Rootkit Detection Using A Cross-View Clean Boot Method

In cyberspace, attackers commonly infect computer systems with malware to gain capabilities such as remote access, keylogging, and stealth. Many malware samples include rootkit functionality to hide attacker activities on the target system. After detection, users can remove the rootkit and associated malware from the system with commercial tools. This research describes, implements, and evaluates a clean boot method using two partitions to detect rootkits on a system. One partition is potentially infected with a rootkit while the other is clean. The method obtains directory listings of the potentially infected operating system from each partition and compares the lists to find hidden files. While the clean boot method is similar to other cross-view detection techniques, this method is unique because it uses a clean partition of the same system as the clean operating system, rather than external media. The method produces a 0% false positive rate and a 40.625% true positive rate. In operation, the true positive rate should increase because the experiment produces limitations that prevent many rootkits from working properly. Limitations such as incorrect rootkit setup and rootkits that detect VMware prevent the method from detecting rootkit behavior in this experiment. Vulnerabilities of the method include the assumption that the system restore folder is clean and the assumption that the clean partition is clean. This thesis provides recommendations for more effective rootkit detection

Effectiveness of Linux rootkit detection tools

Abstract. Rootkits — a type of software that specializes in hiding entities in computer systems while enabling continuous control or access to it — are particularly difficult to detect compared to other kinds of software. Various tools exist for detecting rootkits, utilizing a wide variety of detection techniques and mechanisms. However, the effectiveness of such tools is not well established, especially in contemporary academic research and in the context of the Linux operating system. This study carried out an empirical evaluation of the effectiveness of five tools with capabilities to detect Linux rootkits: OSSEC, AIDE, Rootkit Hunter, Chkrootkit and LKRG. The effectiveness of each tool was tested by injecting 15 publicly available rootkits in individual detection tests in virtual machines running Ubuntu 16.04, executing the detection tool and capturing its results for analysis. A total of 75 detection tests were performed. The results showed that only 37.3% of the detection tests provided any indication of a rootkit infection or suspicious system behaviour, with the rest failing to provide any signs of anomalous behaviour. However, combining the findings of multiple detection tools increased the overall detection rate to 93.3%, as all but a single rootkit were discovered by at least one tool. Variation was observed in the effectiveness of the detection tools, with detection rates ranging from 13.3% to 53.3%. Variation in detection effectiveness was also found between categories of rootkits, as the overall detection rate was 46.7% for user mode rootkits and 31.1% for kernel mode rootkits. Overall, the findings showed that while an individual detection tool‘s effectiveness can be lacking, using a combination of tools considerably increased the likelihood of a successful detection

Hardware Virtualization Applied to Rootkit Defense

This research effort examines the idea of applying virtualization hardware to enhance operating system security against rootkits. Rootkits are sets of tools used to hide code and/or functionality from the user and operating system. Rootkits can accomplish this feat through using access to one part of an operating system to change another part that resides at the same privilege level. Hardware assisted virtualization (HAV) provides an opportunity to defeat this tactic through the introduction of a new operating mode. Created to aid operating system virtualization, HAV provides hardware support for managing and saving multiple states of the processor. This hardware support overcomes a problem in pure software virtualization, which is the need to modify guest software to run at a less privileged level. Using HAV, guest software can operate at the pre-HAV most privileged level. This thesis provides a plan to protect data structures targeted by rootkits through unconventional use of HAV technology to secure system resources such as memory. This method of protection will provide true real-time security through OS attack prevention, rather than reaction

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

Lost at Sea: Assessment and Evaluation of Rootkit Attacks on Shipboard Microgrids

Increased dependence of the maritime industry on information and communication networks has made shipboard power systems vulnerable to stealthy cyber-attacks. One such attack variant, called rootkit, can leverage system knowledge to hide its presence and allow remotely located malware handlers to gain complete control of infected subsystems. This paper presents a comprehensive evaluation of the threat landscape imposed by such attack variants on Medium Voltage DC (MVDC) shipboard microgrids, including a discussion of their impact on the overall maritime sector in general, and provides several simulation results to demonstrate the same. It also analyzes and presents the actions of possible defense mechanisms, with specific emphasis on evasion, deception, and detection frameworks, that will help ship operators and maritime cybersecurity professionals protect their systems from such attacks.Comment: 2023 IEEE Electric Ship Technologies Symposium (ESTS

Thoughts on hypervisor-based virtualization threats and vulnerabilities / Pensamentos sobre ameaças e vulnerabilidades de virtualização baseadas no hipervisor

As vulnerability and threat analysis play a vital role in software security in an ever-increasing digital world of virtualized computer and information systems, it is paramount that key security concepts are understood and that crucial security practices are applied in order to safeguard these types of assets. For that, this work attempts to provide an insight at vulnerabilities and threats related to the hypervisor model of virtualization while also fomenting a discussion about the security demands and challenges that this technology brings

