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@article{CESCHIN2022118590,
title = {Fast & Furious: On the modelling of malware detection as an evolving data stream},
author = {Fabrício Ceschin and Marcus Botacin and Heitor Murilo Gomes and Felipe Pinagé and Luiz S Oliveira and André Grégio},
url = {https://www.sciencedirect.com/science/article/pii/S0957417422016463
https://secret.inf.ufpr.br/papers/fabricio_eswa_22.pdf},
doi = {https://doi.org/10.1016/j.eswa.2022.118590},
issn = {0957-4174},
year = {2022},
date = {2022-08-22},
journal = {Expert Systems with Applications},
pages = {118590},
abstract = {Malware is a major threat to computer systems and imposes many challenges to cyber security. Targeted threats, such as ransomware, cause millions of dollars in losses every year. The constant increase of malware infections has been motivating popular antiviruses (AVs) to develop dedicated detection strategies, which include meticulously crafted machine learning (ML) pipelines. However, malware developers unceasingly change their samples’ features to bypass detection. This constant evolution of malware samples causes changes to the data distribution (i.e., concept drifts) that directly affect ML model detection rates, something not considered in the majority of the literature work. In this work, we evaluate the impact of concept drift on malware classifiers for two Android datasets: DREBIN (≈130K apps) and a subset of AndroZoo (≈285K apps). We used these datasets to train an Adaptive Random Forest (ARF) classifier, as well as a Stochastic Gradient Descent (SGD) classifier. We also ordered all datasets samples using their VirusTotal submission timestamp and then extracted features from their textual attributes using two algorithms (Word2Vec and TF-IDF). Then, we conducted experiments comparing both feature extractors, classifiers, as well as four drift detectors (Drift Detection Method, Early Drift Detection Method, ADaptive WINdowing, and Kolmogorov–Smirnov WINdowing) to determine the best approach for real environments. Finally, we compare some possible approaches to mitigate concept drift and propose a novel data stream pipeline that updates both the classifier and the feature extractor. To do so, we conducted a longitudinal evaluation by (i) classifying malware samples collected over nine years (2009–2018), (ii) reviewing concept drift detection algorithms to attest its pervasiveness, (iii) comparing distinct ML approaches to mitigate the issue, and (iv) proposing an ML data stream pipeline that outperformed literature approaches, achieving an improvement of 22.05 percentage points of F1Score in the DREBIN dataset, and 8.77 in the AndroZoo dataset.},
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pubstate = {published},
tppubtype = {article}
}

@article{9786768,
title = {Online Binary Models are Promising for Distinguishing Temporally Consistent Computer Usage Profiles},
author = {Luiz Giovanini and Fabrício Ceschin and Mirela Silva and Aokun Chen and Ramchandra Kulkarni and Sanjay Banda and Madison Lysaght and Heng Qiao and Nikolaos Sapountzis and Ruimin Sun and Brandon Matthews and Dapeng Oliver Wu and André Grégio and Daniela Oliveira},
url = {https://secret.inf.ufpr.br/papers/UsageProfiles_IEEE_TBIOM_2021.pdf},
doi = {10.1109/TBIOM.2022.3179206},
year = {2022},
date = {2022-06-03},
journal = {IEEE Transactions on Biometrics, Behavior, and Identity Science},
pages = {1-1},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{10.1145/3494535,
title = { Terminator: A Secure Coprocessor to Accelerate Real-Time AntiViruses Using Inspection Breakpoints},
author = {Marcus Botacin and Francis B Moreira and Philippe O A Navaux and André Grégio and Marco A Z Alves},
url = {https://doi.org/10.1145/3494535
https://secret.inf.ufpr.br/papers/marcus_coproc.pdf},
doi = {10.1145/3494535},
issn = {2471-2566},
year = {2022},
date = {2022-03-01},
journal = {ACM Trans. Priv. Secur.},
volume = {25},
number = {2},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
abstract = {AntiViruses (AVs) are essential to face the myriad of malware threatening Internet users. AVs operate in two modes: on-demand checks and real-time verification. Software-based real-time AVs intercept system and function calls to execute AV’s inspection routines, resulting in significant performance penalties as the monitoring code runs among the suspicious code. Simultaneously, dark silicon problems push the industry to add more specialized accelerators inside the processor to mitigate these integration problems. In this article, we propose Terminator, an AV-specific coprocessor to assist software AVs by outsourcing their matching procedures to the hardware, thus saving CPU cycles and mitigating performance degradation. We designed Terminator   to be flexible and compatible with existing AVs by using YARA and ClamAVrules. Our experiments show that our approach can save up to 70 million CPU cycles per rule when outsourcing on-demand checks for matching typical, unmodified YARA rules against a dataset of 30 thousand in-the-wild malware samples. Our proposal eliminates the AV’s need for blocking the CPU to perform full system checks, which can now occur in parallel. We also designed a new inspection breakpoint mechanism that signals to the coprocessor the beginning of a monitored region, allowing it to scan the regions in parallel with their execution. Overall, our mechanism mitigated up to 44% of the overhead imposed to execute and monitor the SPEC benchmark applications in the most challenging scenario.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{BOTACIN2022117083,
title = {HEAVEN: A Hardware-Enhanced AntiVirus ENgine to accelerate real-time, signature-based malware detection},
author = {Marcus Botacin and Marco Zanata Alves and Daniela Oliveira and André Grégio},
url = {https://www.sciencedirect.com/science/article/pii/S0957417422004882
https://secret.inf.ufpr.br/papers/marcus_heaven.pdf},
doi = {https://doi.org/10.1016/j.eswa.2022.117083},
issn = {0957-4174},
year = {2022},
date = {2022-01-01},
journal = {Expert Systems with Applications},
pages = {117083},
