Balance Enhancement Contrast technique (BCET).Further, image segmentation is performed using fuzzy c-means and finally the segmented images are produced as an input to a canny edge detection resulting with the tumor image. This report entices the approach, design, and implementation of the application and finally the results. I have tried implementing/developing this application in Python. The Jupyter notebook provides a block simulation for the entire flow of the project.
    • An Empirical Wi-Fi Intrusion Detection System

      Kholidy, Hisham A.; Basnet, Diwash Bikram; Kholidy, Hisham A.; Advisor (SUNY Polytechnic Institute, 2020-05)
      Today, the wireless network devices are growing rapidly, and it is of utmost importance for securing those devices. Attackers or hackers use new methods and techniques to trick the system and steal the most important data. Intrusion Detection Systems detect the attacks by inspecting the network traffics or logs. The work demonstrated the effectiveness of detecting the attacks using machine learning techniques on the AWID dataset, which is produced from real wireless network logging. The author of the AWID dataset may have used several supervised learning models to successfully detect the intrusions. In this paper, we propose a newer approach for intrusion detection model based on dense neural networks, and long short-term memory networks (LSTM) and evaluate the model against the AWID-CLS-R subset. To get the best results from the model, we applied feature selection by replacing the unknown data with the value of “none”, getting rid of all repeated values, and kept only the important features. We did preprocess and feature scaling of both training and testing dataset, additional we also change the 2-dimensional to the 3- dimensional array because LSTM takes an input of 3-dimensional array, and later we used flatten layers to change into a 2-dimensional array for output. A comprehensive evaluation of DNN and LSTM networks are used to classify and predict the attacks and compute the precision, recall, and F1 score. We perform binary classification and multiclass classification on the dataset using neural networks and achieve accuracy ranging from 86.70 % to 96.01%.
    • Employee Collaboration in Sharepoint

      Vempati, Sai Sandeep Soumithri; Chiang, Chen-Fu; Adviser; Novillo, Jorge; Reviewer; Rezk, Mohamed; Reviewer (2016-12-01)
      This project aims at developing a portal for a company’s internal needs that include leave portal, a pre-sales dashboard and a document sharing list for the employees in SharePoint Online. SharePoint Online is web based Content Management System (CMS) provided by Microsoft. Microsoft introduced SharePoint in 2001 which was an instant winner. It had all the features that are needed for storage and collaboration. SharePoint later on evolved into two major versions, namely, On-premise and Cloud version. SharePoint the cloud version proved to be a feasible CMS for start-ups and small companies. As the usage of SharePoint Online has minimised the burden maintenance of servers and administration more companies started using SharePoint. The utility of SharePoint has caught the attention of many companies lately. It has scaled up to, 75000 organisations saving 160 million users [8]. The usage of SharePoint made companies develop portals that are interactive and act as platforms for collaboration and exchange of information. The workflow automation provided by SharePoint helps in simplifying the business process management. Web technologies can be used to develop the portal in a user friendly and responsive manner. In this project, a portal is developed that mainly has three functionalities – a leave application platform, a dashboard for Presales and a list that helps sharing of information. The leave application feature is based on the workflow automation service provided by SharePoint in which the user can request concerned manager for a leave approval. The whole process of approval is automated in the portal. The Presales dashboard option helps in viewing data related to projects that can be used to develop reports by the Presales team of a company. The data is shown in various forms suitable for easy understanding using web parts in the dashboard. A list that demonstrates file approval is included in the portal.
    • Enhancing the Effectiveness of Software Test Automation

      Jansing, David; Novillo, Jorge; Adviser; Cavallo, Roger; Reviewer; Spetka, Scott; Reviewer (2015-12-01)
      Effective software testing can save money and effort by catching problems before they make it very far through the software development process. It is known that the longer a defect remains undetected, the more expensive it is to fix. Testing is, therefore a critical part of the development process. It can also be expensive and labor intensive, particularly when done by hand. It is estimated that the total effort testing software consumes at least half of a project’s overall labor. Automation can make much of the testing an organization does more accurate and cheaper than merely putting several people in a room and having them run tests from a paper script. It also frees the testing staff to do more specific and in-­‐depth testing than would otherwise be possible. This paper focuses mainly on software test automation techniques and how automation can enhance the efficiency of a software team as well as the quality of the final product.
    • Evaluating Variant Deep Learning and Machine Learning Approaches for the Detection of Cyberattacks on the Next Generation 5G Systems

      Kholidy, Hisham A.; Borgesen, Michael E.; Kholidy, Hisham A.; Advisor (SUNY Polytechnic Institute, 2020)
      5G technology promises to completely transform telecommunication networks, introducing a wealth of benefits such as faster download speeds, lower download times, low latency, high network capacity. These benefits will pave the way for additional new capabilities and support connectivity for applications like smart homes and cities, industrial automation, autonomous vehicles, telemedicine, and virtual/augmented reality. However, attackers use these resources in their advantages to speed up the attacking process. This report evaluates four different machine learning and deep learning approaches namely the Naïve Bayes model, the logistic regression model, the decision tree model, and the random forest model. The performance evaluation and the validation of these approaches are discussed in details in this report.