Malware traffic classification using principal component analysis and artificial neural network for extreme surveillance

Arivudainambi, D.; Kumar, K. A. Varun; Chakkaravarthy, S. Sibi; Visu, P.

doi:10.1016/j.comcom.2019.08.003

Cited by 65 publications

(15 citation statements)

References 6 publications

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“…Moreover, ML has got significant importance in the field of cyber security [17]. Different machine learning techniques namely, Bayesian Networks [18], Decision Trees [19,20], Artificial Neural Networks [21,22], Association Rule [23,24], Support Vector Machine [25,26], Regression Trees [27], K-nearest neighbors [28,29], Random Forest [30,31] have been implemented to detect and predict cyber intrusions. But there are very few research studies that try to explore machine learning approaches to detect suspicious access or intrusions to EHR systems.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Integrating Machine Learning in Healthcare for Ensuring Confidentiality of the Electronic Records

Seh¹,

Al-Amri²,

Subahi³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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The adoption of sustainable electronic healthcare infrastructure has revolutionized healthcare services and ensured that E-health technology caters efficiently and promptly to the needs of the stakeholders associated with healthcare. Despite the phenomenal advancement in the present healthcare services, the major obstacle that mars the success of E-health is the issue of ensuring the confidentiality and privacy of the patients' data. A thorough scan of several research studies reveals that healthcare data continues to be the most sought after entity by cyber invaders. Various approaches and methods have been practiced by researchers to secure healthcare digital services. However, there are very few from the Machine learning (ML) domain even though the technique has the proactive ability to detect suspicious accesses against Electronic Health Records (EHRs). The main aim of this work is to conduct a systematic analysis of the existing research studies that address healthcare data confidentiality issues through ML approaches. B.A. Kitchenham guidelines have been practiced as a manual to conduct this work. Seven well-known digital libraries namely IEEE Xplore, Science Direct, Springer Link, ACM Digital Library, Willey Online Library, PubMed (Medical and Bio-Science), and MDPI have been included to perform an exhaustive search for the existing pertinent studies. Results of this study depict that machine learning provides a more robust security mechanism for sustainable management of the EHR systems in a proactive fashion, yet the specified area has not been fully explored by the researchers. K-nearest neighbor algorithm and KNIEM implementation tools are mostly used to conduct experiments on EHR systems' log data. Accuracy and performance measure of practiced techniques are not sufficiently outlined in the primary studies. This research endeavour depicts that there is a need to analyze the dynamic digital healthcare environment more comprehensively. Greater accuracy and effective implementation of ML-based models are the need of the day for ensuring the confidentiality of EHRs in a proactive fashion.

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Section: Introductionmentioning

confidence: 99%

An Analysis of Integrating Machine Learning in Healthcare for Ensuring Confidentiality of the Electronic Records

Seh¹,

Al-Amri²,

Subahi³

et al. 2022

Computer Modeling in Engineering &Amp; Sciences

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“…FSA prevents the collision by ensuring that none of the neighboring node gets the same slot. Nodes (Arivudainambi et al , 2019b) which are separated by two or more hopes can get assigned in the same slot, thereby preventing the collision. To achieve fairness at the scheduling level, the FSA maintains four different states for each node as IDLE, REQUEST, GRANT and RELEASE.…”

Section: Proposed Methodologymentioning

confidence: 99%

Self-co-ordination algorithm (SCA) for multi-UAV systems using fair scheduling queue

K.A.

Priyadarshini

P.C.

