Malware is one of the most serious security threats on the Internet today. In fact, most Internet problems such as spam e-mails and denial of service attacks have malware as their underlying cause. That is, computers that are compromised with malware are often networked together to form botnets, and many attacks are launched using these malicious, attacker-controlled networks.

With the increasing significance of malware in Internet attacks, much research has concentrated on developing techniques to collect, study, and mitigate malicious code. Without doubt, it is important to collect and study malware found on the Internet. However, it is even more important to develop mitigation and detection techniques based on the insights gained from the analysis work. Unfortunately, current host-based detection approaches (i.e., anti-virus software) suffer from ineffective detection models. These models concentrate on the features of a specific malware instance, and are often easily evadable by obfuscation or polymorphism. Also, detectors that check for the presence of a sequence of system calls exhibited by a malware instance are often evadable by system call reordering. In order to address the shortcomings of ineffectivemodels, several dynamic detection approaches have been proposed that aim to identify the behavior exhibited by a malware family. Although promising, these approaches are unfortunately too slow to be used as real-time detectors on the end host, and they often require cumbersome virtual machine technology.

In this paper, we propose a novel malware detection approach that is both effective and efficient, and thus, can be used to replace or complement traditional antivirus software at the end host. Our approach first analyzes a malware program in a controlled environment to build a model that characterizes its behavior. Such models describe the information flows between the system calls essential to the malware's mission, and therefore, cannot be easily evaded by simple obfuscation or polymorphic techniques. Then, we extract the program slices responsible for such information flows. For detection, we execute these slices to match our models against the runtime behavior of an unknown program. Our experiments show that our approach can effectively detect running malicious code on an end user's host with a small overhead.

/pdf/effective-and-efficient-malware-detection-at-the-end-host-55y8dh11qs.pdf

Effective and efficient malware detection at the end host

In this paper, we present the results of a long-term study of ransomware attacks that have been observed in the wild between 2006 and 2014. We also provide a holistic view on how ransomware attacks have evolved during this period by analyzing 1,359 samples that belong to 15 different ransomware families. Our results show that, despite a continuous improvement in the encryption, deletion, and communication techniques in the main ransomware families, the number of families with sophisticated destructive capabilities remains quite small. In fact, our analysis reveals that in a large number of samples, the malware simply locks the victim's computer desktop or attempts to encrypt or delete the victim's files using only superficial techniques.i?źOur analysis also suggests that stopping advanced ransomware attacks is not as complex as it has been previously reported. For example, we show that by monitoring abnormal file system activity, it is possible to design a practical defense system that could stop a large number of ransomware attacks, even those using sophisticated encryption capabilities. A close examination on the file system activities of multiple ransomware samples suggests that by looking at I/O requests and protecting Master File Table MFT in the NTFS file system, it is possible to detect and prevent a significant number of zero-day ransomware attacks.

/pdf/cutting-the-gordian-knot-a-look-under-the-hood-of-ransomware-4ggofmzmta.pdf

Cutting the Gordian Knot: A Look Under the Hood of Ransomware Attacks

The execution of malware in an instrumented sandbox is a widespread approach for the analysis of malicious code, largely because it sidesteps the difficulties involved in the static analysis of obfuscated code As malware analysis sandboxes increase in popularity, they are faced with the problem of malicious code detecting the instrumented environment to evade analysis In the absence of an "undetectable", fully transparent analysis sandbox, defense against sandbox evasion is mostly reactive: Sandbox developers and operators tweak their systems to thwart individual evasion techniques as they become aware of them, leading to a never-ending arms race

The goal of this work is to automate one step of this fight: Screening malware samples for evasive behavior Thus, we propose novel techniques for detecting malware samples that exhibit semantically different behavior across different analysis sandboxes These techniques are compatible with any monitoring technology that can be used for dynamic analysis, and are completely agnostic to the way that malware achieves evasion We implement the proposed techniques in a tool called Disarm, and demonstrate that it can accurately detect evasive malware, leading to the discovery of previously unknown evasion techniques

/pdf/detecting-environment-sensitive-malware-57989w77o5.pdf

Detecting environment-sensitive malware

Estonia was the first country in the world to use Internet voting nationally, and today more than 30% of its ballots are cast online. In this paper, we analyze the security of the Estonian I-voting system based on a combination of in-person election observation, code review, and adversarial testing. Adopting a threat model that considers the advanced threats faced by a national election system---including dishonest insiders and state-sponsored attacks---we find that the I-voting system has serious architectural limitations and procedural gaps that potentially jeopardize the integrity of elections. In experimental attacks on a reproduction of the system, we demonstrate how such attackers could target the election servers or voters' clients to alter election results or undermine the legitimacy of the system. Our findings illustrate the practical obstacles to Internet voting in the modern world, and they carry lessons for Estonia, for other countries considering adopting such systems, and for the security research community.

