Advanced Security Analysis for Emergent Software Platforms

Emergent software ecosystems, boomed by the advent of smartphones and the Internet of Things (IoT) platforms, are perpetually sophisticated, deployed into highly dynamic environments, and facilitating interactions across heterogeneous domains. Accordingly, assessing the security thereof is a pressing need, yet requires high levels of scalability and reliability to handle the dynamism involved in such volatile ecosystems. This dissertation seeks to enhance conventional security detection methods to cope with the emergent features of contemporary software ecosystems. In particular, it analyzes the security of Android and IoT ecosystems by developing rigorous vulnerability detection methods. A critical aspect of this work is the focus on detecting vulnerable and unsafe interactions between applications that share common components and devices. Contributions of this work include novel insights and methods for: (1) detecting vulnerable interactions between Android applications that leverage dynamic loading features for concealing the interactions; (2) identifying unsafe interactions between smart home applications by considering physical and cyber channels; (3) detecting malicious IoT applications that are developed to target numerous IoT devices; (4) detecting insecure patterns of emergent security APIs that are reused from open-source software. In all of the four research thrusts, we present thorough security analysis and extensive evaluations based on real-world applications. Our results demonstrate that the proposed detection mechanisms can efficiently and effectively detect vulnerabilities in contemporary software platforms. Advisers: Hamid Bagheri and Qiben Ya

Security slicing for auditing XML, XPath, and SQL injection vulnerabilities

XML, XPath, and SQL injection vulnerabilities are among the most common and serious security issues for Web applications and Web services. Thus, it is important for security auditors to ensure that the implemented code is, to the extent pos- sible, free from these vulnerabilities before deployment. Although existing taint analysis approaches could automatically detect potential vulnerabilities in source code, they tend to generate many false warnings. Furthermore, the produced traces, i.e. data- flow paths from input sources to security-sensitive operations, tend to be incomplete or to contain a great deal of irrelevant infor- mation. Therefore, it is difficult to identify real vulnerabilities and determine their causes. One suitable approach to support security auditing is to compute a program slice for each security-sensitive operation, since it would contain all the information required for performing security audits (Soundness). A limitation, however, is that such slices may also contain information that is irrelevant to security (Precision), thus raising scalability issues for security audits. In this paper, we propose an approach to assist security auditors by defining and experimenting with pruning techniques to reduce original program slices to what we refer to as security slices, which contain sound and precise information. To evaluate the proposed pruning mechanism by using a number of open source benchmarks, we compared our security slices with the slices generated by a state-of-the-art program slicing tool. On average, our security slices are 80% smaller than the original slices, thus suggesting significant reduction in auditing costs

Cybersecurity for Manufacturers: Securing the Digitized and Connected Factory

As manufacturing becomes increasingly digitized and data-driven, manufacturers will find themselves at serious risk. Although there has yet to be a major successful cyberattack on a U.S. manufacturing operation, threats continue to rise. The complexities of multi-organizational dependencies and data-management in modern supply chains mean that vulnerabilities are multiplying. There is widespread agreement among manufacturers, government agencies, cybersecurity firms, and leading academic computer science departments that U.S. industrial firms are doing too little to address these looming challenges. Unfortunately, manufacturers in general do not see themselves to be at particular risk. This lack of recognition of the threat may represent the greatest risk of cybersecurity failure for manufacturers. Public and private stakeholders must act before a significant attack on U.S. manufacturers provides a wake-up call. Cybersecurity for the manufacturing supply chain is a particularly serious need. Manufacturing supply chains are connected, integrated, and interdependent; security of the entire supply chain depends on security at the local factory level. Increasing digitization in manufacturing— especially with the rise of Digital Manufacturing, Smart Manufacturing, the Smart Factory, and Industry 4.0, combined with broader market trends such as the Internet of Things (IoT)— exponentially increases connectedness. At the same time, the diversity of manufacturers—from large, sophisticated corporations to small job shops—creates weakest-link vulnerabilities that can be addressed most effectively by public-private partnerships. Experts consulted in the development of this report called for more holistic thinking in industrial cybersecurity: improvements to technologies, management practices, workforce training, and learning processes that span units and supply chains. Solving the emerging security challenges will require commitment to continuous improvement, as well as investments in research and development (R&D) and threat-awareness initiatives. This holistic thinking should be applied across interoperating units and supply chains.National Science Foundation, Grant No. 1552534https://deepblue.lib.umich.edu/bitstream/2027.42/145442/1/MForesight_CybersecurityReport_Web.pd

DAG-Based Attack and Defense Modeling: Don't Miss the Forest for the Attack Trees