abstract = {Antiviruses (AVs) are computing-intensive applications that rely on constant monitoring of OS events and on applying pattern matching procedures on binaries to detect malware. In this paper, we introduce HEAVEN, a framework for Intel x86/x86-64 and MS Windows that combines hardware and software to improve AVs performance. HEAVEN workflow consists of a hardware-assisted signature matching process as its first step (triage), which is fast, and only invokes the software-based AV when the software is suspicious, i.e., with an unknown hardware signature for malignity. We implement a PoC for HEAVEN by instrumenting Intel’s x86/x86-64 branch predictor, which allows for the generation of malware signatures based on branch pattern history. To validate our PoC, we evaluate HEAVEN with a dataset composed of 10,000 malware and 1,000 benign software samples from different categories and accomplished malware detection rates of 100% (no false-positives). The detection occurred before the execution of 10% of the samples’ code. HEAVEN is designed to be memory efficient: it identified unique 32-bit signatures for each sample at the storage cost of only 35KB of SRAM. HEAVEN is also designed with processing efficiency in mind: its hardware extensions present negligible performance overhead and reduces the average workload of the chosen software AV counterpart (ClamWin)—10% for CPU usage, 5.61% for memory throughput, 16.22% for disk writes, and 20.22% for disk reads. With HEAVEN, we may decrease the number of CPU cycles used for malware scanning by 87.5%, which is a promising result regarding the feasibility of our proposal: the combination of hardware-/software-based AVs for practical and effective malware detection that flags suspicious software while posing negligible performance overhead.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{10.1145/3429741,
title = {One Size Does Not Fit All: A Longitudinal Analysis of Brazilian Financial Malware},
author = {Marcus Botacin and Hojjat Aghakhani and Stefano Ortolani and Christopher Kruegel and Giovanni Vigna and Daniela Oliveira and Paulo Lício De Geus and André Grégio},
url = {https://doi.org/10.1145/3429741
https://secret.inf.ufpr.br/papers/marcus_tops_br.pdf},
doi = {10.1145/3429741},
issn = {2471-2566},
year = {2021},
date = {2021-01-01},
journal = {ACM Trans. Priv. Secur.},
volume = {24},
number = {2},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
abstract = {Malware analysis is an essential task to understand infection campaigns, the behavior of malicious codes, and possible ways to mitigate threats. Malware analysis also allows better assessment of attackers’ capabilities, techniques, and processes. Although a substantial amount of previous work provided a comprehensive analysis of the international malware ecosystem, research on regionalized, country-, and population-specific malware campaigns have been scarce. Moving towards addressing this gap, we conducted a longitudinal (2012-2020) and comprehensive (encompassing an entire population of online banking users) study of MS Windows desktop malware that actually infected Brazilian banks’ users. We found that the Brazilian financial desktop malware has been evolving quickly: it started to make use of a variety of file formats instead of typical PE binaries, relied on native system resources, and abused obfuscation techniques to bypass detection mechanisms. Our study on the threats targeting a significant population on the ecosystem of the largest and most populous country in Latin America can provide invaluable insights that may be applied to other countries’ user populations, especially those in the developing world that might face cultural peculiarities similar to Brazil’s. With this evaluation, we expect to motivate the security community/industry to seriously consider a deeper level of customization during the development of next-generation anti-malware solutions, as well as to raise awareness towards regionalized and targeted Internet threats.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{BOTACIN2021102287,
title = {Challenges and Pitfalls in Malware Research},
author = {Marcus Botacin and Fabricio Ceschin and Ruimin Sun and Daniela Oliveira and André Grégio},
url = {https://www.sciencedirect.com/science/article/pii/S0167404821001115
https://secret.inf.ufpr.br/papers/marcus_challenges.pdf},
doi = {https://doi.org/10.1016/j.cose.2021.102287},
issn = {0167-4048},
year = {2021},
date = {2021-01-01},
journal = {Computers & Security},
pages = {102287},
abstract = {As the malware research field became more established over the last two decades, new research questions arose, such as how to make malware research reproducible, how to bring scientific rigor to attack papers, or what is an appropriate malware dataset for relevant experimental results. The challenges these questions pose also brings pitfalls that affect the multiple malware research stakeholders. To help answering those questions and to highlight potential research pitfalls to be avoided, in this paper, we present a systematic literature review of 491 papers on malware research published in major security conferences between 2000 and 2018. We identified the most common pitfalls present in past literature and propose a method for assessing current (and future) malware research. Our goal is towards integrating science and engineering best practices to develop further, improved research by learning from issues present in the published body of work. As far as we know, this is the largest literature review of its kind and the first to summarize research pitfalls in a research methodology that avoids them. In total, we discovered 20 pitfalls that limit current research impact and reproducibility. The identified pitfalls range from (i) the lack of a proper threat model, that complicates paper’s evaluation, to (ii) the use of closed-source solutions and private datasets, that limit reproducibility. We also report yet-to-be-overcome challenges that are inherent to the malware nature, such as non-deterministic analysis results. Based on our findings, we propose a set of actions to be taken by the malware research and development community for future work: (i) Consolidation of malware research as a field constituted of diverse research approaches (e.g., engineering solutions, offensive research, landscapes/observational studies, and network traffic/system traces analysis); (ii) design of engineering solutions with clearer, direct assumptions (e.g., positioning solutions as proofs-of-concept vs. deployable); (iii) design of experiments that reflects (and emphasizes) the target scenario for the proposed solution (e.g., corporation, user, country-wide); (iv) clearer exposition and discussion of limitations of used technologies and exercised norms/standards for research (e.g., the use of closed-source antiviruses as ground-truth).},
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pubstate = {published},
tppubtype = {article}
}