Information Security Commons^™

Enterprise Environment Modeling For Penetration Testing On The Openstack Virtualization Platform, Vincent Karovič Jr., Jakub Bartaloš, Vincent Karovič, Michal Greguš

Journal of Global Business Insights

The article presents the design of a model environment for penetration testing of an organization using virtualization. The need for this model was based on the constantly increasing requirements for the security of information systems, both in legal terms and in accordance with international security standards. The model was created based on a specific team from the unnamed company. The virtual working environment offered the same functions as the physical environment. The virtual working environment was created in OpenStack and tested with a Linux distribution Kali Linux. We demonstrated that the virtual environment is functional and its security testable. Virtualizing ...

Understanding Ransomware Trajectory To Create An Informed Prediction, Jacob D. Klusnick

University Honors Theses

Ransomware is a form of extortion in which digital files are rendered inaccessible until a ransom payment is made. Modern ransomware emerged in 2006 and its destructive influence has been expanding ever since. In recent years cybercriminals have evolved who they target, what computer systems they target, and how they infect those systems. Meanwhile, cybersecurity experts have modelled ransomware methods allowing them to innovate their defense techniques across three paradigms: recovery, detection, and prevention. Ultimately either ransomware attackers or ransomware defenders will dominate this ongoing conflict. A review of the literature indicates that the ransomware crime wave will likely be ...

Windows Kernel Hijacking Is Not An Option: Memoryranger Comes To The Rescue Again, Igor Korkin

Journal of Digital Forensics, Security and Law

The security of a computer system depends on OS kernel protection. It is crucial to reveal and inspect new attacks on kernel data, as these are used by hackers. The purpose of this paper is to continue research into attacks on dynamically allocated data in the Windows OS kernel and demonstrate the capacity of MemoryRanger to prevent these attacks. This paper discusses three new hijacking attacks on kernel data, which are based on bypassing OS security mechanisms. The first two hijacking attacks result in illegal access to files open in exclusive access. The third attack escalates process privileges, without applying ...

Deterring Intellectual Property Thieves: Algorithmic Generation Of Adversary-Aware Fake Knowledge Graphs, Snow Kang

Dartmouth College Undergraduate Theses

Publicly available estimates suggest that in the U.S. alone, IP theft costs our economy between $225 billion and $600 billion each year. In our paper, we propose combating IP theft by generating fake versions of technical documents. If an enterprise system has n fake documents for each real document, any IP thief must sift through an array of documents in an attempt to separate the original from a sea of fakes. This costs the attacker time and money - and inflicts pain and frustration on the part of its technical staff.

Leveraging a graph-theoretic approach, we created the Clique-FakeKG algorithm ...

Malware Classification With Bert, Joel Lawrence Alvares

Master's Projects

Malware Classification is used to distinguish unique types of malware from each other.

This project aims to carry out malware classification using word embeddings which are used in Natural Language Processing (NLP) to identify and evaluate the relationship between words of a sentence. Word embeddings generated by BERT and Word2Vec for malware samples to carry out multi-class classification. BERT is a transformer based pre- trained natural language processing (NLP) model which can be used for a wide range of tasks such as question answering, paraphrase generation and next sentence prediction. However, the attention mechanism of a pre-trained BERT model can ...

Fake Malware Classification With Cnn Via Image Conversion: A Game Theory Approach, Yash Sahasrabuddhe

Master's Projects

Improvements in malware detection techniques have grown significantly over the past decade. These improvements have resulted in better security for systems from various forms of malware attacks. However, it is also the reason for continuous evolution of malware which makes it harder for current security mechanisms to detect them. Hence, there is a need to understand different malwares and study classification techniques using the ever-evolving field of machine learning. The goal of this research project is to identify similarities between malware families and to improve on classification of malwares within different malware families by implementing Convolutional Neural Networks (CNNs) on ...

Fake Malware Opcodes Generation Using Hmm And Different Gan Algorithms, Harshit Trehan

Master's Projects

Malware, or malicious software, is a program that is intended to harm systems. In the past decade, the number of malware attacks have grown and, more importantly, evolved. Many researchers have successfully integrated cutting edge Machine Learning techniques to combat this ever present and growing threat to cyber and information security. One big challenge faced by many researchers is the lack of enough data to train machine learning models and specifically deep neural networks properly. Generative modelling has proven to be very efficient at generating synthesized data that can match the actual data distribution.

