An Assurance-Based Risk Management Framework for Distributed Systems

Anisetti, Marco; Ardagna, Claudio Agostino; Bena, Nicola; Foppiani, Andrea

doi:10.1109/icws53863.2021.00068

Cited by 11 publications

(24 citation statements)

References 28 publications

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“…Together, these aspects highlight the critical importance of cutting-edge technology inclusive of deep gaining knowledge of (DL), gadget learning (ML), and user-targeted gadget getting to know structures in strengthening cybersecurity measures and responding to the ever-evolving cyber danger environment. Cagatay [24] noticed that the machine learning and deep studying have gained significance in malware detection, in particular in static evaluation that concentrates on facts extracted from every malware and valid code, inclusive of Windows API calls and Assembly commands. Static assessment techniques, leveraging more than a few classifier algorithms, frequently achieve immoderate tiers of accuracy, precision, and keep in mind exceeding 0.Nine, albeit they may be intrusive.…”

Section: Anomaly Based Detectionmentioning

confidence: 99%

Comprehensive Analysis to Detect Mobile Malware Using Multiple Techniques

2024

IJFMR

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Mobile devices have end up integral elements of our each day lives, storing great quantities of private and sensitive data. However, this convenience comes with inherent dangers, as cybercriminals increasingly more goal cellular devices for malicious sports consisting of stealing personal statistics, disrupting operations, and compromising the working machine. Various sorts of cellular malware, together with Remote Access Tools (RATs), Bank Trojans, Ransomware, Cryptomining Malware, and Advertising Click Fraud, pose sizable threats to users' privateness and protection. Detecting and mitigating cell malware is essential in safeguarding customers' gadgets and statistics. This paper systematically examines and surveys cellular malware detection strategies, specializing in traditional and superior strategies. Traditional detection methods encompass signature-based detection, conduct-primarily based detection, and permission analysis, while superior techniques embody gadget studying-based detection and anomaly detection. Each approach has its strengths and obstacles, emphasizing the significance of using a mixture of strategies for complete safety. The paper reviews relevant literature to research the effectiveness of different detection techniques and their packages in actual-global situations. It discusses the evolution of malware detection methodologies, highlighting advancements which include mobile botnet type, dynamic anomaly-based totally detection, and characteristic-based adverse attacks on device getting to know classifiers. Additionally, the paper explores the demanding situations confronted via cutting-edge detection techniques and proposes avenues for future research to address those obstacles. By presenting a comprehensive evaluation of cell malware detection strategies, this thesis contributes to the advancement of studies in cybersecurity and aids in the improvement of greater strong and green detection mechanisms to combat evolving threats in the cellular surroundings.

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Section: Anomaly Based Detectionmentioning

confidence: 99%

Comprehensive Analysis to Detect Mobile Malware Using Multiple Techniques

2024

IJFMR

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“…This is because it typically uses an optimization technique belonging to the hill-climbing Newton family, such as Newton-Raphson and its variants and extensions, to arrive at an estimate. Therefore, it appears that the conventional logistic regression has difficulty managing a large range of features [23]. When building a machine learning model, feature selection is used to narrow down the data points to a manageable collection.…”

Section: Fig 1: Malware Analysis In Cloudmentioning

confidence: 99%

Intellectual Feature Ranking Model with Correlated Feature Set based Malware Detection in Cloud environment using Machine Learning

Rao,

Jain,

Gangadhar

et al. 2023

IJRITCC

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Malware detection for cloud systems has been studied extensively, and many different approaches have been developed and implemented in an effort to stay ahead of this ever-evolving threat. Malware refers to any programme or defect that is designed to duplicate itself or cause damage to the system's hardware or software. These attacks are designed specifically to cause harm to operational systems, but they are invisible to the human eye. One of the most exciting developments in data storage and service delivery today is cloud computing. There are significant benefits to be gained over more conventional protection methods by making use of this fast evolving technology to protect computer-based systems from cyber-related threats. Assets to be secured may reside in any networked computing environment, including but not limited to Cyber Physical Systems (CPS), critical systems, fixed and portable computers, mobile devices, and the Internet of Things (IoT). Malicious software or malware refers to any programme that intentionally compromises a computer system in order to compromise its security, privacy, or availability. A cloud-based intelligent behavior analysis model for malware detection system using feature set is proposed to identify the ever-increasing malware attacks. The suggested system begins by collecting malware samples from several virtual machines, from which unique characteristics can be extracted easily. Then, the malicious and safe samples are separated using the features provided to the learning-based and rule-based detection agents. To generate a relevant feature set for accurate malware detection, this research proposes an Intellectual Feature Ranking Model with Correlated Feature Set (IFR-CFS) model using enhanced logistic regression model for accurate detection of malware in the cloud environment. The proposed model when compared to the traditional feature selection model, performs better in generation of feature set for accurate detection of malware.

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“…Non-functional properties mimics Protection Profiles of CC [25]. They have been mostly considered in the context of service certification [2] and later enhanced in the context of cloud certification [3,34,43], cloud plan adaption [6], DevOps pipelines [4], and to complement and validate risk management [1].…”

Section: Motivation and Reference Scenario 21 Motivation And State Of...mentioning

confidence: 99%

“…The monetary costs are finally calculated assuming an average hourly labour cost of 23€ in Spain. 1 We note that, for simplicity, costs related to the adoption of services and infrastructures have been assumed to be negligible; more sophisticated cost models can be applied in our methodology.…”

Section: Walkthroughmentioning

confidence: 99%

Bridging the Gap Between Certification and Software Development

Ardagna

Bena

Pozuelo

2022

Proceedings of the 17th International Conference on Availability, Reliability and Security

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While certification is widely recognized as a means to increase system trustworthiness and reduce uncertainty in decision making, it faces severe challenges preventing a wider adoption thereof. Certification is not adequately planned and integrated within the development process, leading to suboptimal scenarios where certification introduces the need to further modify the developed system with high costs. We propose a methodology that bridges the gap between software development and certification processes. Our methodology automatically produces the certification requirements driving all steps of the development process, and maximizes the strength of certificates while taking costs under control. We formalize the above problem as a multi-objective mathematical program and solve it through a genetic algorithm. The proposed approach is tested in a real-world, cloud-based financial scenario at Caixa-Bank and its performance and quality is evaluated in a simulated scenario. CCS CONCEPTS• Security and privacy → Security requirements; Software security engineering; • Software and its engineering → Software design techniques; Software design tradeoffs; Software design engineering; Empirical software validation.

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An Assurance-Based Risk Management Framework for Distributed Systems

Cited by 11 publications

References 28 publications

Comprehensive Analysis to Detect Mobile Malware Using Multiple Techniques

Comprehensive Analysis to Detect Mobile Malware Using Multiple Techniques

Intellectual Feature Ranking Model with Correlated Feature Set based Malware Detection in Cloud environment using Machine Learning

Bridging the Gap Between Certification and Software Development

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