An introduction to software obfuscation.

Obfuscation protects software by making the code more difficult to understand. We review a collection of obfuscation techniques. We then consider what would constitute a theory of obfuscation. Several possibilities that could lead to such a theory are explored

Adversarial Machine Learning for the Protection of Legitimate Software

Obfuscation is the transforming a given program into one that is syntactically different but semantically equivalent. This new obfuscated program now has its code and/or data changed so that they are hidden and difficult for attackers to understand. Obfuscation is an important security tool and used to defend against reverse engineering. When applied to a program, different transformations can be observed to exhibit differing degrees of complexity and changes to the program. Recent work has shown, by studying these side effects, one can associate patterns with different transformations. By taking this into account and attempting to profile these unique side effects, it is possible to create a classifier using machine learning which can analyze transformed software and identifies what transformation was used to put it in its current state. This has the effect of weakening the security of obfuscating transformations used to protect legitimate software. In this research, we explore options to increase the robustness of obfuscation against attackers who utilize machine learning, particular those who use it to identify the type of obfuscation being employed. To accomplish this, we segment our research into three stages. For the first stage, we implement a suite of classifiers that are used to xiv identify the obfuscation used in samples. These establish a baseline for determining the effectiveness of our proposed defenses and make use of three varied feature sets. For the second stage, we explore methods to evade detection by the classifiers. To accomplish this, attacks setup using the principles of adversarial machine learning are carried out as evasion attacks. These attacks take an obfuscated program and make subtle changes to various aspects that will cause it to be mislabeled by the classifiers. The changes made to the programs affect features looked at by our classifiers, focusing mainly on the number and distribution of opcodes within the program. A constraint of these changes is that the program remains semantically unchanged. In addition, we explore a means of algorithmic dead code insertion in to achieve comparable results against a broader range of classifiers. In the third stage, we combine our attack strategies and evaluate the effect of our changes on the strength of obfuscating transformations. We also propose a framework to implement and automate these and other measures. We the following contributions: 1. An evaluation of the effectiveness of supervised learning models at labeling obfuscated transformations. We create these models using three unique feature sets: Code Images, Opcode N-grams, and Gadgets. 2. Demonstration of two approaches to algorithmic dummy code insertion designed to improve the stealth of obfuscating transformations against machine learning: Adversarial Obfuscation and Opcode Expansion 3. A unified version of our two defenses capable of achieving effectiveness against a broad range of classifiers, while also demonstrating its impact on obfuscation metrics

Security and trust in cloud computing and IoT through applying obfuscation, diversification, and trusted computing technologies