Attacks on the Android Platform

The focus of this research revolves around Android platform security, specifically Android malware attacks and defensive techniques. Android is a mobile operating system developed by Google, based on the Linux kernel and designed primarily for touchscreen mobile devices such as smartphones and tablets. With the rise of device mobility in our data-driven world, Android constitutes most of the operating systems on these mobile devices playing a dominant role in today’s world. Hence, this paper analyzes attacks and the various defensive mechanisms that have been proposed to prevent those attacks

Hardware Mechanisms for Efficient Memory System Security

The security of a computer system hinges on the trustworthiness of the operating system and the hardware, as applications rely on them to protect code and data. As a result, multiple protections for safeguarding the hardware and OS from attacks are being continuously proposed and deployed. These defenses, however, are far from ideal as they only provide partial protection, require complex hardware and software stacks, or incur high overheads. This dissertation presents hardware mechanisms for efficiently providing strong protections against an array of attacks on the memory hardware and the operating system’s code and data. In the first part of this dissertation, we analyze and optimize protections targeted at defending memory hardware from physical attacks. We begin by showing that, contrary to popular belief, current DDR3 and DDR4 memory systems that employ memory scrambling are still susceptible to cold boot attacks (where the DRAM is frozen to give it sufficient retention time and is then re-read by an attacker after reboot to extract sensitive data). We then describe how memory scramblers in modern memory controllers can be transparently replaced by strong stream ciphers without impacting performance. We also demonstrate how the large storage overheads associated with authenticated memory encryption schemes (which enable tamper-proof storage in off-chip memories) can be reduced by leveraging compact integer encodings and error-correcting code (ECC) DRAMs – without forgoing the error detection and correction capabilities of ECC DRAMs. The second part of this dissertation presents Neverland: a low-overhead, hardware-assisted, memory protection scheme that safeguards the operating system from rootkits and kernel-mode malware. Once the system is done booting, Neverland’s hardware takes away the operating system’s ability to overwrite certain configuration registers, as well as portions of its own physical address space that contain kernel code and security-critical data. Furthermore, it prohibits the CPU from fetching privileged code from any memory region lying outside the physical addresses assigned to the OS kernel and drivers. This combination of protections makes it extremely hard for an attacker to tamper with the kernel or introduce new privileged code into the system – even in the presence of software vulnerabilities. Neverland enables operating systems to reduce their attack surface without having to rely on complex integrity monitoring software or hardware. The hardware mechanisms we present in this dissertation provide building blocks for constructing a secure computing base while incurring lower overheads than existing protections.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147604/1/salessaf_1.pd

MADAM: Effective and Efficient Behavior-based Android Malware Detection and Prevention

Android users are constantly threatened by an increasing number of malicious applications (apps), generically called malware. Malware constitutes a serious threat to user privacy, money, device and file integrity. In this paper we note that, by studying their actions, we can classify malware into a small number of behavioral classes, each of which performs a limited set of misbehaviors that characterize them. These misbehaviors can be defined by monitoring features belonging to different Android levels. In this paper we present MADAM, a novel host-based malware detection system for Android devices which simultaneously analyzes and correlates features at four levels: kernel, application, user and package, to detect and stop malicious behaviors. MADAM has been designed to take into account those behaviors characteristics of almost every real malware which can be found in the wild. MADAM detects and effectively blocks more than 96% of malicious apps, which come from three large datasets with about 2,800 apps, by exploiting the cooperation of two parallel classifiers and a behavioral signature-based detector. Extensive experiments, which also includes the analysis of a testbed of 9,804 genuine apps, have been conducted to show the low false alarm rate, the negligible performance overhead and limited battery consumption

A Survey on Security for Mobile Devices

Nowadays, mobile devices are an important part of our everyday lives since they enable us to access a large variety of ubiquitous services. In recent years, the availability of these ubiquitous and mobile services has signicantly increased due to the dierent form of connectivity provided by mobile devices, such as GSM, GPRS, Bluetooth and Wi-Fi. In the same trend, the number and typologies of vulnerabilities exploiting these services and communication channels have increased as well. Therefore, smartphones may now represent an ideal target for malware writers. As the number of vulnerabilities and, hence, of attacks increase, there has been a corresponding rise of security solutions proposed by researchers. Due to the fact that this research eld is immature and still unexplored in depth, with this paper we aim to provide a structured and comprehensive overview of the research on security solutions for mobile devices. This paper surveys the state of the art on threats, vulnerabilities and security solutions over the period 2004-2011. We focus on high-level attacks, such those to user applications, through SMS/MMS, denial-of-service, overcharging and privacy. We group existing approaches aimed at protecting mobile devices against these classes of attacks into dierent categories, based upon the detection principles, architectures, collected data and operating systems, especially focusing on IDS-based models and tools. With this categorization we aim to provide an easy and concise view of the underlying model adopted by each approach