Physical Sciences and Mathematics Commons^™

Passive Physical Layer Distinct Native Attribute Cyber Security Monitor, Christopher M. Rondeau, Michael A. Temple, Juan Lopez Jr, J. Addison Betances

AFIT Patents

A method for cyber security monitor includes monitoring a network interface that is input-only configured to surreptitiously and covertly receive bit-level, physical layer communication between networked control and sensor field devices. During a training mode, a baseline distinct native attribute (DNA) fingerprint is generated for each networked field device. During a protection mode, a current DNA fingerprint is generated for each networked field device. The current DNA fingerprint is compared to the baseline DNA fingerprint for each networked field device. In response to detect at least one of RAA and PAA based on a change in the current DNA fingerprint …

From Asset Flow To Status, Action And Intention Discovery: Early Malice Detection In Cryptocurrency, Ling Cheng, Feida Zhu, Yong Wang, Ruicheng Liang, Huiwen Liu

Research Collection School Of Computing and Information Systems

Cryptocurrency has been subject to illicit activities probably more often than traditional financial assets due to the pseudo-anonymous nature of its transacting entities. An ideal detection model is expected to achieve all three critical properties of early detection, good interpretability, and versatility for various illicit activities. However, existing solutions cannot meet all these requirements, as most of them heavily rely on deep learning without interpretability and are only available for retrospective analysis of a specific illicit type. To tackle all these challenges, we propose Intention Monitor for early malice detection in Bitcoin, where the on-chain record data for a certain …

Learning Program Semantics For Vulnerability Detection Via Vulnerability-Specific Inter-Procedural Slicing, Bozhi Wu, Shangqing Liu, Xiao Yang, Zhiming Li, Jun Sun, Shang-Wei Lin

Research Collection School Of Computing and Information Systems

Learning-based approaches that learn code representations for software vulnerability detection have been proven to produce inspiring results. However, they still fail to capture complete and precise vulnerability semantics for code representations. To address the limitations, in this work, we propose a learning-based approach namely SnapVuln, which first utilizes multiple vulnerability-specific inter-procedural slicing algorithms to capture vulnerability semantics of various types and then employs a Gated Graph Neural Network (GGNN) with an attention mechanism to learn vulnerability semantics. We compare SnapVuln with state-of-the-art learning-based approaches on two public datasets, and confirm that SnapVuln outperforms them. We further perform an ablation study …

Mitigating Membership Inference Attacks Via Weighted Smoothing, Minghan Tan, Xiaofei Xie, Jun Sun, Tianhao Wang

Research Collection School Of Computing and Information Systems

Recent advancements in deep learning have spotlighted a crucial privacy vulnerability to membership inference attack (MIA), where adversaries can determine if specific data was present in a training set, thus potentially revealing sensitive information. In this paper, we introduce a technique, weighted smoothing (WS), to mitigate MIA risks. Our approach is anchored on the observation that training samples differ in their vulnerability to MIA, primarily based on their distance to clusters of similar samples. The intuition is clusters will make model predictions more confident and increase MIA risks. Thus WS strategically introduces noise to training samples, depending on whether they …

Closing The Gap: Leveraging Aes-Ni To Balance Adversarial Advantage And Honest User Performance In Argon2i, Nicholas Harrell, Nathaniel Krakauer

CERIAS Technical Reports

The challenge of providing data privacy and integrity while maintaining efficient performance for honest users is a persistent concern in cryptography. Attackers exploit advances in parallel hardware and custom circuit hardware to gain an advantage over regular users. One such method is the use of Application-Specific Integrated Circuits (ASICs) to optimize key derivation function (KDF) algorithms, giving adversaries a significant advantage in password guessing and recovery attacks. Other examples include using graphical processing units (GPUs) and field programmable gate arrays (FPGAs). We propose a focused approach to close the gap between adversarial advantage and honest user performance by leveraging the …

