A fast and scalable binary similarity method for open source libraries

Abstract. Usage of third party open source software has become more and more popular in the past years, due to the need for faster development cycles and the availability of good quality libraries. Those libraries are integrated as dependencies and often in the form of binary artifacts. This is especially common in embedded software applications. Dependencies, however, can proliferate and also add new attack surfaces to an application due to vulnerabilities in the library code. Hence, the need for binary similarity analysis methods to detect libraries compiled into applications. Binary similarity detection methods are related to text similarity methods and build upon the research in that area. In this research we focus on fuzzy matching methods, that have been used widely and successfully in text similarity analysis. In particular, we propose using locality sensitive hashing schemes in combination with normalised binary code features. The normalization allows us to apply the similarity comparison across binaries produced by different compilers using different optimization flags and being build for various machine architectures. To improve the matching precision, we use weighted code features. Machine learning is used to optimize the feature weights to create clusters of semantically similar code blocks extracted from different binaries. The machine learning is performed in an offline process to increase scalability and performance of the matching system. Using above methods we build a database of binary similarity code signatures for open source libraries. The database is utilized to match by similarity any code blocks from an application to known libraries in the database. One of the goals of our system is to facilitate a fast and scalable similarity matching process. This allows integrating the system into continuous software development, testing and integration pipelines. The evaluation shows that our results are comparable to other systems proposed in related research in terms of precision while maintaining the performance required in continuous integration systems.Nopea ja skaalautuva käännettyjen ohjelmistojen samankaltaisuuden tunnistusmenetelmä avoimen lähdekoodin kirjastoille. Tiivistelmä. Kolmansien osapuolten kehittämien ohjelmistojen käyttö on yleistynyt valtavasti viime vuosien aikana nopeutuvan ohjelmistokehityksen ja laadukkaiden ohjelmistokirjastojen tarjonnan kasvun myötä. Nämä kirjastot ovat yleensä lisätty kehitettävään ohjelmistoon riippuvuuksina ja usein jopa käännettyinä binääreinä. Tämä on yleistä varsinkin sulatetuissa ohjelmistoissa. Riippuvuudet saattavat kuitenkin luoda uusia hyökkäysvektoreita kirjastoista löytyvien haavoittuvuuksien johdosta. Nämä kolmansien osapuolten kirjastoista löytyvät haavoittuvuudet synnyttävät tarpeen tunnistaa käännetyistä binääriohjelmistoista löytyvät avoimen lähdekoodin ohjelmistokirjastot. Binäärien samankaltaisuuden tunnistusmenetelmät usein pohjautuvat tekstin samankaltaisuuden tunnistusmenetelmiin ja hyödyntävät tämän tieteellisiä saavutuksia. Tässä tutkimuksessa keskitytään sumeisiin tunnistusmenetelmiin, joita on käytetty laajasti tekstin samankaltaisuuden tunnistamisessa. Tutkimuksessa hyödynnetään sijainnille sensitiivisiä tiivistemenetelmiä ja normalisoituja binäärien ominaisuuksia. Ominaisuuksien normalisoinnin avulla binäärien samankaltaisuutta voidaan vertailla ohjelmiston kääntämisessä käytetystä kääntäjästä, optimisaatiotasoista ja prosessoriarkkitehtuurista huolimatta. Menetelmän tarkkuutta parannetaan painotettujen binääriominaisuuksien avulla. Koneoppimista hyödyntämällä binääriomisaisuuksien painotus optimoidaan siten, että samankaltaisista binääreistä puretut ohjelmistoblokit luovat samankaltaisien ohjelmistojen joukkoja. Koneoppiminen suoritetaan erillisessä prosessissa, mikä parantaa järjestelmän suorituskykyä. Näiden menetelmien avulla luodaan tietokanta avoimen lähdekoodin kirjastojen tunnisteista. Tietokannan avulla minkä tahansa ohjelmiston samankaltaiset binääriblokit voidaan yhdistää tunnettuihin avoimen lähdekoodin kirjastoihin. Menetelmän tavoitteena on tarjota nopea ja skaalautuva samankaltaisuuden tunnistus. Näiden ominaisuuksien johdosta järjestelmä voidaan liittää osaksi ohjelmistokehitys-, integraatioprosesseja ja ohjelmistotestausta. Vertailu muihin kirjallisuudessa esiteltyihin menetelmiin osoittaa, että esitellyn menetlmän tulokset on vertailtavissa muihin kirjallisuudessa esiteltyihin menetelmiin tarkkuuden osalta. Menetelmä myös ylläpitää suorituskyvyn, jota vaaditaan jatkuvan integraation järjestelmissä

