Exploring How to Apply Secure Software Design Principles

Ebad, Shouki A.

doi:10.1109/access.2022.3227434

Cited by 7 publications

(3 citation statements)

References 16 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Secure design principles: Since this study focuses on the feasibility of the executable adversarial attack and retraining a robust deep learning model against these attacks, the implementation of these models should comply with the application-level secure design principles [38]. Due to the limited effect, we leave this part for our future work.…”

Section: Limitations and Secure Design Principlesmentioning

confidence: 99%

A Robust CNN for Malware Classification against Executable Adversarial Attack

Zhang,

Jiang,

et al. 2024

Electronics

View full text Add to dashboard Cite

Deep-learning-based malware-detection models are threatened by adversarial attacks. This paper designs a robust and secure convolutional neural network (CNN) for malware classification. First, three CNNs with different pooling layers, including global average pooling (GAP), global max pooling (GMP), and spatial pyramid pooling (SPP), are proposed. Second, we designed an executable adversarial attack to construct adversarial malware by changing the meaningless and unimportant segments within the Portable Executable (PE) header file. Finally, to consolidate the GMP-based CNN, a header-aware loss algorithm based on the attention mechanism is proposed to defend the executive adversarial attack. The experiments showed that the GMP-based CNN achieved better performance in malware detection than other CNNs with around 98.61% accuracy. However, all CNNs were vulnerable to the executable adversarial attack and a fast gradient-based attack with a 46.34% and 34.65% accuracy decline on average, respectively. Meanwhile, the improved header-aware CNN achieved the best performance with an evasion ratio of less than 5.0%.

show abstract

Section: Limitations and Secure Design Principlesmentioning

confidence: 99%

A Robust CNN for Malware Classification against Executable Adversarial Attack

Zhang,

Jiang,

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…This two-step strategy enables us to proficiently accomplish the malware classification task, optimizing it effectively, with each step addressing distinct optimization objectives. To meet the security design requirements, the method applies some principles [39] to work, including economy mechanisms, open design, and input validation. We keep the overall architecture consistent with the Transformer, with the only introduction of the local attention module to avoid the complexity caused by excessive modifications.…”

Section: Overview and Design Principlesmentioning

confidence: 99%

“…We make assumptions on the input, so we need to apply the validation, including checking if the API call is legal from the system library and validating the input of the API pair for the model. In addition, more privilege To meet the security design requirements, the method applies some principles [39] to work, including economy mechanisms, open design, and input validation. We keep the overall architecture consistent with the Transformer, with the only introduction of the local attention module to avoid the complexity caused by excessive modifications.…”

Section: Overview and Design Principlesmentioning

confidence: 99%

TTDAT: Two-Step Training Dual Attention Transformer for Malware Classification Based on API Call Sequences

Wang,

Lin,

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

The surge in malware threats propelled by the rapid evolution of the internet and smart device technology necessitates effective automatic malware classification for robust system security. While existing research has primarily relied on some feature extraction techniques, issues such as information loss and computational overhead persist, especially in instruction-level tracking. To address these issues, this paper focuses on the nuanced analysis of API (Application Programming Interface) call sequences between the malware and system and introduces TTDAT (Two-step Training Dual Attention Transformer) for malware classification. TTDAT utilizes Transformer architecture with original multi-head attention and an integrated local attention module, streamlining the encoding of API sequences and extracting both global and local patterns. To expedite detection, we introduce a two-step training strategy: ensemble Transformer models to generate class representation vectors, thereby bolstering efficiency and adaptability. Our extensive experiments demonstrate TTDAT’s effectiveness, showcasing state-of-the-art results with an average F1 score of 0.90 and an accuracy of 0.96.

show abstract