et al. 2022

Self Cite

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Purpose Data traffic through wireless communication is significantly increasing, resulting in the frequency of streaming applications as various formats and the evolution of the Internet of Things (IoT), such as virtual reality, edge device based transportation and surveillance systems. Growth in kind of applications resulted in increasing the scope of wireless communication and allocating a spectrum, as well as methods to decrease the intervention between nearby-located wireless links functioning on the same spectrum bands and hence to proliferation for the spectral efficiency. Recent advancement in drone technology has evolved quickly leading on board sensors with increased energy, storage, communication and processing capabilities. In future, the drone sensor networks will be more common and energy utilization will play a crucial role to maintain a fully functional network for the longest period of time. Envisioning the aerial drone network, this study proposes a robust high level design of algorithms for the drones (group coordination). The proposed design is validated with two algorithms using multiple drones consisting of various on-board sensors. In addition, this paper also discusses the challenges involved in designing solutions. The result obtained through proposed method outperforms the traditional techniques with the transfer rate of more than 3 MB for data transfer in the drone with coordination Design/methodology/approach Fair Scheduling Algorithm (FSA) using a queue is a distributed slot assignment algorithm. The FSA executes in rounds. The duration of each round is dynamic based upon the delay in the network. FSA prevents the collision by ensuring that none of the neighboring node gets the same slot. Nodes (Arivudainambi et al., 2019) which are separated by two or more hopes can get assigned in the same slot, thereby preventing the collision. To achieve fairness at the scheduling level, the FSA maintains four different states for each node as IDLE, REQUEST, GRANT and RELEASE. Findings A multi-unmanned aerial vehicle (UAV) system can operate in both centralized and decentralized manner. In a centralized system, the ground control system will take care of drone data collection, decisions on navigation, task updation, etc. In a decentralized system, the UAVs are unambiguously collaborating on various levels as mentioned in the centralized system to achieve the goal which is represented in Figure 2. Research limitations/implications However, the multi-UAVs are context aware in situations such as environmental observation, UAV–UAV communication and decision-making. Independent of whether operation is centralized or decentralized, this study relates the goals of the multi-UAVs are sensing, communication and coordination among other UAVs, etc. Figure 3 shows overall system architecture. Practical implications The individual events attempts in the UAV’s execution are required to complete the mission in superlative manner which affects in every multi UAV system. This multi UAV systems need to take a steady resolute on what way UAV has to travel and what they need to complete to face the critical situations in changing of environments with the uncertain information. This coordination algorithm has certain dimensions including events that they needs to resolute on, the information that they used to make a resolution, the resolute making algorithm, the degree of decentralization. In multi UAV systems, the coordinated events ranges from lower motion level. Originality/value This study has proposed a novel self-organizing coordination algorithm for multi-UAV systems. Further, the experimental results also confirm that is robust to form network at ease. The testbed for this simulation to sensing, communication, evaluation and networking. The algorithm coordination has to testbed with multi UAVs systems. The two scheduling techniques has been used to transfer the packets using done network. The self-organizing algorithm (SOA) with fair scheduling queue outperforms the weighted queue scheduling in the transfer rate with less loss and time lag. The results obtained through from Figure 10 clearly indicates that the fair queue scheduling with SOA have several advantages over weighted fair queue in different parameters.

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“…The transformed principal components might not always align with the features that are most relevant for specific detection tasks. In [17], certain cases, using PCA alone for feature selection might discard discriminative information, leading to reduced detection accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing Feature Selection in Intrusion Detection Using Fisher Score Algorithm: An Analytical Study

Rathod,

Sabnis,

Jain

2024

Preprint

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Traditional intrusion detection methods often fail to meet the unique security requirements of IoT applications, raising serious security concerns due to the rapid expansion of the Internet of Things (IoT) -connectivity and its extensive property, complex structure, and complexity. Machine learning algorithms for intrusion detection have emerged as a solution, offering promise in protecting these complex systems. This paper examines a comprehensive analysis of an Intrusion Detection System (IDS) in an IoT system. Recognizing the important role of feature extraction in IDS, this study aims to help researchers by providing insights on data set selection and confirming the effectiveness of the Fisher Score algorithm. Through a careful comparative analysis using established selection methods—Mutual Information, Chi-Square, Principal Component Analysis (PCA), and Recursive Feature Elimination (RFE)— this study seeks to help researchers choose the most appropriate feature extraction technique in intrusion detection within the IoT framework. Using logistic regression as a classification model, this study allows a thorough analysis and comparison of different selection methods. The results highlight the importance and accuracy of the Fisher Score algorithm in selecting key features for Intrusion Detection in IoT systems. It is worth mentioning, that this research is limited to a specific dataset, NBaIot, which is considered the best and most up-to-date for Intrusion Detection. The specificity observed in this study is dependent on the number of items and data sets. Although the findings may be variable, the selected sets and item sizes have informed the identification of interventions in this study that are deemed optimal.

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Malware traffic classification using principal component analysis and artificial neural network for extreme surveillance

Cited by 65 publications

References 6 publications

An Analysis of Integrating Machine Learning in Healthcare for Ensuring Confidentiality of the Electronic Records

An Analysis of Integrating Machine Learning in Healthcare for Ensuring Confidentiality of the Electronic Records

Self-co-ordination algorithm (SCA) for multi-UAV systems using fair scheduling queue

Optimizing Feature Selection in Intrusion Detection Using Fisher Score Algorithm: An Analytical Study

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