Resilient and Scalable Android Malware Fingerprinting and Detection

Malicious software (Malware) proliferation reaches hundreds of thousands daily. The manual analysis of such a large volume of malware is daunting and time-consuming. The diversity of targeted systems in terms of architecture and platforms compounds the challenges of Android malware detection and malware in general. This highlights the need to design and implement new scalable and robust methods, techniques, and tools to detect Android malware. In this thesis, we develop a malware fingerprinting framework to cover accurate Android malware detection and family attribution. In this context, we emphasize the following: (i) the scalability over a large malware corpus; (ii) the resiliency to common obfuscation techniques; (iii) the portability over different platforms and architectures. In the context of bulk and offline detection on the laboratory/vendor level: First, we propose an approximate fingerprinting technique for Android packaging that captures the underlying static structure of the Android apps. We also propose a malware clustering framework on top of this fingerprinting technique to perform unsupervised malware detection and grouping by building and partitioning a similarity network of malicious apps. Second, we propose an approximate fingerprinting technique for Android malware's behavior reports generated using dynamic analyses leveraging natural language processing techniques. Based on this fingerprinting technique, we propose a portable malware detection and family threat attribution framework employing supervised machine learning techniques. Third, we design an automatic framework to produce intelligence about the underlying malicious cyber-infrastructures of Android malware. We leverage graph analysis techniques to generate relevant, actionable, and granular intelligence that can be used to identify the threat effects induced by malicious Internet activity associated to Android malicious apps. In the context of the single app and online detection on the mobile device level, we further propose the following: Fourth, we design a portable and effective Android malware detection system that is suitable for deployment on mobile and resource constrained devices, using machine learning classification on raw method call sequences. Fifth, we elaborate a framework for Android malware detection that is resilient to common code obfuscation techniques and adaptive to operating systems and malware change overtime, using natural language processing and deep learning techniques. We also evaluate the portability of the proposed techniques and methods beyond Android platform malware, as follows: Sixth, we leverage the previously elaborated techniques to build a framework for cross-platform ransomware fingerprinting relying on raw hybrid features in conjunction with advanced deep learning techniques

The digital harms of smart home devices:a systematic literature review

The connection of home electronic devices to the internet allows remote control of physical devices and involves the collection of large volumes of data. With the increase in the uptake of Internet-of-Things home devices, it becomes critical to understand the digital harms of smart homes. We present a systematic literature review on the security and privacy harms of smart homes. PRISMA methodology is used to systematically review 63 studies published between January 2011 and October 2021; and a review of known cases is undertaken to illustrate the literature review findings with real-world scenarios. Published literature identifies that smart homes may pose threats to confidentiality (unwanted release of information), authentication (sensing information being falsified) and unauthorised access to system controls. Most existing studies focus on privacy intrusions as a prevalent form of harm against smart homes. Other types of harms that are less common in the literature include hacking, malware and DoS attacks. Digital harms, and data associated with these harms, may vary extensively across smart devices. Most studies propose technical measures to mitigate digital harms, while fewer consider social prevention mechanisms. We also identify salient gaps in research, and argue that these should be addressed in future crossdisciplinary research initiatives

Neural malware detection

At the heart of today’s malware problem lies theoretically infinite diversity created by metamorphism. The majority of conventional machine learning techniques tackle the problem with the assumptions that a sufficiently large number of training samples exist and that the training set is independent and identically distributed. However, the lack of semantic features combined with the models under these wrong assumptions result largely in overfitting with many false positives against real world samples, resulting in systems being left vulnerable to various adversarial attacks. A key observation is that modern malware authors write a script that automatically generates an arbitrarily large number of diverse samples that share similar characteristics in program logic, which is a very cost-effective way to evade detection with minimum effort. Given that many malware campaigns follow this paradigm of economic malware manufacturing model, the samples within a campaign are likely to share coherent semantic characteristics. This opens up a possibility of one-to-many detection. Therefore, it is crucial to capture this non-linear metamorphic pattern unique to the campaign in order to detect these seemingly diverse but identically rooted variants. To address these issues, this dissertation proposes novel deep learning models, including generative static malware outbreak detection model, generative dynamic malware detection model using spatio-temporal isomorphic dynamic features, and instruction cognitive malware detection. A comparative study on metamorphic threats is also conducted as part of the thesis. Generative adversarial autoencoder (AAE) over convolutional network with global average pooling is introduced as a fundamental deep learning framework for malware detection, which captures highly complex non-linear metamorphism through translation invariancy and local variation insensitivity. Generative Adversarial Network (GAN) used as a part of the framework enables oneshot training where semantically isomorphic malware campaigns are identified by a single malware instance sampled from the very initial outbreak. This is a major innovation because, to the best of our knowledge, no approach has been found to this challenging training objective against the malware distribution that consists of a large number of very sparse groups artificially driven by arms race between attackers and defenders. In addition, we propose a novel method that extracts instruction cognitive representation from uninterpreted raw binary executables, which can be used for oneto- many malware detection via one-shot training against frequency spectrum of the Transformer’s encoded latent representation. The method works regardless of the presence of diverse malware variations while remaining resilient to adversarial attacks that mostly use random perturbation against raw binaries. Comprehensive performance analyses including mathematical formulations and experimental evaluations are provided, with the proposed deep learning framework for malware detection exhibiting a superior performance over conventional machine learning methods. The methods proposed in this thesis are applicable to a variety of threat environments here artificially formed sparse distributions arise at the cyber battle fronts.Doctor of Philosoph

