A survey of the hazards of using USB as a universal charging standard as pertains to smart devices

The Universal Serial Bus protocol was designed to be, and has become, the single standard used for interfacing with computer peripherals and electronic components. The proliferation of this protocol has resulted in USB power outlets in public places being a common sight. However, the very universality of the protocol, combined with the rapidly shrinking size of computer processors and microcontrollers, creates some rather severe security vulnerabilities. Plugging a USB smart device into a compromised port, even one purported to be solely a power delivery system, can allow an attacker to perform many different malicious actions, the range and severity of which depend on the length of time the device remains attached, the specific type of device in question, and the resources of the attacker. This thesis presents a survey of the types of attacks possible against some of the most common and popular devices, a comparison of these devices from the standpoint of defense against these types of attacks, and possible mitigation strategies

Detecting Hardware-assisted Hypervisor Rootkits within Nested Virtualized Environments

Virtual machine introspection (VMI) is intended to provide a secure and trusted platform from which forensic information can be gathered about the true behavior of malware within a guest. However, it is possible for malware to escape a guest into the host and for hypervisor rootkits, such as BluePill, to stealthily transition a native OS into a virtualized environment. This research examines the effectiveness of selected detection mechanisms against hardware-assisted virtualization rootkits (HAV-R) within a nested virtualized environment. It presents the design, implementation, analysis, and evaluation of a hypervisor rootkit detection system which exploits both processor and translation lookaside buffer-based mechanisms to detect hypervisor rootkits within a variety of nested virtualized systems. It evaluates the effects of different types of virtualization on hypervisor rootkit detection and explores the effectiveness in-guest HAV-R obfuscation efforts. The results provide convincing evidence that the HAV-Rs are detectable in all SVMI scenarios examined, regardless of HAV-R or virtualization type. Also, that the selected detection techniques are effective at detection of HAV-R within nested virtualized environments, and that the type of virtualization implemented in a VMI system has minimal to no effect on HAV-R detection. Finally, it is determined that in-guest obfuscation does not successfully obfuscate the existence of HAV-R

Autoscopy Jr.: Intrusion Detection for Embedded Control Systems

Securing embedded control systems within the power grid presents a unique challenge: on top of the resource restrictions inherent to these devices, SCADA systems must also accommodate strict timing requirements that are non-negotiable, and their massive scale greatly amplifies costs such as power consumption. These constraints make the conventional approach to host intrusion detection--namely, employing virtualization in some manner--too costly or impractical for embedded control systems within critical infrastructure. Instead, we take an in-kernel approach to system protection, building upon the Autoscopy system developed by Ashwin Ramaswamy that places probes on indirectly-called functions and uses them to monitor its host system for behavior characteristic of control-flow-altering malware, such as rootkits. In this thesis, we attempt to show that such a method would indeed be a viable method of protecting embedded control systems. We first identify several issues with the original prototype, and present a new version of the program (dubbed Autoscopy Jr.) that uses trusted location lists to verify that control is coming from a known, trusted location inside our kernel. Although we encountered additional performance overhead when testing our new design, we developed a kernel profiler that allowed us to identify the probes responsible for this overhead and discard them, leaving us with a final probe list that generated less than 5% overhead on every one of our benchmark tests. Finally, we attempted to run Autoscopy Jr. on two specialized kernels (one with an optimized probing framework, and another with a hardening patch installed), finding that the former did not produce enough performance benefits to preclude using our profiler, and that the latter required a different method of scanning for indirect functions for Autoscopy Jr. to operate. We argue that Autoscopy Jr. is indeed a feasible intrusion detection system for embedded control systems, as it can adapt easily to a variety of system architectures and allows us to intelligently balance security and performance on these critical devices

SHI(EL)DS: A Novel Hardware-based Security Backplane to Enhance Security with Minimal Impact to System Operation

Computer security continues to increase in importance both in the commercial world and within the Air Force. Dedicated hardware for security purposes presents and enhances a number of security capabilities. Hardware enhances both the security of the security system and the quality and trustworthiness of the information being gathered by the security monitors. Hardware reduces avenues of attack on the security system and ensures the trustworthiness of information only through proper design and placement. Without careful system design, security hardware leaves itself vulnerable to many attacks that it is capable of defending against. Our SHI(EL)DS architecture combines these insights into a comprehensive, modular hardware security backplane architecture. This architecture provides many of the capabilities required by the Cybercraft deployment platform. Most importantly, it makes significant progress towards establishing a root of trust for this platform. Progressing the development of the Cybercraft initiative advances the capabilities of the Air Force’s ability to operate in and defend cyberspace

Malware detection and analysis via layered annotative execution

Malicious software (i.e., malware) has become a severe threat to interconnected computer systems for decades and has caused billions of dollars damages each year. A large volume of new malware samples are discovered daily. Even worse, malware is rapidly evolving to be more sophisticated and evasive to strike against current malware analysis and defense systems. This dissertation takes a root-cause oriented approach to the problem of automatic malware detection and analysis. In this approach, we aim to capture the intrinsic natures of malicious behaviors, rather than the external symptoms of existing attacks. We propose a new architecture for binary code analysis, which is called whole-system out-of-the-box fine-grained dynamic binary analysis, to address the common challenges in malware detection and analysis. to realize this architecture, we build a unified and extensible analysis platform, codenamed TEMU. We propose a core technique for fine-grained dynamic binary analysis, called layered annotative execution, and implement this technique in TEMU. Then on the basis of TEMU, we have proposed and built a series of novel techniques for automatic malware detection and analysis. For postmortem malware analysis, we have developed Renovo, Panorama, HookFinder, and MineSweeper, for detecting and analyzing various aspects of malware. For proactive malware detection, we have built HookScout as a proactive hook detection system. These techniques capture intrinsic characteristics of malware and thus are well suited for dealing with new malware samples and attack mechanisms