Ppdf: A Privacy-Preserving Cloud-Based Data Distribution System With Filtering, Yudi Zhang, Willy Susilo, Fuchun Guo, Guomin Yang

Research Collection School Of Computing and Information Systems

Cloud computing has emerged as a popular choice for distributing data among both individuals and companies. Ciphertext-policy attribute-based encryption (CP-ABE) has been extensively used to provide data security and enable fine-grained access control. With this encryption technique, only users whose attributes satisfy the access policy can access the plaintext. In order to mitigate the computational overhead on users, particularly on lightweight devices, partial decryption has been introduced, where the cloud assists in performing the decryption computations without revealing sensitive information. However, in this process, the cloud obtains the user's attributes, thus infringing on the user's privacy. To address this issue, …

Privacy-Preserving Bloom Filter-Based Keyword Search Over Large Encrypted Cloud Data, Yanrong Liang, Jianfeng Ma, Yinbin Miao, Da Kuang, Xiangdong Meng, Robert H. Deng

Research Collection School Of Computing and Information Systems

To achieve the search over encrypted data in cloud server, Searchable Encryption (SE) has attracted extensive attention from both academic and industrial fields. The existing Bloom filter-based SE schemes can achieve similarity search, but will generally incur high false positive rates, and even leak the privacy of values in Bloom filters (BF). To solve the above problems, we first propose a basic Privacy-preserving Bloom filter-based Keyword Search scheme using the Circular Shift and Coalesce-Bloom Filter (CSC-BF) and Symmetric-key Hidden Vector Encryption (SHVE) technology (namely PBKS), which can achieve effective search while protecting the values in BFs. Then, we design a …

Cyberattacks And Security Of Cloud Computing: A Complete Guideline, Muhammad Dawood, Shanshan Tu, Chuangbai Xiao, Hisham Alasmary, Muhammad Waqas, Sadaqat Ur Rehman

Research outputs 2022 to 2026

Cloud computing is an innovative technique that offers shared resources for stock cache and server management. Cloud computing saves time and monitoring costs for any organization and turns technological solutions for large-scale systems into server-to-service frameworks. However, just like any other technology, cloud computing opens up many forms of security threats and problems. In this work, we focus on discussing different cloud models and cloud services, respectively. Next, we discuss the security trends in the cloud models. Taking these security trends into account, we move to security problems, including data breaches, data confidentiality, data access controllability, authentication, inadequate diligence, phishing, …

Privacy-Preserving Arbitrary Geometric Range Query In Mobile Internet Of Vehicles, Yinbin Miao, Lin Song, Xinghua Li, Hongwei Li, Kim-Kwang Raymond Choo, Robert H. Deng

Research Collection School Of Computing and Information Systems

The mobile Internet of Vehicles (IoVs) has great potential for intelligent transportation, and creates spatial data query demands to realize the value of data. Outsourcing spatial data to a cloud server eliminates the need for local computation and storage, but it leads to data security and privacy threats caused by untrusted third-parties. Existing privacy-preserving spatial range query solutions based on Homomorphic Encryption (HE) have been developed to increase security. However, in the single server model, the private key is held by the query user, which incurs high computation and communication burdens on query users due to multiple rounds of interactions. …

Krover: A Symbolic Execution Engine For Dynamic Kernel Analysis, Pansilu Madhura Bhashana Pitigalaarachchi Pitigala Arachchillage, Xuhua Ding, Haiqing Qiu, Haoxin Tu, Jiaqi Hong, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

We present KRover, a novel kernel symbolic execution engine catered for dynamic kernel analysis such as vulnerability analysis and exploit generation. Different from existing symbolic execution engines, KRover operates directly upon a live kernel thread's virtual memory and weaves symbolic execution into the target's native executions. KRover is compact as it neither lifts the target binary to an intermediary representation nor uses QEMU or dynamic binary translation. Benchmarked against S2E, our performance experiments show that KRover is up to 50 times faster but with one tenth to one quarter of S2E memory cost. As shown in our four case studies, …

Executive Order On The Safe, Secure, And Trustworthy Development And Use Of Artificial Intelligence, Joseph R. Biden

Copyright, Fair Use, Scholarly Communication, etc.

