Studying Ransomware Attacks Using Web Search Logs

Bansal, Chetan; Deligiannis, Pantazis; Maddila, Chandra; Rao, Nikitha

doi:10.1145/3397271.3401189

Cited by 15 publications

(6 citation statements)

References 7 publications

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“…To analyze search behavior, search logs of GPSEs are widely adopted by the different research communities [12,30,57]. Research work [53] on the healthcare domain utilizes search log for detecting devastating diseases, while recent work [15] uses search log to measure employment demand.…”

Section: Related Workmentioning

confidence: 99%

An Empirical Study of Software Exceptions in the Field using Search Logs

Hassan¹,

Bansal²,

Nagappan³

et al. 2020

Preprint

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Software engineers spend a substantial amount of time using Web search to accomplish software engineering tasks. Such search tasks include finding code snippets, API documentation, seeking help with debugging, etc. While debugging a bug or crash, one of the common practices of software engineers is to search for information about the associated error or exception traces on the internet.In this paper, we analyze query logs from a leading commercial general-purpose search engine (GPSE) such as Google, Yahoo! or Bing to carry out a large scale study of software exceptions. To the best of our knowledge, this is the first large scale study to analyze how Web search is used to find information about exceptions. We analyzed about 1 million exception related search queries from a random sample of 5 billion web search queries. To extract exceptions from unstructured query text, we built a novel and highperformance machine learning model with a F1-score of 0.82. Using the machine learning model, we extracted exceptions from raw queries and performed popularity, effort, success, query characteristic and web domain analysis. We also performed programming language-specific analysis to give a better view of the exception search behavior. These techniques can help improve existing methods, documentation and tools for exception analysis and prediction. Further, similar techniques can be applied for APIs, frameworks, etc.

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Section: Related Workmentioning

confidence: 99%

An Empirical Study of Software Exceptions in the Field using Search Logs

Hassan¹,

Bansal²,

Nagappan³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Intent Classification: Applications of intent classification in web search include several domains like healthcare [13], security [14] and e-commerce [15], [16]. Wang et al have leveraged intent understanding for improving effort estimation in code reviews [17], [18].…”

Section: Background and Motivationmentioning

confidence: 99%

Search4Code: Code Search Intent Classification Using Weak Supervision

Rao¹,

Bansal²,

Guan³

2020

Preprint

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Developers use search for various tasks such as finding code, documentation, debugging information, etc. In particular, web search is heavily used by developers for finding code examples and snippets during the coding process. Recently, natural language based code search has been an active area of research. However, the lack of real-world large-scale datasets is a significant bottleneck. In this work, we propose a weak supervision based approach for detecting code search intent in search queries for C# and Java programming languages. We evaluate the approach against several baselines on a real-world dataset comprised of over 1 million queries mined from Bing web search engine and show that the CNN based model can achieve an accuracy of 77% and 76% for C# and Java respectively. Furthermore, we are also releasing the first large-scale real-world dataset of code search queries mined from Bing web search engine. We hope that the dataset will aid future research on code search.

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“…Recently, the market share of Android mobile operating system (OS) has approximately reached 72.97% by Q4 2020 1 . However, this rapid evolution of the Android market has attracted many attackers to gain illegal access to Android devices and data using malware applications.…”

Section: Introductionmentioning

confidence: 99%

“…Many forms of malware applications can infect the victim's device, including but not limited to Trojans, Spyware, and Ransomware. Among the afore- 1 https://gs.statcounter.com/os-market-share/mobile/worldwide mentioned types, Ransomware has been recording a dramatic increase by recent studies [1]- [3]. Ransomware intrudes the device's OS by using a malicious code that blocks the access of the victim's data unless a ransom is paid [4].…”

Section: Introductionmentioning

confidence: 99%

Android Ransomware Detection Based on a Hybrid Evolutionary Approach in the Context of Highly Imbalanced Data

Almomani

Qaddoura

Habib³

et al. 2021

IEEE Access

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In recent years, Ransomware has been a critical threat that attacks smartphones. Ransomware is a kind of malware that blocks the mobile's system and prevents the user of the infected device from accessing their data until a ransom is paid. Worldwide, Ransomware attacks have led to serious losses for individuals and stakeholders. However, the dramatic increase of Ransomware families makes to the process of identifying them more challenging due to their continuously evolved characteristics. Traditional malware detection methods (e.g., statistical-based prevention methods) fail to combat the evolving Ransomware since they result in a high percentage of false positives. Indeed, developing a non-classical, intelligent technique to safeguarding against Ransomware is of significant importance. This paper introduces a new methodology for the detection of Ransomware that is depending on an evolutionary-based machine learning approach. The binary particle swarm optimization algorithm is utilized for tuning the hyperparameters of the classification algorithm, as well as performing feature selection. The support vector machines (SVM) algorithm is used alongside the synthetic minority oversampling technique (SMOTE) for classification. The utilized dataset is collected from various sources, which consists of 10,153 Android applications, where 500 of them are Ransomware. The performance of the proposed approach SMOTE-tBPSO-SVM achieved merits over traditional machine learning algorithms by having the highest scores in terms of sensitivity, specificity, and g-mean.

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Studying Ransomware Attacks Using Web Search Logs

Cited by 15 publications

References 7 publications

An Empirical Study of Software Exceptions in the Field using Search Logs

An Empirical Study of Software Exceptions in the Field using Search Logs

Search4Code: Code Search Intent Classification Using Weak Supervision

Android Ransomware Detection Based on a Hybrid Evolutionary Approach in the Context of Highly Imbalanced Data

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