This paper presents the current state of the art on attack and defense modeling approaches that are based on directed acyclic graphs (DAGs). DAGs allow for a hierarchical decomposition of complex scenarios into simple, easily understandable and quantifiable actions. Methods based on threat trees and Bayesian networks are two well-known approaches to security modeling. However there exist more than 30 DAG-based methodologies, each having different features and goals. The objective of this survey is to present a complete overview of graphical attack and defense modeling techniques based on DAGs. This consists of summarizing the existing methodologies, comparing their features and proposing a taxonomy of the described formalisms. This article also supports the selection of an adequate modeling technique depending on user requirements

Security slicing for auditing common injection vulnerabilities

Cross-site scripting and injection vulnerabilities are among the most common and serious security issues for Web applications. Although existing static analysis approaches can detect potential vulnerabilities in source code, they generate many false warnings and source-sink traces with irrelevant information, making their adoption impractical for security auditing. One suitable approach to support security auditing is to compute a program slice for each sink, which contains all the information required for security auditing. However, such slices are likely to contain a large amount of information that is irrelevant to security, thus raising scalability issues for security audits. In this paper, we propose an approach to assist security auditors by defining and experimenting with pruning techniques to reduce original program slices to what we refer to as security slices, which contain sound and precise information. To evaluate the proposed approach, we compared our security slices to the slices generated by a state-of-the-art program slicing tool, based on a number of open-source benchmarks. On average, our security slices are 76% smaller than the original slices. More importantly, with security slicing, one needs to audit approximately 1% of the total code to fix all the vulnerabilities, thus suggesting significant reduction in auditing costs

A Survey of DeFi Security: Challenges and Opportunities

DeFi, or Decentralized Finance, is based on a distributed ledger called blockchain technology. Using blockchain, DeFi may customize the execution of predetermined operations between parties. The DeFi system use blockchain technology to execute user transactions, such as lending and exchanging. The total value locked in DeFi decreased from \$200 billion in April 2022 to \$80 billion in July 2022, indicating that security in this area remained problematic. In this paper, we address the deficiency in DeFi security studies. To our best knowledge, our paper is the first to make a systematic analysis of DeFi security. First, we summarize the DeFi-related vulnerabilities in each blockchain layer. Additionally, application-level vulnerabilities are also analyzed. Then we classify and analyze real-world DeFi attacks based on the principles that correlate to the vulnerabilities. In addition, we collect optimization strategies from the data, network, consensus, smart contract, and application layers. And then, we describe the weaknesses and technical approaches they address. On the basis of this comprehensive analysis, we summarize several challenges and possible future directions in DeFi to offer ideas for further research

Detection of Lightweight Directory Access Protocol Query Injection Attacks in Web Applications

The Lightweight Directory Access Protocol (LDAP) is a common protocol used in organizations for Directory Service. LDAP is popular because of its features such as representation of data objects in hierarchical form, being open source and relying on TCP/IP, which is necessary for Internet access. However, with LDAP being used in a large number of web applications, different types of LDAP injection attacks are becoming common. The idea behind LDAP injection attacks is to take advantage of an application not validating inputs before being used as part of LDAP queries. An attacker can provide inputs that may result in alteration of intended LDAP query structure. LDAP injection attacks can lead to various types of security breaches including (i) Login Bypass, (ii) Information Disclosure, (iii) Privilege Escalation, and (iv) Information Alteration. Despite many research efforts focused on traditional SQL Injection attacks, most of the proposed techniques cannot be suitably applied for mitigating LDAP injection attacks due to syntactic and semantic differences between LDAP and SQL queries. Many implemented web applications remain vulnerable to LDAP injection attacks. In particular, there has been little attention for testing web applications to detect the presence of LDAP query injection attacks. The aim of this thesis is two folds: First, study various types of LDAP injection attacks and vulnerabilities reported in the literature. The planned research is to critically examine and evaluate existing injection mitigation techniques using a set of open source applications reported to be vulnerable to LDAP query injection attacks. Second, propose an approach to detect LDAP injection attacks by generating test cases when developing secure web applications. In particular, the thesis focuses on specifying signatures for detecting LDAP injection attack types using Object Constraint Language (OCL) and evaluates the proposed approach using PHP web applications. We also measure the effectiveness of generated test cases using a metric named Mutation Score

ASAP : automatic semantics-aware analysis of network payloads

Automatic inspection of network payloads is a prerequisite for effective analysis of network communication. Security research has largely focused on network analysis using protocol specifications, for example for intrusion detection, fuzz testing and forensic analysis. The specification of a protocol alone, however, is often not sufficient for accurate analysis of communication, as it fails to reflect individual semantics of network applications. We propose a framework for semantics-aware analysis of network payloads which automaticylly extracts semantic components from recorded network traffic. Our method proceeds by mapping network payloads to a vector space and identifying semantic templates corresponding to base directions in the vector space. We demonstrate the efficacy of semantics-aware analysis in different security applications: automatic discovery of patterns in honeypot data, analysis of malware communication and network intrusion detection