In this project, we aim to ...

Presentation Attack Detection In Facial Biometric Authentication, Hardik Kumar

Master's Projects

Biometric systems are referred to those structures that enable recognizing an individual, or specifically a characteristic, using biometric data and mathematical algorithms. These are known to be widely employed in various organizations and companies, mostly as authentication systems. Biometric authentic systems are usually much more secure than a classic one, however they also have some loopholes. Presentation attacks indicate those attacks which spoof the biometric systems or sensors. The presentation attacks covered in this project are: photo attacks and deepfake attacks. In the case of photo attacks, it is observed that interactive action check like Eye Blinking proves efficient in ...

Classifying Illegal Advertisements On The Darknet Using Nlp, Karan Shashin Shah

Master's Projects

The Darknet has become a place to conduct various illegal activities like child labor, contract murder, drug selling while staying anonymous. Traditionally, international and government agencies try to control these activities, but most of those actions are manual and time-consuming. Recently, various researchers developed Machine Learning (ML) approaches trying to aid in the process of detecting illegal activities. The above problem can benefit by using different Natural Language Processing (NLP) techniques. More specifically, researchers have used various classical topic modeling techniques like bag of words, N-grams, Term Frequency, Term Frequency Inverse Document Frequency (TF-IDF) to represent features and train machine ...

Defending Vehicles Against Cyberthreats: Challenges And A Detection-Based Solution, Qilin Liu

Master's Projects

The lack of concern with security when vehicular network protocols were designed some thirty years ago is about to take its toll as vehicles become more connected and smart. Today as demands for more functionality and connectivity on vehicles continue to grow, a plethora of Electronic Control Units (ECUs) that are able to communicate to external networks are added to the automobile networks. The proliferation of ECU and the increasing autonomy level give drivers more control over their vehicles and make driving easier, but at the same time they expand the attack surface, bringing more vulnerabilities to vehicles that might ...

Software-Based Side Channel Attacks And The Future Of Hardened Microarchitecture, Nathaniel Hatfield

Senior Honors Theses

Side channel attack vectors found in microarchitecture of computing devices expose systems to potentially system-level breaches. This thesis consists of a comprehensive report on current exploits of this nature, describing their fundamental basis and usage, paving the way to further research into hardware mitigations that may be utilized to combat these and future vulnerabilities. It will discuss several modern software-based side channel attacks, describing the mechanisms they utilize to gain access to privileged information. Attack vectors will be exemplified, along with applicability to various architectures utilized in modern computing. Finally, discussion of how future architectural changes must successfully harden chips ...

Analysis Of Theoretical And Applied Machine Learning Models For Network Intrusion Detection, Jonah Baron

Masters Theses & Doctoral Dissertations

Network Intrusion Detection System (IDS) devices play a crucial role in the realm of network security. These systems generate alerts for security analysts by performing signature-based and anomaly-based detection on malicious network traffic. However, there are several challenges when configuring and fine-tuning these IDS devices for high accuracy and precision. Machine learning utilizes a variety of algorithms and unique dataset input to generate models for effective classification. These machine learning techniques can be applied to IDS devices to classify and filter anomalous network traffic. This combination of machine learning and network security provides improved automated network defense by developing highly-optimized ...

How The Growth Of Technology Has Forced Accounting Firms To Put An Emphasis On Cybersecurity, Holden Halbach

Accounting Undergraduate Honors Theses

The advancement of technology has brought many changes to accounting firms. Computer applications such as Microsoft Excel have made calculators and physical spreadsheets obsolete. Then with the introduction of cloud computing employees can store, access, and exchange large amounts of data instantaneously from any location. These technological innovations have increased the accuracy and efficiency of firms substantially. However, this growth in technology has shown the importance of putting an emphasis on cybersecurity throughout the accounting industry. The emphasis placed on cybersecurity throughout accounting firms is more prevalent than any other industry. This is primarily because accounting firms not only deal ...

