JSObfusDetector: A binary PSO-based one-class classifier ensemble to detect obfuscated JavaScript code

Jodavi, Mehran; Abadi, Mahdi; Parhizkar, Elham

doi:10.1109/aisp.2015.7123508

Cited by 21 publications

(5 citation statements)

References 14 publications

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“…The PIN will be obfuscated by randomizing the placement of the real PIN within the 10-digit number. Randomization obfuscation is a technique that randomly inserts or changes some elements of code without changing the semantics (Jodavi et al, 2015). Though this method is commonly applied by researchers in data mining and databases, it can be applied equally in the production of a usable dataset that can be directly displayed to end users, and this is supported by data obfuscation (Baken et al, 2004).…”

Section: "Randomization Obfuscation" Techniquementioning

confidence: 99%

Adapting User Interface Design to Mitigate Shoulder Surfing Attacks in USSD Channel

A. P.,

J. O.

2024

African Journal of Environment and Natural Science Research

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The most widely accepted authentication method involves the use of a personal identification number (PIN). This method is applicable across many technologies, of which one of them is Unstructured Supplementary Service Data (USSD). USSD is a capability built into the Global System for Mobile Communication (GSM). In some developing countries like Nigeria, USSD is used in carrying out financial transactions. It has been observed that while carrying out banking transactions using this technology, users' personal identification number (PIN) entered for authentication appears in plain text on the mobile interface, thereby subjecting it to shoulder surfing attacks. Findings revealed that users' PIN appears in plain text because USSD technology is designed to convey only textual data. That is why many existing authentication methods against Human shoulder surfing attacks which contain features like images, colors, or graphical password, that can provide security to users' PIN on mobile interface are not implemented on the USSD channel. This is one of the reasons why many existing authentication methods, which are designed with features such as images, colors or graphical passwords to prevent shoulder surfing attack, are not implemented on the USSD channel. This research is, therefore, on the design of a new authentication method that can provide security to users’ PIN at the mobile interface of the USSD channel and secure the users’ transaction against shoulder surfing attacks. In this method, the challenge response approach is adopted to provide a secure PIN entry method in the presence of a human shoulder surfer, using the randomization obfuscation method that randomly places the user's chosen PIN within randomly generated 10-digit numbers, in Left to Right order. For further security, the designed model includes features like Bag of Soft Biometrics (BoSB) details and one-time password (OTP).

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Section: "Randomization Obfuscation" Techniquementioning

confidence: 99%

Adapting User Interface Design to Mitigate Shoulder Surfing Attacks in USSD Channel

A. P.,

J. O.

2024

African Journal of Environment and Natural Science Research

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“…Jodavi et al [19] worked on a novel classifier ensemble approach called JSObfusDetector to identify vulnerable JS code. An ensemble of a one-class SVM classifier was used with a binary particle swarm optimization algorithm.…”

Section: A Description Of Primary Studies (Ps)mentioning

confidence: 99%

A Systematic Literature Review and Quality Analysis of Javascript Malware Detection

Sohan

Basalamah

2020

IEEE Access

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Context: JavaScript (JS) is an often-used programming language by millions of web pages and is also affected by thousands of malicious attacks. Objective: In this investigation, we provided a general view and a quick understanding of JavaScript Malware Detection (JSMD) research reported in the scientific literature from several perspectives. Method: We performed a Systematic Literature Review (SLR) and quality analysis of published research articles on the topic. We investigated 32 articles published between the year 2009 to the year 2019. Results: Selected 32 papers explained in this article reflect the outline of what was published so far. One of our key findings is the performance of Machine Learning (ML) based detection models were relatively higher than others. We also found that only a few papers were able to achieve high scores according to the quality assessment criteria. Conclusion: In this SLR, we summarized and synthesized the existing JSMD studies to identify the previous research practices and also to shed light on future guidelines in the malware detection space. This study will guide and help future researchers to investigate the previous literature efficiently and effectively.

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“…We use binary coding method to encode the resource allocation problem. A chromosome represents a resource allocation plan.…”

Section: On‐line Decision‐making Based On Genetic Algorithmmentioning

confidence: 99%

Self‐learning and self‐adaptive resource allocation for cloud‐based software services

Chen

Lin

et al. 2018

Concurrency and Computation

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Summary In the presence of scale, dynamism, uncertainty, and elasticity, cloud engineers face several challenges when allocating resources for cloud‐based software services. They should allocate appropriate resources in order to guarantee good quality of services as well as low cost of resources. Self‐adaptive ability is needed in this process because engineers' intervention is difficult. Traditional self‐adaptive resource allocation methods are policy‐driven. Thus, cloud engineers usually have to develop separate sets of rules for each systems in order to allocate resources effectively, which leads to high administrative cost and implementation complexity. Machine learning has made great achievements in many fields, and it can be also applied to resource allocation. In this paper, we present a self‐learning and self‐adaptive approach to resource allocation for cloud‐based software services. For a given cloud‐based software service, its QoS model is firstly trained on history data, which is capable to predict the QoS value as output by using the information on workload and allocated resources as inputs. Then, on‐line decision‐making on resource allocation can be carried out automatically based on genetic algorithm, which is aimed to search reasonable resource allocation plan by using the QoS model. We evaluate our approach on RUBiS benchmark, demonstrating the accuracy of the QoS model over 90% and the improvement of resource utilization by 10%‐30%.

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JSObfusDetector: A binary PSO-based one-class classifier ensemble to detect obfuscated JavaScript code

Cited by 21 publications

References 14 publications

Adapting User Interface Design to Mitigate Shoulder Surfing Attacks in USSD Channel

Adapting User Interface Design to Mitigate Shoulder Surfing Attacks in USSD Channel

A Systematic Literature Review and Quality Analysis of Javascript Malware Detection

Self‐learning and self‐adaptive resource allocation for cloud‐based software services

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