Deep transfer learning framework for the identification of malicious activities to combat cyberattack

Singh, Deepak; Shukla, Anurag; Sajwan, Mohit

doi:10.1016/j.future.2021.07.015

Cited by 31 publications

(4 citation statements)

References 10 publications

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“…The integration of BERT and deep learning in cybersecurity represents a significant advancement in the field. Research topics include deep learning for threat detection, applying complex neural networks to identify cybersecurity threats [39,40]; BERT for natural language processing in cybersecurity, utilizing BERT's capabilities for processing and analyzing text-based data in cybersecurity contexts [41,42]; neural network architectures for malware classification, designing and testing various neural network models for effective malware identification [43]; transfer learning in cybersecurity tasks, applying knowledge gained from one task to another in the cybersecurity domain [44,45]; deep learning models for anomaly detection, using sophisticated algorithms to detect unusual patterns indicative of security threats [46,47]; BERT-based feature extraction, leveraging BERT to extract relevant features from data for cybersecurity applications [48,49,50]; and the use of BERT for understanding malware the comparison of opcode patterns with those of known malware samples is performed using similarity scoring techniques such as cosine similarity or Jaccard index, which quantify the resemblance between the opcode sequence of a given sample and known malware signatures.…”

Section: Applications Of Bert and Deep Learning In Cybersecuritymentioning

confidence: 99%

A Novel Approach of Evasive Malware Analysis Through Binary Opcode and BERT

Wang,

2024

Preprint

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This study addressed the growing challenge of evasive malware detection by introducing an innovative approach that integrates binary opcode analysis with the advanced machine learning capabilities of BERT (Bidirectional Encoder Representations from Transformers). Focusing on the rapidly evolving landscape of cybersecurity, this study explored the intricacies of malware behavior, particularly in the context of opcode obfuscation and direct OS lower-level API access. Through a comprehensive methodology, the study demonstrated the effectiveness of combining traditional disassembly techniques with state-of-the-art deep learning algorithms. The findings revealed a significant distinction between malware and benign software in terms of their opcode structures and system interactions. This study not only offered a novel perspective in malware analysis, but also set the stage for future advancements in cybersecurity tools and techniques. The integration of binary opcode analysis and BERT represented a vital development in cybersecurity, providing enhanced detection capabilities and a deeper understanding of sophisticated malware threats.

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Section: Applications Of Bert and Deep Learning In Cybersecuritymentioning

confidence: 99%

A Novel Approach of Evasive Malware Analysis Through Binary Opcode and BERT

Wang,

2024

Preprint

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“…In addition, residual units increase sensitivity to small changes in local features, further adjusting network weights, and improving training performance. Due to its few parameters and superior performance, ResNet has been widely used in DL in recent years [23,24] (see Figure 7). ResNet networks have different layers of structure, including 18, 34, 50, and 101, with ResNet50 and ResNet101 being the most commonly used types.…”

Section: Fig 6 Flow Chart Of Automatic Inspection Of Construction Qua...mentioning

confidence: 99%

Automatic Quality Inspection and Intelligent Prevention of Prefabricated Building Construction Based on BIM and Fuzzy Logic

2024

Decis. Mak. Appl. Manag. Eng.

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The construction industry plays an important role in China's economy. However, traditional construction quality inspection methods have problems such as low efficiency, high leakage rate, and poor real-time performance. Therefore, taking the appearance quality defects of reinforced concrete engineering as an example, this study combines building information models and computer vision technology. By this way, it can achieve automatic quality inspection and intelligent quality problem prevention, to improve the quality management level during the construction process. In this study, ResNet50 network was pre-trained and classified using a self-made defect image database. After transfer learning, the accuracy values of the training and testing sets remained stable at around 0.95 and the loss value remained stable at around 0.10 after 10 epochs, indicating a significant improvement in learning performance. In the defect area quantification experiment, four corner coordinates of the defect image were calculated. These corner coordinates are simultaneously obtained and saved during on-site image acquisition. According to the calculation results, the defect area of honeycomb is approximately 67.67 square centimetres. These results confirm that this method improves the efficiency and accuracy of quality inspection and has potential application in quality inspection of prefabricated building construction.

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“…Using various graphs, we visualize different patterns and relationships between various features and attributes 51 . This aids in our understanding of the dataset structure.…”

Section: Proposed Modelmentioning

confidence: 99%

Performance evaluation of machine learning models for distributed denial of service attack detection using improved feature selection and hyper‐parameter optimization techniques

Habib

Khursheed

2022

Concurrency and Computation

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This article gives the framework of extensive experimentation of various machine learning models to detect distributed denial of service attacks (DDoS). We use six-tier feature ranking methods that use statistical techniques as well as machine learning based classifiers to obtain the significant features. The measurable statistical based feature selection involves Chi-Square (Chi2), information gain (IG), merged Chi-Square (Chi2)-IG ranking and machine learning classifiers involve ensemble classifiers, that is, decision tree, random forest and eXtreme gradient boosting (XGBoost). Different supervised machine learning models (logistic regression, decision tree classifier, linear support vector machine, k-nearest neighbors, Gaussian Naive Bayes, random forest classifier, XGBoost) are trained on a feature-engineered datasets. To further our research, we use neural networks (ANN and CNN) using both feature-selected and auto-feature selection training setup. We check the validation and adaptability of these models with the optimal tuning of various parameters using GridSearchCV and the effectiveness of random sampling in overcoming the class imbalance problem. Based on various feature selection methods, the models are evaluated for their best performance. The experimental results show that our trained machine learning models and neural networks outperformed the ones in the state of art. The performance analysis is done based on confusion matrix scores, that is, accuracy, false alarm rate, sensitivity, specificity, false-positive rate, F1 score, area under curve analysis and loss functions on well-known KDD Cup 99 and UNSW-NB15 datasets. This study is significant for furthering the research in DDoS detection with machine learning and deep neural networks.

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Deep transfer learning framework for the identification of malicious activities to combat cyberattack

Cited by 31 publications

References 10 publications

A Novel Approach of Evasive Malware Analysis Through Binary Opcode and BERT

A Novel Approach of Evasive Malware Analysis Through Binary Opcode and BERT

Automatic Quality Inspection and Intelligent Prevention of Prefabricated Building Construction Based on BIM and Fuzzy Logic

Performance evaluation of machine learning models for distributed denial of service attack detection using improved feature selection and hyper‐parameter optimization techniques

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