184 research outputs found
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
Cybersecurity: Past, Present and Future
The digital transformation has created a new digital space known as
cyberspace. This new cyberspace has improved the workings of businesses,
organizations, governments, society as a whole, and day to day life of an
individual. With these improvements come new challenges, and one of the main
challenges is security. The security of the new cyberspace is called
cybersecurity. Cyberspace has created new technologies and environments such as
cloud computing, smart devices, IoTs, and several others. To keep pace with
these advancements in cyber technologies there is a need to expand research and
develop new cybersecurity methods and tools to secure these domains and
environments. This book is an effort to introduce the reader to the field of
cybersecurity, highlight current issues and challenges, and provide future
directions to mitigate or resolve them. The main specializations of
cybersecurity covered in this book are software security, hardware security,
the evolution of malware, biometrics, cyber intelligence, and cyber forensics.
We must learn from the past, evolve our present and improve the future. Based
on this objective, the book covers the past, present, and future of these main
specializations of cybersecurity. The book also examines the upcoming areas of
research in cyber intelligence, such as hybrid augmented and explainable
artificial intelligence (AI). Human and AI collaboration can significantly
increase the performance of a cybersecurity system. Interpreting and explaining
machine learning models, i.e., explainable AI is an emerging field of study and
has a lot of potentials to improve the role of AI in cybersecurity.Comment: Author's copy of the book published under ISBN: 978-620-4-74421-
A Survey and Evaluation of Android-Based Malware Evasion Techniques and Detection Frameworks
Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion techniques deployed to circumvent malware detection. The study characterizes such evasion techniques into two broad categories, polymorphism and metamorphism, and analyses techniques used for stealth malware detection based on the malware’s unique characteristics. Furthermore, the article also presents a qualitative and systematic comparison of evasion detection frameworks and their detection methodologies for Android-based malware. Finally, the survey discusses open-ended questions and potential future directions for continued research in mobile malware detection
Machine learning approaches for malware classification based on hybrid artefacts
Malware could be developed and transformed into various forms to deceive users and evade antivirus and security endpoint detection. Furthermore, if one machine in the network is compromised, it could be used for lateral movement--when malware spreads stealthily without sending an alarm to monitoring systems. Malware attacks pose security threats to modern enterprises and can cause massive financial, reputation, and data loss to major enterprises. Therefore, it is important to detect these attacks effectively to reduce the loss to the minimum level. The current research uses different approaches, including static and dynamic analysis, to detect and analyze malware categories using distinct feature sets, such as imported modules, opcodes, and API calls, which can improve performance in binary and multi-class classification problems.
This thesis proposes a method for identifying and analyzing malware samples via static and dynamic approaches, including memory analysis and consecutive application operation sequences performed on the Windows 10 virtual environment. Standard classifiers and frequently used sequence models are utilized to expose the malware characteristics and benefit predictive capabilities. The features used in these algorithms are extracted from the static and dynamic analysis of malware samples, such as the rich header feature, debug information, temporary files, prefetch files, and event logs. The measurement of the classifiers and the degree of correctness are calculated using the accuracy, f1-score, Mean Absolute Error (MAE), confusion matrix, and Area under the ROC Curve (AUC). Combining two feature sets can provide the best classification performance on static file properties and dynamic analysis results, regardless of whether applying feature selection or not, achieving the accuracy and f1_score at 97% for integrating two datasets. For consecutive sequences, concatenating the Gated Recurrent Unit (GRU) and Transformers model can yield the highest accuracy at 97% for Noriben operations, while GRU can achieve the maximum accuracy for Opcode sequences at 89%
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