Malicious software in form of Internet worms, computer viruses, and Trojan horses poses a major threat to the security of networked systems. The diversity and amount of its variants severely undermine the effectiveness of classical signature-based detection. Yet variants of malware families share typical behavioral patternsreflecting its origin and purpose. We aim to exploit these shared patterns for classification of malware and propose a method for learning and discrimination of malware behavior. Our method proceeds in three stages: (a) behavior of collected malware is monitored in a sandbox environment, (b) based on a corpus of malware labeled by an anti-virus scanner a malware behavior classifieris trained using learning techniques and (c) discriminative features of the behavior models are ranked for explanation of classification decisions. Experiments with different heterogeneous test data collected over several months using honeypots demonstrate the effectiveness of our method, especially in detecting novelinstances of malware families previously not recognized by commercial anti-virus software.

/pdf/learning-and-classification-of-malware-behavior-46fi5y2c30.pdf

Learning and Classification of Malware Behavior

This introduction to the Security Development Lifecycle (SDL) provides a history of the methodology and guides you through each stage of a proven process-from design to release-that helps minimize security defects.

/pdf/the-security-development-lifecycle-vc6r51s5rg.pdf

The Security Development Lifecycle

Because of rampant security breaches in IoT devices, searching vulnerabilities in massive IoT ecosystems is more crucial than ever. Recent studies have demonstrated that control-flow graph (CFG) based bug search techniques can be effective and accurate in IoT devices across different architectures. However, these CFG-based bug search approaches are far from being scalable to handle an enormous amount of IoT devices in the wild, due to their expensive graph matching overhead. Inspired by rich experience in image and video search, we propose a new bug search scheme which addresses the scalability challenge in existing cross-platform bug search techniques and further improves search accuracy. Unlike existing techniques that directly conduct searches based upon raw features (CFGs) from the binary code, we convert the CFGs into high-level numeric feature vectors. Compared with the CFG feature, high-level numeric feature vectors are more robust to code variation across different architectures, and can easily achieve realtime search by using state-of-the-art hashing techniques. We have implemented a bug search engine, Genius, and compared it with state-of-art bug search approaches. Experimental results show that Genius outperforms baseline approaches for various query loads in terms of speed and accuracy. We also evaluated Genius on a real-world dataset of 33,045 devices which was collected from public sources and our system. The experiment showed that Genius can finish a search within 1 second on average when performed over 8,126 firmware images of 420,558,702 functions. By only looking at the top 50 candidates in the search result, we found 38 potentially vulnerable firmware images across 5 vendors, and confirmed 23 of them by our manual analysis. We also found that it took only 0.1 seconds on average to finish searching for all 154 vulnerabilities in two latest commercial firmware images from D-LINK. 103 of them are potentially vulnerable in these images, and 16 of them were confirmed.

Scalable Graph-based Bug Search for Firmware Images

The identification of security-critical vulnerabilities is a key for protecting computer systems. Being able to perform this process at the binary level is very important given that many software projects are closed-source. Even if the source code is available, compilation may create a mismatch between the source code and the binary code that is executed by the processor, causing analyses that are performed on source code to fail at detecting certain bugs and thus potential vulnerabilities. Existing approaches to find bugs in binary code 1) use dynamic analysis, which is difficult for firmware; 2) handle only a single architecture; or 3) use semantic similarity, which is very slow when analyzing large code bases. In this paper, we present a new approach to efficiently search for similar functions in binary code. We use this method to identify known bugs in binaries as follows: starting with a vulnerable binary function, we identify similar functions in other binaries across different compilers, optimization levels, operating systems, and CPU architectures. The main idea is to compute similarity between functions based on the structure of the corresponding control flow graphs. To minimize this costly computation, we employ an efficient pre-filter based on numeric features to quickly identify a small set of candidate functions. This allows us to efficiently search for similar functions in large code bases. We have designed and implemented a prototype of our approach, called discovRE, that supports four instruction set architectures (x86, x64, ARM, MIPS). We show that discovRE is four orders of magnitude faster than the state-of-the-art academic approach for cross-architecture bug search in binaries. We also show that we can identify Heartbleed and POODLE vulnerabilities in an Android system image that contains over 130,000 native ARM functions in about 80 milliseconds.

discovRE: Efficient Cross-Architecture Identification of Bugs in Binary Code.

We introduce BinHunt, a novel technique for finding semantic differences in binary programs. Semantic differences between two binary files contrast with syntactic differences in that semantic differences correspond to changes in the program functionality. Semantic differences are difficult to find because of the noise from syntactic differences caused by, e.g., different register allocation and basic block re-ordering. BinHunt bases its analysis on the control flow of the programs using a new graph isomorphism technique, symbolic execution, and theorem proving. We implement a system based on BinHunt and demonstrate the application of the system with three case studies in which BinHunt manages to identify the semantic differences between an executable and its patched version, revealing the vulnerability that the patch eliminates.

/pdf/binhunt-automatically-finding-semantic-differences-in-binary-35i6kwwqpj.pdf

BinHunt: Automatically Finding Semantic Differences in Binary Programs

A method to heuristically construct an isomorphism between the sets of functions in two similar but differing versions of the same executable file is presented. Such an isomorphism has multiple practical applications, specifically the ability to detect programmatic changes between the two executable versions. Moreover, information (function names) which is available for one of the two versions can also be made available for the other . A framework implementing the described methods is presented, along with empirical data about its performance when used to analyze patches to recent security vulnerabilities. As a more practical example, a security update which fixes a critical vulnerability in an H.323 parsing component is analyzed, the relevant vulnerability extracted and the implications of the vulnerability and the fix discussed.

/pdf/structural-comparison-of-executable-objects-3fnuqcc3ud.pdf

Structural Comparison of Executable Objects

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Halvar Flake

Author Tools

Chat about Author

Papers

Structural Comparison of Executable Objects