Section 1. Purpose. Artificial intelligence (AI) holds extraordinary potential for both promise and peril. Responsible AI use has the potential to help solve urgent challenges while making our world more prosperous, productive, innovative, and secure. At the same time, irresponsible use could exacerbate societal harms such as fraud, discrimination, bias, and disinformation; displace and disempower workers; stifle competition; and pose risks to national security. Harnessing AI for good and realizing its myriad benefits requires mitigating its substantial risks. This endeavor demands a society-wide effort that includes government, the private sector, academia, and civil society.

My Administration places the highest urgency …

Integrating Human Expert Knowledge With Openai And Chatgpt: A Secure And Privacy-Enabled Knowledge Acquisition Approach, Ben Phillips

College of Engineering Summer Undergraduate Research Program

Advanced Large Language Models (LLMs) struggle to produce accurate results and preserve user privacy for use cases involving domain-specific knowledge. A privacy-preserving approach for leveraging LLM capabilities on domain-specific knowledge could greatly expand the use cases of LLMs in a variety of disciplines and industries. This project explores a method for acquiring domain-specific knowledge for use with GPT3 while protecting sensitive user information with ML-based text-sanitization.

The Infosys Times, Vol. 9, No. 1, St. Cloud State University

The Infosys TIMES

• Paving the Future
• Cybersecurity Week
• International Student Ambassadors
• Student Highlight
• MISA Internship Procedures
• Faculty Spotlight
• Staff Farewell - Kelley Hennen
• Alumni Diaries
• InfoSys Diaries
• HBS Updates
• Undergrad Certifications
• Congrats / Farewell Graduates

Decentralized Multimedia Data Sharing In Iov: A Learning-Based Equilibrium Of Supply And Demand, Jiani Fan, Minrui Xu, Jiale Guo, Lwin Khin Shar, Jiawen Kang, Dusit Niyato, Kwok-Yan Lam

Research Collection School Of Computing and Information Systems

The Internet of Vehicles (IoV) has great potential to transform transportation systems by enhancing road safety, reducing traffic congestion, and improving user experience through onboard infotainment applications. Decentralized data sharing can improve security, privacy, reliability, and facilitate infotainment data sharing in IoVs. However, decentralized data sharing may not achieve the expected efficiency if there are IoV users who only want to consume the shared data but are not willing to contribute their own data to the community, resulting in incomplete information observed by other vehicles and infrastructure, which can introduce additional transmission latency. Therefore, in this paper, by modeling the …

Visilience: An Interactive Visualization Framework For Resilience Analysis Using Control-Flow Graph, Hailong Jiang, Shaolun Ruan, Bo Fang, Yong Wang, Qiang Guan

Research Collection School Of Computing and Information Systems

Soft errors have become one of the main concerns for the resilience of HPC applications, as these errors can cause HPC applications to generate serious outcomes such as silent data corruption (SDC). Many approaches have been proposed to analyze the resilience of HPC applications. However, existing studies rarely address the challenges of analysis result perception. Specifically, resilience analysis techniques often produce a massive volume of unstructured data, making it difficult for programmers to perform resilience analysis due to non-intuitive raw data. Furthermore, different analysis models produce diverse results with multiple levels of detail, which can create obstacles to compare and …

Configuring Timing Parameters To Ensure Execution-Time Opacity In Timed Automata, Étienne André, Engel Lefaucheux, Didier Lime, Dylan Marinho, Jun Sun

