Intrusion detection techniques in cloud environment

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.

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

Cryptography plays an important role in computer and communication security. In practical implementations of cryptosystems, the cryptographic keys are usually loaded into the memory as plaintext, and then used in the cryptographic algorithms. Therefore, the private keys are subject to memory disclosure attacks that read unauthorized data from RAM. Such attacks could be performed through software methods (e.g., Open SSL Heart bleed) even when the integrity of the victim system's executable binaries is maintained. They could also be performed through physical methods (e.g., Cold-boot attacks on RAM chips) even when the system is free of software vulnerabilities. In this paper, we propose Mimosa that protects RSA private keys against the above software-based and physical memory attacks. When the Mimosa service is in idle, private keys are encrypted and reside in memory as cipher text. During the cryptographic computing, Mimosa uses hardware transactional memory (HTM) to ensure that (a) whenever a malicious process other than Mimosa attempts to read the plaintext private key, the transaction aborts and all sensitive data are automatically cleared with hardware mechanisms, due to the strong atomicity guarantee of HTM, and (b) all sensitive data, including private keys and intermediate states, appear as plaintext only within CPU-bound caches, and are never loaded to RAM chips. To the best of our knowledge, Mimosa is the first solution to use transactional memory to protect sensitive data against memory disclosure attacks. We have implemented Mimosa on a commodity machine with Intel Core i7 Has well CPUs. Through extensive experiments, we show that Mimosa effectively protects cryptographic keys against various attacks that attempt to read sensitive data from memory, and it only introduces a small performance overhead.

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Protecting Private Keys against Memory Disclosure Attacks Using Hardware Transactional Memory

Author(s): Song, Dokyung; Hetzelt, Felicitas; Das, Dipanjan; Spensky, Chad; Na, Yeoul; Volckaert, Stijn; Vigna, Giovanni; Kruegel, Christopher; Seifert, Jean-Pierre; Franz, Michael

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PeriScope: An Effective Probing and Fuzzing Framework for the Hardware-OS Boundary

Virtualization technology enables Cloud providers to efficiently use their computing services and resources. Even if the benefits in terms of performance, maintenance, and cost are evident, however, virtualization has also been exploited by attackers to devise new ways to compromise a system. To address these problems, research security solutions have evolved considerably over the years to cope with new attacks and threat models. In this work, we review the protection strategies proposed in the literature and show how some of the solutions have been invalidated by new attacks, or threat models, that were previously not considered. The goal is to show the evolution of the threats, and of the related security and trust assumptions, in virtualized systems that have given rise to complex threat models and the corresponding sophistication of protection strategies to deal with such attacks. We also categorize threat models, security and trust assumptions, and attacks against a virtualized system at the different layers—in particular, hardware, virtualization, OS, and application.

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Evolution of Attacks, Threat Models, and Solutions for Virtualized Systems

Attackers constantly explore ways to camouflage illicit activities against computer platforms. Stealthy attacks are required in industrial espionage and also by criminals stealing banking credentials. Modern computers contain dedicated hardware such as network and graphics cards. Such devices implement independent execution environments but have direct memory access (DMA) to the host runtime memory. In this work we introduce DMA malware, i.e., malware executed on dedicated hardware to launch stealthy attacks against the host using DMA. DMA malware goes beyond the capability to control DMA hardware. We implemented DAGGER, a keylogger that attacks Linux and Windows platforms. Our evaluation confirms that DMA malware can efficiently attack kernel structures even if memory address randomization is in place. DMA malware is stealthy to a point where the host cannot detect its presense. We evaluate and discuss possible countermeasures and the (in)effectiveness of hardware extensions such as input/output memory management units.

Iurii Bystrov

Papers

Understanding DMA malware