/pdf/security-analysis-of-the-estonian-internet-voting-system-572fooy5sq.pdf

Security Analysis of the Estonian Internet Voting System

Although malicious software (malware) has been around since the early days of computers, the sophistication and innovation of malware has increased over the years. In particular, the latest crop of ransomware has drawn attention to the dangers of malicious software, which can cause harm to private users as well as corporations, public services (hospitals and transportation systems), governments, and security institutions. To protect these institutions and the public from malware attacks, malicious activity must be detected as early as possible, preferably before it conducts its harmful acts. However, it is not always easy to know what to look for—especially when dealing with new and unknown malware that has never been seen. Analyzing a suspicious file by static or dynamic analysis methods can provide relevant and valuable information regarding a file's impact on the hosting system and help determine whether the file is malicious or not, based on the method's predefined rules. While various techniques (e.g., code obfuscation, dynamic code loading, encryption, and packing) can be used by malware writers to evade static analysis (including signature-based anti-virus tools), dynamic analysis is robust to these techniques and can provide greater understanding regarding the analyzed file and consequently can lead to better detection capabilities. Although dynamic analysis is more robust than static analysis, existing dynamic analysis tools and techniques are imperfect, and there is no single tool that can cover all aspects of malware behavior. The most recent comprehensive survey performed in this area was published in 2012. Since that time, the computing environment has changed dramatically with new types of malware (ransomware, cryptominers), new analysis methods (volatile memory forensics, side-channel analysis), new computing environments (cloud computing, IoT devices), new machine-learning algorithms, and more. The goal of this survey is to provide a comprehensive and up-to-date overview of existing methods used to dynamically analyze malware, which includes a description of each method, its strengths and weaknesses, and its resilience against malware evasion techniques. In addition, we include an overview of prominent studies presenting the usage of machine-learning methods to enhance dynamic malware analysis capabilities aimed at detection, classification, and categorization.

/pdf/dynamic-malware-analysis-in-the-modern-era-a-state-of-the-47zce4d0kw.pdf

Dynamic Malware Analysis in the Modern Era—A State of the Art Survey

"It's imperative that everybody working in the field of cyber-security read this book to understand the growing threat of rootkits." --Mark Russinovich, editor, Windows IT Pro / Windows & .NET Magazine"This material is not only up-to-date, it defines up-to-date. It is truly cutting-edge. As the only book on the subject, Rootkits will be of interest to any Windows security researcher or security programmer. It's detailed, well researched and the technical information is excellent. The level of technical detail, research, and time invested in developing relevant examples is impressive. In one word: Outstanding." --Tony Bautts, Security Consultant; CEO, Xtivix, Inc."This book is an essential read for anyone responsible for Windows security. Security professionals, Windows system administrators, and programmers in general will want to understand the techniques used by rootkit authors. At a time when many IT and security professionals are still worrying about the latest e-mail virus or how to get all of this month's security patches installed, Mr. Hoglund and Mr. Butler open your eyes to some of the most stealthy and significant threats to the Windows operating system. Only by understanding these offensive techniques can you properly defend the networks and systems for which you are responsible." --Jennifer Kolde, Security Consultant, Author, and Instructor"What's worse than being owned? Not knowing it. Find out what it means to be owned by reading Hoglund and Butler's first-of-a-kind book on rootkits. At the apex the malicious hacker toolset--which includes decompilers, disassemblers, fault-injection engines, kernel debuggers, payload collections, coverage tools, and flow analysis tools--is the rootkit. Beginning where Exploiting Software left off, this book shows how attackers hide in plain sight. "Rootkits are extremely powerful and are the next wave of attack technology. Like other types of malicious code, rootkits thrive on stealthiness. They hide away from standard system observers, employing hooks, trampolines, and patches to get their work done. Sophisticated rootkits run in such a way that other programs that usually monitor machine behavior can't easily detect them. A rootkit thus provides insider access only to people who know that it is running and available to accept commands. Kernel rootkits can hide files and running processes to provide a backdoor into the target machine. "Understanding the ultimate attacker's tool provides an important motivator for those of us trying to defend systems. No authors are better suited to give you a detailed hands-on understanding of rootkits than Hoglund and Butler. Better to own this book than to be owned." --Gary McGraw, Ph.D., CTO, Cigital, coauthor of Exploiting Software (2004) and Building Secure Software (2002), both from Addison-Wesley"Greg and Jamie are unquestionably the go-to experts when it comes to subverting the Windows API and creating rootkits. These two masters come together to pierce the veil of mystery surrounding rootkits, bringing this information out of the shadows. Anyone even remotely interested in security for Windows systems, including forensic analysis, should include this book very high on their must-read list." --Harlan Carvey, author of Windows Forensics and Incident Recovery (Addison-Wesley, 2005)Rootkits are the ultimate backdoor, giving hackers ongoing and virtually undetectable access to the systems they exploit. Now, two of the world's leading experts have written the first comprehensive guide to rootkits: what they are, how they work, how to build them, and how to detect them. Rootkit.com's Greg Hoglund and James Butler created and teach Black Hat's legendary course in rootkits. In this book, they reveal never-before-told offensive aspects of rootkit technology--learn how attackers can get in and stay in for years, without detection.Hoglund and Butler show exactly how to subvert the Windows XP and Windows 2000 kernels, teaching concepts that are easily applied to virtually any modern operating system, from Windows Server 2003 to Linux and UNIX. Using extensive downloadable examples, they teach rootkit programming techniques that can be used for a wide range of software, from white hat security tools to operating system drivers and debuggers.After reading this book, readers will be able to Understand the role of rootkits in remote command/control and software eavesdropping Build kernel rootkits that can make processes, files, and directories invisible Master key rootkit programming techniques, including hooking, runtime patching, and directly manipulating kernel objects Work with layered drivers to implement keyboard sniffers and file filters Detect rootkits and build host-based intrusion prevention software that resists rootkit attacksVisit rootkit.com for code and programs from this book. The site also contains enhancements to the book's text, such as up-to-the-minute information on rootkits available nowhere else.

Rootkits: Subverting the Windows Kernel

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Rootkits: Subverting the Windows Kernel