Intelligent Behavior-Based Malware Detection System on Cloud Computing Environment

Aslan, Ömer; Ozkan-Okay, Merve; Gupta, Deepti

doi:10.1109/access.2021.3087316

Cited by 62 publications

(19 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…disease should not get undetected. In order to protect the devices, security models are discussed in several research [19]- [23].…”

Section: A Anomaly Detection Models For Rpmmentioning

confidence: 99%

Detecting Anomalous User Behavior in Remote Patient Monitoring

Gupta¹,

Gupta²,

Bhatt³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

The growth in Remote Patient Monitoring (RPM) services using wearable and non-wearable Internet of Medical Things (IoMT) promises to improve the quality of diagnosis and facilitate timely treatment for a gamut of medical conditions. At the same time, the proliferation of IoMT devices increases the potential for malicious activities that can lead to catastrophic results including theft of personal information, data breach, and compromised medical devices, putting human lives at risk. IoMT devices generate tremendous amount of data that reflect user behavior patterns including both personal and day-to-day social activities along with daily routine health monitoring. In this context, there are possibilities of anomalies generated due to various reasons including unexpected user behavior, faulty sensor, or abnormal values from malicious/compromised devices. To address this problem, there is an imminent need to develop a framework for securing the smart health care infrastructure to identify and mitigate anomalies. In this paper, we present an anomaly detection model for RPM utilizing IoMT and smart home devices. We propose Hidden Markov Model (HMM) based anomaly detection that analyzes normal user behavior in the context of RPM comprising both smart home and smart health devices, and identifies anomalous user behavior. We design a testbed with multiple IoMT devices and home sensors to collect data and use the HMM model to train using network and user behavioral data. Proposed HMM based anomaly detection model achieved over 98% accuracy in identifying the anomalies in the context of RPM.

show abstract

“…disease should not get undetected. In order to protect the devices, security models are discussed in several research [19]- [23].…”

Section: A Anomaly Detection Models For Rpmmentioning

confidence: 99%

Detecting Anomalous User Behavior in Remote Patient Monitoring

Gupta¹,

Gupta²,

Bhatt³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is a kind of specific energy efficiency analysis, which is a more reasonable evaluation index for specific energy efficiency analysis. A reasonable definition of the conversion efficiency based on CC is conducive to comparing the technical development level of various technologies, which is more reasonable than the traditional thermal efficiency analysis method, and can accurately analyze the weak links of certain types of equipment [17][18].…”

Section: Ece Analysis Theory Based On CC Environmentmentioning

confidence: 99%

Energy Conversion Efficiency in Cloud Computing Environment

2021

View full text Add to dashboard Cite

With the requirements of large-scale computing and green concepts, cloud computing(CC) has become a hot topic. As one of the key problems in CC, energy conversion efficiency(ECE) has also received more and more optimized solutions from all angles under the attention of many scholars. This paper studies the ECE in CC environment, introduces the basic theory and related knowledge of CC, as well as the whole concept and framework of CC, discusses the ECE analysis theory based on CC environment, and analyzes the algorithm of energy conversion under CC technology. On this basis, the conversion efficiency is analyzed and studied, and good results are achieved.

show abstract

“…Owing to auto-scaling, assigning more resources to this server is commenced by the cloud manager [6]. If the attack is unidentified and unmitigated, the resource allocation process continues making a huge loss [7]. Moreover, the running of numerous Virtual Machines (VMs) occurs on a single physical server.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Cyber Security Framework Based on SC-AJSO Feature Selection and HT-RLSTM Attack Detection

2022

View full text Add to dashboard Cite

Cyber security is identified as an emerging concern for information technology management in business and society, owing to swift advances in telecommunication and wireless technologies. Cyberspace security has had a tremendous impact on numerous crucial infrastructures. Along with current security status data, historical data should be acquired by the system to implement the latest cyber security defense and protection. It also makes intelligent decisions that can provide adaptive security management and control. An intelligent cyber security framework using Hyperparameter Tuning based on Regularized Long Short-Term Memory (HT-RLSTM) technique was developed in this work to elevate the security level of core system assets. To detect various attacks, the proposed framework was trained and tested on the collection of data. Owing to missing values, poor scaling, imbalanced and overlapped data, the data was primarily incomplete and inconsistent. To elevate the decision making for detecting attacks, the inconsistent or unstructured data issue was addressed. The missing values were handled by this work along with scaling performance using the developed Kernelized Robust Scaler (KRS). Using the developed Random Over Sample-Based Density-Based Spatial Clustering Associated with Noise (ROS-DBSCAN), the imbalanced and overlapped data were handled, which was followed by the relevant feature selection of data utilizing the Sine Cosine-Based Artificial Jellyfish Search Optimization (SC-AJSO) technique. The data were split under the provision of Stratified K-Fold cross-validation along being trained in the proposed HT-RLSTM. The experimental analysis depicted that better accuracy was attained in detecting attacks by the proposed work for different datasets. When analogized with prevailing state-of-the-art methods, a low false detection rate, as well as computation time, was attained by the proposed scheme.

show abstract

Intelligent Behavior-Based Malware Detection System on Cloud Computing Environment

Cited by 62 publications

References 33 publications

Detecting Anomalous User Behavior in Remote Patient Monitoring

Detecting Anomalous User Behavior in Remote Patient Monitoring

Energy Conversion Efficiency in Cloud Computing Environment

Intelligent Cyber Security Framework Based on SC-AJSO Feature Selection and HT-RLSTM Attack Detection

Contact Info

Product

Resources

About