Yazar "Shepelev, Vladimir" seçeneğine göre listele

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    Cost and Efficiency Analysis of Steganography in the IEEE 802.11ah IoT Protocol
    (Tech Science Press, 2022) Almohammedi, Akram A.; Shepelev, Vladimir; Darshi, Sam; Balfaqih, Mohammed; Ghawbar, Fayad
    The widespread use of the Internet of Things (IoT) applications has enormously increased the danger level of data leakage and theft in IoT as data transmission occurs through a public channel. As a result, the security of the IoT has become a serious challenge in the field of information security. Steganography on the network is a critical tool for preventing the leakage of private information and enabling secure and encrypted communication. The primary purpose of steganography is to conceal sensitive information in any form of media such as audio, video, text, or photos, and securely transfer it through wireless networks. In this paper, we analyse the performance characteristics of one of the steganography techniques called Hidden Communication System for Corrupted Networks (HCCNETs) for hiding sensitive data. This performance analysis includes the efficiency and the cost of the system in Wireless Local Area Networks (WLANs), specifically in the IEEE 802.11ah IoT protocol. The analysis is mainly based on a two-dimensional Markov chain model in the presence of an error channel. Additionally, the model considers packet arrival rate, back-off timer freezing, back-off stages, and short retry limit to ensure compliance with IEEE 802.11ah requirements. It stresses the importance of taking these elements into consideration while modeling the efficiency and cost of the steganographic channel system. These parameters often result in a high precise channel access estimation, a more accurate and efficient accuracy measurements system, efficient channel utilisation, avoidance of throughput saturation overestimation, and ensuring that no packet is served endlessly. Evaluated results demonstrate that HCCNETs is an effective approach at low cost.
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    Drone Assisted Network Coded Cooperation With Energy Harvesting: Strengthening the Lifespan of the Wireless Networks
    (Ieee-Inst Electrical Electronics Engineers Inc, 2022) Kumar, Pankaj; Bhattacharyya, Sagnik; Darshi, Sam; Sharma, Ashwani; Almohammedi, Akram A.; Shepelev, Vladimir; Shailendra, Samar
    Next generation wireless systems include battery operated devices which demand higher throughput and a better reliability in an energy efficient fashion. To fulfil these requirements, in this paper, we propose a novel scenario where we include a dynamic Wireless Power Splitting (WPS) factor for Energy Harvesting (EH) at nodes in a Drone Assisted Network Coded Cooperation (DA-NCC) system. The dynamic WPS factor used for EH in DA-NCC system is made more realistic by determining through the probability of Line-of-Sight (LoS) occurrence. Analytical framework is developed for residual Analog Network Coding (ANC) noise and variance of ANC-noise in EH scenario. We also derive the average rate and average outage probability expressions for the proposed channel model. Various algorithms are developed for deciding the Air-to-Ground (A2G) channel distributions, harvesting the energy at relay and source nodes and evaluating the performance metrics of our proposed work. Our investigations reveal that the use of EH in DA-NCC improves the lifespan of the network. Our findings play important roles in disaster management scenarios where cellular connections to base stations are disrupted due to natural calamities and battery constrained drones are deployed for assistance.
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    Visualizing Realistic Benchmarked IDS Dataset: CIRA-CIC-DoHBrw-2020
    (Ieee-Inst Electrical Electronics Engineers Inc, 2022) Yusof, Mohammad Hafiz Mohd; Almohammedi, Akram A.; Shepelev, Vladimir; Ahmed, Osman
    Intrusion Detection System (IDS) dataset is crucial to detect lateral movement of cyber-attacks. IDS dataset will help to train the IDS classifier model to achieve earliest detection. A good near-realism public dataset is essential to assist the development of advanced IDS classifier models. However, the available public IDS dataset has long been under scrutiny for its practicality to reflect real low-footprint cyber threats, render real-time network scenario, reflect recent malware attack over newly developed DoH protocol, disregard layer 3 information and finally publish contradictory results of classification and analysis between various studies which makes it non-reproducible and without shareable results. This problem can be resolved by sophisticatedly visualizing a new realistic, real-time, low footprint and up-to-date benchmarked dataset. Visualization helps to detect data deformation before designing the optimized and highly accurate classifier model. Therefore, this study aims to review a new realistic benchmarked IDS dataset and apply sophisticated technique to visualize them. The review starts by carefully examining production network features. These are then compared with various well-established public IDS datasets. Many of them are static, unrealistic meta-features and disregard source and destination Internet Protocol (IP) information except CIRA-CIC-DoHBrw-2020 dataset. The study then applies Eigen Centrality (EC) technique from the graph theory to visualize this layer 3 (L3) information. Finally, using various visualization techniques such as Principal Component Analysis (PCA) and Gaussian Mixture Model (GMM), the study further analyzes and subsequently visualizes the data. Results show that the CIRA-CIC-DoHBrw-2020 simulated recent malware attack and has a very imbalanced dataset which reflects the realistic low-footprint cyber-attacks. The centrality graph clearly visualizes IPs that are compromised by recent DoH attack in real-time, and the study concludes decisively that smaller packet length of size 1000 to 2000 bytes is to fit an attack trait.