The Future of Cybercrime: AI and Emerging Technologies Are Creating a Cybercrime Tsunami

This paper reviews the impact of AI and emerging technologies on the future of cybercrime and the necessary strategies to combat it effectively. Society faces a pressing challenge as cybercrime proliferates through AI and emerging technologies. At the same time, law enforcement and regulators struggle to keep it up. Our primary challenge is raising awareness as cybercrime operates within a distinct criminal ecosystem. We explore the hijacking of emerging technologies by criminals (CrimeTech) and their use in illicit activities, along with the tools and processes (InfoSec) to protect against future cybercrime. We also explore the role of AI and emerging technologies (DeepTech) in supporting law enforcement, regulation, and legal services (LawTech)

Combining Programs to Enhance Security Software

Automatic threats require automatic solutions, which become automatic threats themselves. When software grows in functionality, it grows in complexity, and in the number of bugs. To keep track of and counter all of the possible ways that a malicious party can exploit these bugs, we need security software. Such software helps human developers identify and remove bugs, or system administrators detect attempted attacks. But like any other software, and likely more so, security software itself can have blind spots or flaws. In the best case, it stops working, and becomes ineffective. In the worst case, the security software has privileged access to the system it is supposed to protect, and the attacker can hijack those privileges for its own purposes. So we will need external programs to compensate for their weaknesses. At the same time, we need to minimize the additional attack surface and development time due to creating new solutions. To address both points, this thesis will explore how to combine multiple programs to overcome a number of weaknesses in individual security software: (1) When login authentication and physical protections of a smart phone fail, fake, decoy applications detect unauthorized usage and draw the attacker away from truly sensitive applications; (2) when a fuzzer, an automatic software testing tool, requires a diverse set of initial test inputs, manipulating the tools that a human uses to generate these inputs multiplies the generated inputs; (3) when the software responsible for detecting attacks, known as an intrusion detection system, itself needs protection against attacks, a simplified state machine tracks the software's interaction with the underlying platform, without the complexity and risks of a fully functional intrusion detection system; (4) when intrusion detection systems run on multiple, independent machines, a graph-theoretic framework drives the design for how the machines cooperatively monitor each other, forcing the attacker to not only perform more work, but also do so faster. Instead of introducing new, stand-alone security software, the above solutions only require a fixed number of new tools that rely on a diverse selection of programs that already exist. Nor do any of the programs, old or new, require additional privileges that the old programs did not have before. In other words, we multiply the power of security software without multiplying their risks

Trust management schemes for peer-to-peer networks

Peer-to-peer (P2P) networking enables users with similar interests to exchange, or obtain files. This network model has been proven popular to exchange music, pictures, or software applications. These files are saved, and most likely executed, at the downloading host. At the expense of this mechanism, worms, viruses, and malware find an open front door to the downloading host and gives them a convenient environment for successful proliferation throughout the network. Although virus detection software is currently available, this countermeasure works in a reactive fashion, and in most times, in an isolated manner. A trust management scheme is considered to contain the proliferation of viruses in P2P networks. Specifically, a cooperative and distributed trust management scheme based on a two-layer approach to bound the proliferation of viruses is proposed. The new scheme is called double-layer dynamic trust (DDT) management scheme. The results show that the proposed scheme bounds the proliferation of malware. With the proposed scheme, the number of infected hosts and the proliferation rate are limited to small values. In addition, it is shown that network activity is not discouraged by using the proposed scheme. Moreover, to improve the efficiency on the calculation of trust values of ratio based normalization models, a model is proposed for trust value calculation using a three-dimensional normalization to represent peer activity with more accuracy than that of a conventional ratio based normalization. Distributed network security is also considered, especially in P2P network security. For many P2P systems, including ad hoc networks and online markets, reputation systems have been considered as a solution for mitigating the affects of malicious peers. However, a sybil attack, wherein forging identities is performed to unfairly and arbitrarily influence the reputation of peers in a network or community. To defend against sybil attack, each reported transaction, which is used to calculate trust values, is verified. In this thesis, it is shown that peer reputation alone cannot bound network subversion of a sybil attack. Therefore, a new trust management framework, called Sybildefense, is introduced. This framework combines a trust management scheme with a cryptography mechanism to verify different transaction claims issue by peers, including those bogus claims of sybil peers. To improve the efficiency on the identification of honest peers from sybil peers, a k-means clustering mechanism is adopted. Moreover, to include a list of peer’s trustees in a warning messages is proposed to generate a local table for a peer that it is used to identify possible clusters of sybil peers. The defensive performance of these algorithms are compared under sybil attacks. The performance results show that the proposed framework (Sybildefense) can thwart sybil attacks efficiently