Research Collection School Of Computing and Information Systems

Timing information leakage occurs whenever an attacker successfully deduces confidential internal information by observing some timed information such as events with timestamps. Timed automata are an extension of finite-state automata with a set of clocks evolving linearly and that can be tested or reset, making this formalism able to reason on systems involving concurrency and timing constraints. In this paper, we summarize a recent line of works using timed automata as the input formalism, in which we assume that the attacker has access (only) to the system execution time. First, we address the following execution-time opacity problem: given a timed …

Toward Intention Discovery For Early Malice Detection In Cryptocurrency, Ling Cheng, Feida Zhu, Yong Wang, Ruicheng Liang, Huiwen Liu

Research Collection School Of Computing and Information Systems

Cryptocurrency’s pseudo-anonymous nature makes it vulnerable to malicious activities. However, existing deep learning solutions lack interpretability and only support retrospective analysis of specific malice types. To address these challenges, we propose Intention-Monitor for early malice detection in Bitcoin. Our model, utilizing Decision-Tree based feature Selection and Complement (DT-SC), builds different feature sets for different malice types. The Status Proposal Module (SPM) and hierarchical self-attention predictor provide real-time global status and address label predictions. A survival module determines the stopping point and proposes the status sequence (intention). Our model detects various malicious activities with strong interpretability, outperforming state-of-the-art methods in extensive …

Owner-Free Distributed Symmetric Searchable Encryption Supporting Conjunctive Queries, Qiuyun Tong, Xinghua Li, Yinbin Miao, Yunwei Wang, Ximeng Liu, Robert H. Deng

Research Collection School Of Computing and Information Systems

Symmetric Searchable Encryption (SSE), as an ideal primitive, can ensure data privacy while supporting retrieval over encrypted data. However, existing multi-user SSE schemes require the data owner to share the secret key with all query users or always be online to generate search tokens. While there are some solutions to this problem, they have at least one weakness, such as non-supporting conjunctive query, result decryption assistance of the data owner, and unauthorized access. To solve the above issues, we propose an Owner-free Distributed Symmetric searchable encryption supporting Conjunctive query (ODiSC). Specifically, we first evaluate the Learning-Parity-with-Noise weak Pseudorandom Function (LPN-wPRF) …

Experimental Comparison Of Features, Analyses, And Classifiers For Android Malware Detection, Lwin Khin Shar, Biniam Fisseha Demissie, Mariano Ceccato, Naing Tun Yan, David Lo, Lingxiao Jiang, Christoph Bienert

Research Collection School Of Computing and Information Systems

Android malware detection has been an active area of research. In the past decade, several machine learning-based approaches based on different types of features that may characterize Android malware behaviors have been proposed. The usually-analyzed features include API usages and sequences at various abstraction levels (e.g., class and package), extracted using static or dynamic analysis. Additionally, features that characterize permission uses, native API calls and reflection have also been analyzed. Initial works used conventional classifiers such as Random Forest to learn on those features. In recent years, deep learning-based classifiers such as Recurrent Neural Network have been explored. Considering various …

Endwatch: A Practical Method For Detecting Non-Termination In Real-World Software, Yao Zhang, Xiaofei Xie, Yi Li, Sen Chen, Cen Zhang, Xiaohong Li

Research Collection School Of Computing and Information Systems

Detecting non-termination is crucial for ensuring program correctness and security, such as preventing denial-of-service attacks. While termination analysis has been studied for many years, existing methods have limited scalability and are only effective on small programs. To address this issue, we propose a practical termination checking technique, called EndWatch, for detecting non-termination through testing. Specifically, we introduce two methods to generate non-termination oracles based on checking state revisits, i.e., if the program returns to a previously visited state at the same program location, it does not terminate. The non-termination oracles can be incorporated into testing tools (e.g., AFL used in …

Autodebloater: Automated Android App Debloating, Jiakun Liu, Xing Hu, Thung Ferdian, Shahar Maoz, Eran Toch, Debin Gao, David Lo

