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2021-05-05
Poudyal, Subash, Dasgupta, Dipankar.  2020.  AI-Powered Ransomware Detection Framework. 2020 IEEE Symposium Series on Computational Intelligence (SSCI). :1154—1161.

Ransomware attacks are taking advantage of the ongoing pandemics and attacking the vulnerable systems in business, health sector, education, insurance, bank, and government sectors. Various approaches have been proposed to combat ransomware, but the dynamic nature of malware writers often bypasses the security checkpoints. There are commercial tools available in the market for ransomware analysis and detection, but their performance is questionable. This paper aims at proposing an AI-based ransomware detection framework and designing a detection tool (AIRaD) using a combination of both static and dynamic malware analysis techniques. Dynamic binary instrumentation is done using PIN tool, function call trace is analyzed leveraging Cuckoo sandbox and Ghidra. Features extracted at DLL, function call, and assembly level are processed with NLP, association rule mining techniques and fed to different machine learning classifiers. Support vector machine and Adaboost with J48 algorithms achieved the highest accuracy of 99.54% with 0.005 false-positive rates for a multi-level combined term frequency approach.

2021-04-08
Ayub, M. A., Continella, A., Siraj, A..  2020.  An I/O Request Packet (IRP) Driven Effective Ransomware Detection Scheme using Artificial Neural Network. 2020 IEEE 21st International Conference on Information Reuse and Integration for Data Science (IRI). :319–324.
In recent times, there has been a global surge of ransomware attacks targeted at industries of various types and sizes from retail to critical infrastructure. Ransomware researchers are constantly coming across new kinds of ransomware samples every day and discovering novel ransomware families out in the wild. To mitigate this ever-growing menace, academia and industry-based security researchers have been utilizing unique ways to defend against this type of cyber-attacks. I/O Request Packet (IRP), a low-level file system I/O log, is a newly found research paradigm for defense against ransomware that is being explored frequently. As such in this study, to learn granular level, actionable insights of ransomware behavior, we analyze the IRP logs of 272 ransomware samples belonging to 18 different ransomware families captured during individual execution. We further our analysis by building an effective Artificial Neural Network (ANN) structure for successful ransomware detection by learning the underlying patterns of the IRP logs. We evaluate the ANN model with three different experimental settings to prove the effectiveness of our approach. The model demonstrates outstanding performance in terms of accuracy, precision score, recall score, and F1 score, i.e., in the range of 99.7%±0.2%.
2021-03-30
Ganfure, G. O., Wu, C.-F., Chang, Y.-H., Shih, W.-K..  2020.  DeepGuard: Deep Generative User-behavior Analytics for Ransomware Detection. 2020 IEEE International Conference on Intelligence and Security Informatics (ISI). :1—6.

In the last couple of years, the move to cyberspace provides a fertile environment for ransomware criminals like ever before. Notably, since the introduction of WannaCry, numerous ransomware detection solution has been proposed. However, the ransomware incidence report shows that most organizations impacted by ransomware are running state of the art ransomware detection tools. Hence, an alternative solution is an urgent requirement as the existing detection models are not sufficient to spot emerging ransomware treat. With this motivation, our work proposes "DeepGuard," a novel concept of modeling user behavior for ransomware detection. The main idea is to log the file-interaction pattern of typical user activity and pass it through deep generative autoencoder architecture to recreate the input. With sufficient training data, the model can learn how to reconstruct typical user activity (or input) with minimal reconstruction error. Hence, by applying the three-sigma limit rule on the model's output, DeepGuard can distinguish the ransomware activity from the user activity. The experiment result shows that DeepGuard effectively detects a variant class of ransomware with minimal false-positive rates. Overall, modeling the attack detection with user-behavior permits the proposed strategy to have deep visibility of various ransomware families.

2020-03-23
Bahrani, Ala, Bidgly, Amir Jalaly.  2019.  Ransomware detection using process mining and classification algorithms. 2019 16th International ISC (Iranian Society of Cryptology) Conference on Information Security and Cryptology (ISCISC). :73–77.

The fast growing of ransomware attacks has become a serious threat for companies, governments and internet users, in recent years. The increasing of computing power, memory and etc. and the advance in cryptography has caused the complicating the ransomware attacks. Therefore, effective methods are required to deal with ransomwares. Although, there are many methods proposed for ransomware detection, but these methods are inefficient in detection ransomwares, and more researches are still required in this field. In this paper, we have proposed a novel method for identify ransomware from benign software using process mining methods. The proposed method uses process mining to discover the process model from the events logs, and then extracts features from this process model and using these features and classification algorithms to classify ransomwares. This paper shows that the use of classification algorithms along with the process mining can be suitable to identify ransomware. The accuracy and performance of our proposed method is evaluated using a study of 21 ransomware families and some benign samples. The results show j48 and random forest algorithms have the best accuracy in our method and can achieve to 95% accuracy in detecting ransomwares.