Data Forgery Detection In Automatic Generation Control: Exploration Of Automated Parameter Generation And Low-Rate Attacks, Yatish R. Dubasi

Computer Science and Computer Engineering Undergraduate Honors Theses

Automatic Generation Control (AGC) is a key control system utilized in electric power systems. AGC uses frequency and tie-line power flow measurements to determine the Area Control Error (ACE). ACE is then used by the AGC to adjust power generation and maintain an acceptable power system frequency. Attackers might inject false frequency and/or tie-line power flow measurements to mislead AGC into falsely adjusting power generation, which can harm power system operations. Various data forgery detection models are studied in this thesis. First, to make the use of predictive detection models easier for users, we propose a method for automated ...

Brave New World Reboot: Technology’S Role In Consumer Manipulation And Implications For Privacy And Transparency, Allie Mertensotto

Marketing Undergraduate Honors Theses

Most consumers are aware that our data is being obtained and collected through the use of our devices we keep in our homes or even on our person throughout the day. But, it is understated how much data is being collected. Conversations you have with your peers – in a close proximity of a device – are being used to tailor advertising. The advertisements you receive on your devices are uniquely catered to your individual person, due to the fact it consistently uses our data to produce efficient and personal ads. On the flip side, our government is also tapping into our ...

Security Fatigue And Its Effects On Perceived Password Strength Among University Students, Chase Carroll

Honors Theses

This study was performed with the goal of observing the effect, if any, that security fatigue has on students’ perceived strength of passwords. In doing so, it was hoped to find some correlation between the two that would help in establishing a measurable effect of the phenomenon in students. This could potentially aid organizational decision-makers, such as security policy writers and system admins, to make more informed decisions about implementing security measures. To achieve the goal of observing this fatigue and attempting to measure it, a survey was distributed to numerous students on the University of Tennessee at Chattanooga campus ...

A Framework To Detect The Susceptibility Of Employees To Social Engineering Attacks, Hashim H. Alneami

PhD Dissertations and Master's Theses

Social engineering attacks (SE-attacks) in enterprises are hastily growing and are becoming increasingly sophisticated. Generally, SE-attacks involve the psychological manipulation of employees into revealing confidential and valuable company data to cybercriminals. The ramifications could bring devastating financial and irreparable reputation loss to the companies. Because SE-attacks involve a human element, preventing these attacks can be tricky and challenging and has become a topic of interest for many researchers and security experts. While methods exist for detecting SE-attacks, our literature review of existing methods identified many crucial factors such as the national cultural, organizational, and personality traits of employees that enable ...

Achieving Differential Privacy And Fairness In Machine Learning, Depeng Xu

Theses and Dissertations

Machine learning algorithms are used to make decisions in various applications, such as recruiting, lending and policing. These algorithms rely on large amounts of sensitive individual information to work properly. Hence, there are sociological concerns about machine learning algorithms on matters like privacy and fairness. Currently, many studies only focus on protecting individual privacy or ensuring fairness of algorithms separately without taking consideration of their connection. However, there are new challenges arising in privacy preserving and fairness-aware machine learning. On one hand, there is fairness within the private model, i.e., how to meet both privacy and fairness requirements simultaneously ...

A Novel Dynamic Analysis Infrastructure To Instrument Untrusted Execution Flow Across User-Kernel Spaces, Jiaqi Hong, Xuhua Ding

Research Collection School Of Computing and Information Systems

Code instrumentation and hardware based event trapping are two primary approaches used in dynamic malware analysis systems. In this paper, we propose a new approach called Execution Flow Instrumentation (EFI) where the analyzer execution flow is interleaved with the target flow in user- and kernel-mode, at junctures flexibly chosen by the analyzer at runtime. We also propose OASIS as the system infrastructure to realize EFI with virtues of the current two approaches, however without their drawbacks. Despite being securely and transparently isolated from the target, the analyzer introspects and controls it in the same native way as instrumentation code. We ...

Privacy Is Infringed In Plain Sight And How To Dissapear, Zachary Taylor

Electronic Theses, Projects, and Dissertations

This culminating project explored how Amazon, Apple, Facebook, Google, and Microsoft infringe on their user's information privacy. Focus was on tools and techniques one can use to strengthen their information privacy. Privacy or information privacy was defined as the right to have some control over how your personal information is collected and used. This project will also introduce a verity of open-source tools and techniques that would help the unsuspected user to maintain their privacy.The questions asked were: what are some common techniques that Amazon, Apple, Facebook, Google, or Microsoft use to gain personal information?, At what cost ...