Research Collection School Of Computing and Information Systems

Android applications are getting bigger with an increasing number of features. However, not all the features are needed by a specific user. The unnecessary features can increase the attack surface and cost additional resources (e.g., storage and memory). Therefore, it is important to remove unnecessary features from Android applications. However, it is difficult for the end users to fully explore the apps to identify the unnecessary features, and there is no off-the-shelf tool available to assist users to debloat the apps by themselves. In this work, we propose AutoDebloater to debloat Android applications automatically for end users. AutoDebloater is a …

Fine-Grained In-Context Permission Classification For Android Apps Using Control-Flow Graph Embedding, Vikas Kumar Malviya, Naing Tun Yan, Chee Wei Leow, Ailys Xynyn Tee, Lwin Khin Shar, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Android is the most popular operating system for mobile devices nowadays. Permissions are a very important part of Android security architecture. Apps frequently need the users’ permission, but many of them only ask for it once—when the user uses the app for the first time—and then they keep and abuse the given permissions. Longing to enhance Android permission security and users’ private data protection is the driving factor behind our approach to explore fine-grained contextsensitive permission usage analysis and thereby identify misuses in Android apps. In this work, we propose an approach for classifying the fine-grained permission uses for each …

Threshold Attribute-Based Credentials With Redactable Signature, Rui Shi, Huamin Feng, Yang Yang, Feng Yuan, Yingjiu Li, Hwee Hwa Pang, Robert H. Deng

Research Collection School Of Computing and Information Systems

Threshold attribute-based credentials are suitable for decentralized systems such as blockchains as such systems generally assume that authenticity, confidentiality, and availability can still be guaranteed in the presence of a threshold number of dishonest or faulty nodes. Coconut (NDSS'19) was the first selective disclosure attribute-based credentials scheme supporting threshold issuance. However, it does not support threshold tracing of user identities and threshold revocation of user credentials, which is desired for internal governance such as identity management, data auditing, and accountability. The communication and computation complexities of Coconut for verifying credentials are linear in the number of each user's attributes and …

Testsgd: Interpretable Testing Of Neural Networks Against Subtle Group Discrimination, Mengdi Zhang, Jun Sun, Jingyi Wang, Bing Sun

Research Collection School Of Computing and Information Systems

Discrimination has been shown in many machine learning applications, which calls for sufficient fairness testing before their deployment in ethic-relevant domains. One widely concerning type of discrimination, testing against group discrimination, mostly hidden, is much less studied, compared with identifying individual discrimination. In this work, we propose TestSGD, an interpretable testing approach which systematically identifies and measures hidden (which we call ‘subtle’) group discrimination of a neural network characterized by conditions over combinations of the sensitive attributes. Specifically, given a neural network, TestSGD first automatically generates an interpretable rule set which categorizes the input space into two groups. Alongside, TestSGD …

Hercules: Boosting The Performance Of Privacy-Preserving Federated Learning, Guowen Xu, Xingshuo Han, Shengmin Xu, Tianwei Zhang, Hongwei Li, Xinyi Huang, Robert H. Deng

Research Collection School Of Computing and Information Systems

In this paper, we address the problem of privacy-preserving federated neural network training with N users. We present Hercules, an efficient and high-precision training framework that can tolerate collusion of up to N−1 users. Hercules follows the POSEIDON framework proposed by Sav et al. (NDSS’21), but makes a qualitative leap in performance with the following contributions: (i) we design a novel parallel homomorphic computation method for matrix operations, which enables fast Single Instruction and Multiple Data (SIMD) operations over ciphertexts. For the multiplication of two h×h dimensional matrices, our method reduces the computation complexity from O(h3) to O(h) . This …

Autoconf: Automated Configuration Of Unsupervised Learning Systems Using Metamorphic Testing And Bayesian Optimization, Lwin Khin Shar, Goknil Arda, Erik Johannes Husom, Sagar Sen Sen, Naing Tun Yan, Kisub Kim