2019-10-07
Agrawal, R., Stokes, J. W., Selvaraj, K., Marinescu, M..  2019.  Attention in Recurrent Neural Networks for Ransomware Detection. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :3222–3226.

Ransomware, as a specialized form of malicious software, has recently emerged as a major threat in computer security. With an ability to lock out user access to their content, recent ransomware attacks have caused severe impact at an individual and organizational level. While research in malware detection can be adapted directly for ransomware, specific structural properties of ransomware can further improve the quality of detection. In this paper, we adapt the deep learning methods used in malware detection for detecting ransomware from emulation sequences. We present specialized recurrent neural networks for capturing local event patterns in ransomware sequences using the concept of attention mechanisms. We demonstrate the performance of enhanced LSTM models on a sequence dataset derived by the emulation of ransomware executables targeting the Windows environment.

2018-03-05
Chen, Zhi-Guo, Kang, Ho-Seok, Yin, Shang-Nan, Kim, Sung-Ryul.  2017.  Automatic Ransomware Detection and Analysis Based on Dynamic API Calls Flow Graph. Proceedings of the International Conference on Research in Adaptive and Convergent Systems. :196–201.

In recent cyber incidents, Ransom software (ransomware) causes a major threat to the security of computer systems. Consequently, ransomware detection has become a hot topic in computer security. Unfortunately, current signature-based and static detection model is often easily evadable by obfuscation, polymorphism, compress, and encryption. For overcoming the lack of signature-based and static ransomware detection approach, we have proposed the dynamic ransomware detection system using data mining techniques such as Random Forest (RF), Support Vector Machine (SVM), Simple Logistic (SL) and Naive Bayes (NB) algorithms for detecting known and unknown ransomware. We monitor the actual (dynamic) behaviors of software to generate API calls flow graphs (CFG) and transfer it in a feature space. Thereafter, data normalization and feature selection were applied to select informative features which are the best for discriminating between various categories of software and benign software. Finally, the data mining algorithms were used for building the detection model for judging whether the software is benign software or ransomware. Our experimental results show that our proposed system can be more effective to improve the performance for ransomware detection. Especially, the accuracy and detection rate of our proposed system with Simple Logistic (SL) algorithm can achieve to 98.2% and 97.6%, respectively. Meanwhile, the false positive rate also can be reduced to 1.2%.

2017-11-03
Ahmadian, M. M., Shahriari, H. R..  2016.  2entFOX: A framework for high survivable ransomwares detection. 2016 13th International Iranian Society of Cryptology Conference on Information Security and Cryptology (ISCISC). :79–84.

Ransomwares have become a growing threat since 2012, and the situation continues to worsen until now. The lack of security mechanisms and security awareness are pushing the systems into mire of ransomware attacks. In this paper, a new framework called 2entFOX' is proposed in order to detect high survivable ransomwares (HSR). To our knowledge this framework can be considered as one of the first frameworks in ransomware detection because of little publicly-available research in this field. We analyzed Windows ransomwares' behaviour and we tried to find appropriate features which are particular useful in detecting this type of malwares with high detection accuracy and low false positive rate. After hard experimental analysis we extracted 20 effective features which due to two highly efficient ones we could achieve an appropriate set for HSRs detection. After proposing architecture based on Bayesian belief network, the final evaluation is done on some known ransomware samples and unknown ones based on six different scenarios. The result of this evaluations shows the high accuracy of 2entFox in detection of HSRs.

Moore, C..  2016.  Detecting Ransomware with Honeypot Techniques. 2016 Cybersecurity and Cyberforensics Conference (CCC). :77–81.

Attacks of Ransomware are increasing, this form of malware bypasses many technical solutions by leveraging social engineering methods. This means established methods of perimeter defence need to be supplemented with additional systems. Honeypots are bogus computer resources deployed by network administrators to act as decoy computers and detect any illicit access. This study investigated whether a honeypot folder could be created and monitored for changes. The investigations determined a suitable method to detect changes to this area. This research investigated methods to implement a honeypot to detect ransomware activity, and selected two options, the File Screening service of the Microsoft File Server Resource Manager feature and EventSentry to manipulate the Windows Security logs. The research developed a staged response to attacks to the system along with thresholds when there were triggered. The research ascertained that witness tripwire files offer limited value as there is no way to influence the malware to access the area containing the monitored files.