Cloud computing and Internet of Things (IoT) are very widely spread and commonly used technologies nowadays. The advanced services offered by cloud computing have made it a highly demanded technology. Enterprises and businesses are more and more relying on the cloud to deliver services to their customers. The prevalent use of cloud means that more data is stored outside the organization’s premises, which raises concerns about the security and privacy of the stored and processed data. This highlights the significance of effective security practices to secure the cloud infrastructure. The number of IoT devices is growing rapidly and the technology is being employed in a wide range of sectors including smart healthcare, industry automation, and smart environments. These devices collect and exchange a great deal of information, some of which may contain critical and personal data of the users of the device. Hence, it is highly significant to protect the collected and shared data over the network; notwithstanding, the studies signify that attacks on these devices are increasing, while a high percentage of IoT devices lack proper security measures to protect the devices, the data, and the privacy of the users. In this dissertation, we study the security of cloud computing and IoT and propose software-based security approaches supported by the hardware-based technologies to provide robust measures for enhancing the security of these environments. To achieve this goal, we use obfuscation and diversification as the potential software security techniques. Code obfuscation protects the software from malicious reverse engineering and diversification mitigates the risk of large-scale exploits. We study trusted computing and Trusted Execution Environments (TEE) as the hardware-based security solutions. Trusted Platform Module (TPM) provides security and trust through a hardware root of trust, and assures the integrity of a platform. We also study Intel SGX which is a TEE solution that guarantees the integrity and confidentiality of the code and data loaded onto its protected container, enclave. More precisely, through obfuscation and diversification of the operating systems and APIs of the IoT devices, we secure them at the application level, and by obfuscation and diversification of the communication protocols, we protect the communication of data between them at the network level. For securing the cloud computing, we employ obfuscation and diversification techniques for securing the cloud computing software at the client-side. For an enhanced level of security, we employ hardware-based security solutions, TPM and SGX. These solutions, in addition to security, ensure layered trust in various layers from hardware to the application. As the result of this PhD research, this dissertation addresses a number of security risks targeting IoT and cloud computing through the delivered publications and presents a brief outlook on the future research directions.Pilvilaskenta ja esineiden internet ovat nykyään hyvin tavallisia ja laajasti sovellettuja tekniikkoja. Pilvilaskennan pitkälle kehittyneet palvelut ovat tehneet siitä hyvin kysytyn teknologian. Yritykset enenevässä määrin nojaavat pilviteknologiaan toteuttaessaan palveluita asiakkailleen. Vallitsevassa pilviteknologian soveltamistilanteessa yritykset ulkoistavat tietojensa käsittelyä yrityksen ulkopuolelle, minkä voidaan nähdä nostavan esiin huolia taltioitavan ja käsiteltävän tiedon turvallisuudesta ja yksityisyydestä. Tämä korostaa tehokkaiden turvallisuusratkaisujen merkitystä osana pilvi-infrastruktuurin turvaamista. Esineiden internet -laitteiden lukumäärä on nopeasti kasvanut. Teknologiana sitä sovelletaan laajasti monilla sektoreilla, kuten älykkäässä terveydenhuollossa, teollisuusautomaatiossa ja älytiloissa. Sellaiset laitteet keräävät ja välittävät suuria määriä informaatiota, joka voi sisältää laitteiden käyttäjien kannalta kriittistä ja yksityistä tietoa. Tästä syystä johtuen on erittäin merkityksellistä suojata verkon yli kerättävää ja jaettavaa tietoa. Monet tutkimukset osoittavat esineiden internet -laitteisiin kohdistuvien tietoturvahyökkäysten määrän olevan nousussa, ja samaan aikaan suuri osuus näistä laitteista ei omaa kunnollisia teknisiä ominaisuuksia itse laitteiden tai niiden käyttäjien yksityisen tiedon suojaamiseksi. Tässä väitöskirjassa tutkitaan pilvilaskennan sekä esineiden internetin tietoturvaa ja esitetään ohjelmistopohjaisia tietoturvalähestymistapoja turvautumalla osittain laitteistopohjaisiin teknologioihin. Esitetyt lähestymistavat tarjoavat vankkoja keinoja tietoturvallisuuden kohentamiseksi näissä konteksteissa. Tämän saavuttamiseksi työssä sovelletaan obfuskaatiota ja diversifiointia potentiaalisiana ohjelmistopohjaisina tietoturvatekniikkoina. Suoritettavan koodin obfuskointi suojaa pahantahtoiselta ohjelmiston takaisinmallinnukselta ja diversifiointi torjuu tietoturva-aukkojen laaja-alaisen hyödyntämisen riskiä. Väitöskirjatyössä tutkitaan luotettua laskentaa ja luotettavan laskennan suoritusalustoja laitteistopohjaisina tietoturvaratkaisuina. TPM (Trusted Platform Module) tarjoaa turvallisuutta ja luottamuksellisuutta rakentuen laitteistopohjaiseen luottamukseen. Pyrkimyksenä on taata suoritusalustan eheys. Työssä tutkitaan myös Intel SGX:ää yhtenä luotettavan suorituksen suoritusalustana, joka takaa suoritettavan koodin ja datan eheyden sekä luottamuksellisuuden pohjautuen suojatun säiliön, saarekkeen, tekniseen toteutukseen. Tarkemmin ilmaistuna työssä turvataan käyttöjärjestelmä- ja sovellusrajapintatasojen obfuskaation ja diversifioinnin kautta esineiden internet -laitteiden ohjelmistokerrosta. Soveltamalla samoja tekniikoita protokollakerrokseen, työssä suojataan laitteiden välistä tiedonvaihtoa verkkotasolla. Pilvilaskennan turvaamiseksi työssä sovelletaan obfuskaatio ja diversifiointitekniikoita asiakaspuolen ohjelmistoratkaisuihin. Vankemman tietoturvallisuuden saavuttamiseksi työssä hyödynnetään laitteistopohjaisia TPM- ja SGX-ratkaisuja. Tietoturvallisuuden lisäksi nämä ratkaisut tarjoavat monikerroksisen luottamuksen rakentuen laitteistotasolta ohjelmistokerrokseen asti. Tämän väitöskirjatutkimustyön tuloksena, osajulkaisuiden kautta, vastataan moniin esineiden internet -laitteisiin ja pilvilaskentaan kohdistuviin tietoturvauhkiin. Työssä esitetään myös näkemyksiä jatkotutkimusaiheista