Research Collection School Of Computing and Information Systems

Unsupervised learning systems using clustering have gained significant attention for numerous applications due to their unique ability to discover patterns and structures in large unlabeled datasets. However, their effectiveness highly depends on their configuration, which requires domain-specific expertise and often involves numerous manual trials. Specifically, selecting appropriate algorithms and hyperparameters adds to the com- plexity of the configuration process. In this paper, we propose, apply, and assess an automated approach (AutoConf) for config- uring unsupervised learning systems using clustering, leveraging metamorphic testing and Bayesian optimization. Metamorphic testing is utilized to verify the configurations of unsupervised learning systems by applying a …

Decompiling X86 Deep Neural Network Executables, Zhibo Liu, Yuanyuan Yuan, Shuai Wang, Xiaofei Xie, Lei Ma

Research Collection School Of Computing and Information Systems

Due to their widespread use on heterogeneous hardware devices, deep learning (DL) models are compiled into executables by DL compilers to fully leverage low-level hardware primitives. This approach allows DL computations to be undertaken at low cost across a variety of computing platforms, including CPUs, GPUs, and various hardware accelerators. We present BTD (Bin to DNN), a decompiler for deep neural network (DNN) executables. BTD takes DNN executables and outputs full model specifications, including types of DNN operators, network topology, dimensions, and parameters that are (nearly) identical to those of the input models. BTD delivers a practical framework to process …

Multi-Granularity Detector For Vulnerability Fixes, Truong Giang Nguyen, Cong, Thanh Le, Hong Jin Kang, Ratnadira Widyasari, Chengran Yang, Zhipeng Zhao, Bowen Xu, Jiayuan Zhou, Xin Xia, Ahmed E. Hassan, David Lo, David Lo

Research Collection School Of Computing and Information Systems

With the increasing reliance on Open Source Software, users are exposed to third-party library vulnerabilities. Software Composition Analysis (SCA) tools have been created to alert users of such vulnerabilities. SCA requires the identification of vulnerability-fixing commits. Prior works have proposed methods that can automatically identify such vulnerability-fixing commits. However, identifying such commits is highly challenging, as only a very small minority of commits are vulnerability fixing. Moreover, code changes can be noisy and difficult to analyze. We observe that noise can occur at different levels of detail, making it challenging to detect vulnerability fixes accurately. To address these challenges and …

A Certificateless Designated Verifier Sanitizable Signature, Yonghua Zhan, Bixia Yi, Yang Yang, Renjie He, Rui Shi

Research Collection School Of Computing and Information Systems

Sanitizable Signature is a digital signature variant that enables modification operations, allowing sanitizers to alter the signed data in a regulated manner without requiring any interaction with the original signer. It is widely used in scenarios such as healthcare data privacy protection, social networks, secure routing, etc. In existing sanitizable signature schemes, anyone can verify the validity and authenticity of the sanitized message, which results in costly certificate management overhead or complicated key escrow problems. To address these challenges, a designated verifier certificateless sanitizable signature scheme is proposed. This scheme introduces the concept of a designated verifier into sanitizable signatures, …

Cybersecurity Safeguards: What Cybersecurity Safeguards Could Have Prevented The Intelligence/Data Breach By A Member Of The Air National Guard, Christopher Curtis Royal

Cyber Operations and Resilience Program Graduate Projects

Jack Teixeira, a 21-year-old IT specialist Air National Guard found himself on the wrong side of the US law after sharing what is considered classified and extremely sensitive information about USA's operations and role in Ukraine and Russia war. Like other previous cases of leakage of classified intelligence, the case of Teixeira raises concerns about the weaknesses and vulnerability of federal agencies' IT systems and security protocols governing accessibility to classified documents. Internal leakages of such classified documents hurt national security and can harm the country, especially when such secretive intelligence finds its way into the hands of enemies. Unauthorized …