On Leveraging Next-Generation Deep Learning Techniques for IoT Malware Classification, Family Attribution and Lineage Analysis

Recent years have witnessed the emergence of new and more sophisticated malware targeting insecure Internet of Things (IoT) devices, as part of orchestrated large-scale botnets. Moreover, the public release of the source code of popular malware families such as Mirai [1] has spawned diverse variants, making it harder to disambiguate their ownership, lineage, and correct label. Such a rapidly evolving landscape makes it also harder to deploy and generalize effective learning models against retired, updated, and/or new threat campaigns. To mitigate such threat, there is an utmost need for effective IoT malware detection, classification and family attribution, which provide essential steps towards initiating attack mitigation/prevention countermeasures, as well as understanding the evolutionary trajectories and tangled relationships of IoT malware. This is particularly challenging due to the lack of fine-grained empirical data about IoT malware, the diverse architectures of IoT-targeted devices, and the massive code reuse between IoT malware families. To address these challenges, in this thesis, we leverage the general lack of obfuscation in IoT malware to extract and combine static features from multi-modal views of the executable binaries (e.g., images, strings, assembly instructions), along with Deep Learning (DL) architectures for effective IoT malware classification and family attribution. Additionally, we aim to address concept drift and the limitations of inter-family classification due to the evolutionary nature of IoT malware, by detecting in-class evolving IoT malware variants and interpreting the meaning behind their mutations. To this end, we perform the following to achieve our objectives: First, we analyze 70,000 IoT malware samples collected by a specialized IoT honeypot and popular malware repositories in the past 3 years. Consequently, we utilize features extracted from strings- and image-based representations of IoT malware to implement a multi-level DL architecture that fuses the learned features from each sub-component (i.e, images, strings) through a neural network classifier. Our in-depth experiments with four prominent IoT malware families highlight the significant accuracy of the proposed approach (99.78%), which outperforms conventional single-level classifiers, by relying on different representations of the target IoT malware binaries that do not require expensive feature extraction. Additionally, we utilize our IoT-tailored approach for labeling unknown malware samples, while identifying new malware strains. Second, we seek to identify when the classifier shows signs of aging, by which it fails to effectively recognize new variants and adapt to potential changes in the data. Thus, we introduce a robust and effective method that uses contrastive learning and attentive Transformer models to learn and compare semantically meaningful representations of IoT malware binaries and codes without the need for expensive target labels. We find that the evolution of IoT binaries can be used as an augmentation strategy to learn effective representations to contrast (dis)similar variant pairs. We discuss the impact and findings of our analysis and present several evaluation studies to highlight the tangled relationships of IoT malware, as well as the efficiency of our contrastively learned fine-grained feature vectors in preserving semantics and reducing out-of-vocabulary size in cross-architecture IoT malware binaries. We conclude this thesis by summarizing our findings and discussing research gaps that lay the way for future work

Endpoints and Interdependencies in Internet of Things Residual Artifacts: Measurements, Analyses, and Insights into Defenses

The usage of Internet of Things (IoT) devices is growing fast. Moreover, the lack of security measures among the IoT devices and their persistent online connection give adversaries an opportunity to exploit them for multiple types of attacks, such as distributed denial-of-service (DDoS). To understand the risks of IoT devices, we analyze IoT malware from an endpoint standpoint. We investigate the relationship between endpoints infected and attacked by IoT malware, and gain insights into the underlying dynamics in the malware ecosystem. We observe the affinities and different patterns among endpoints. Towards this, we reverse-engineer 2,423 IoT malware samples and extract IP addresses from them. We further gather information about these endpoints from Internet-wide scans. For masked IP addresses, we examine their network distribution, with networks accumulating more than 100 million endpoints. Moreover, we conduct a network penetration analysis, leveraging information such as active ports, vulnerabilities, and organizations. We discover the possibility of ports being an entry point of attack and observe the low presence of vulnerable services in dropzones. Our analysis shows the tolerance of organizations towards endpoints with malicious intent. To understand the dependencies among malware, we highlight dropzone characteristics including spatial, network, and organizational affinities. Towards the analysis of dropzones\u27 interdependencies and dynamics, we identify dropzones chains. In particular, we identify 56 unique chains, which unveil coordination among different malware families. Our further analysis of chains suggests a centrality-based defense and monitoring mechanism to limit malware propagation. Finally, we propose a defense based on the observed measures, such as the blocked/blacklisted IP addresses or ports. In particular, we investigate network-level and country-level defenses, by blocking a list of ports that are not commonly used by benign applications, and study the underlying issues and possible solutions of such a defense

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