Evaluation Methodologies in Software Protection Research

Man-at-the-end (MATE) attackers have full control over the system on which the attacked software runs, and try to break the confidentiality or integrity of assets embedded in the software. Both companies and malware authors want to prevent such attacks. This has driven an arms race between attackers and defenders, resulting in a plethora of different protection and analysis methods. However, it remains difficult to measure the strength of protections because MATE attackers can reach their goals in many different ways and a universally accepted evaluation methodology does not exist. This survey systematically reviews the evaluation methodologies of papers on obfuscation, a major class of protections against MATE attacks. For 572 papers, we collected 113 aspects of their evaluation methodologies, ranging from sample set types and sizes, over sample treatment, to performed measurements. We provide detailed insights into how the academic state of the art evaluates both the protections and analyses thereon. In summary, there is a clear need for better evaluation methodologies. We identify nine challenges for software protection evaluations, which represent threats to the validity, reproducibility, and interpretation of research results in the context of MATE attacks

Looking for Criminal Intents in JavaScript Obfuscated Code

The majority of websites incorporate JavaScript for client-side execution in a supposedly protected environment. Unfortunately, JavaScript has also proven to be a critical attack vector for both independent and state-sponsored groups of hackers. On the one hand, defenders need to analyze scripts to ensure that no threat is delivered and to respond to potential security incidents. On the other, attackers aim to obfuscate the source code in order to disorient the defenders or even to make code analysis practically impossible. Since code obfuscation may also be adopted by companies for legitimate intellectual-property protection, a dilemma remains on whether a script is harmless or malignant, if not criminal. To help analysts deal with such a dilemma, a methodology is proposed, called JACOB, which is based on five steps, namely: (1) source code parsing, (2) control flow graph recovery, (3) region identification, (4) code structuring, and (5) partial evaluation. These steps implement a sort of decompilation for control flow flattened code, which is progressively transformed into something that is close to the original JavaScript source, thereby making eventual code analysis possible. Most relevantly, JACOB has been successfully applied to uncover unwanted user tracking and fingerprinting in e-commerce websites operated by a well-known Chinese company

Diversification and obfuscation techniques for software security: A systematic literature review

Context: Diversification and obfuscation are promising techniques for securing software and protecting computers from harmful malware. The goal of these techniques is not removing the security holes, but making it difficult for the attacker to exploit security vulnerabilities and perform successful attacks.Objective: There is an increasing body of research on the use of diversification and obfuscation techniques for improving software security; however, the overall view is scattered and the terminology is unstructured. Therefore, a coherent review gives a clear statement of state-of-the-art, normalizes the ongoing discussion and provides baselines for future research.Method: In this paper, systematic literature review is used as the method of the study to select the studies that discuss diversification/obfuscation techniques for improving software security. We present the process of data collection, analysis of data, and report the results.Results: As the result of the systematic search, we collected 357 articles relevant to the topic of our interest, published between the years 1993 and 2017. We studied the collected articles, analyzed the extracted data from them, presented classification of the data, and enlightened the research gaps.Conclusion: The two techniques have been extensively used for various security purposes and impeding various types of security attacks. There exist many different techniques to obfuscate/diversify programs, each of which targets different parts of the programs and is applied at different phases of software development life-cycle. Moreover, we pinpoint the research gaps in this field, for instance that there are still various execution environments that could benefit from these two techniques, including cloud computing, Internet of Things (IoT), and trusted computing. We also present some potential ideas on applying the techniques on the discussed environments.</p