Browsing by Author "Jamali, Mohammad Ali Jabraeil"

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Citation - WoS: 7
Citation - Scopus: 6
Botnets Unveiled: a Comprehensive Survey on Evolving Threats and Defense Strategies
(Wiley, 2024) Jafari Navimipour, Nima; Jamali, Mohammad Ali Jabraeil; Heidari, Arash; Navimipour, Nima Jafari
Botnets have emerged as a significant internet security threat, comprising networks of compromised computers under the control of command and control (C&C) servers. These malevolent entities enable a range of malicious activities, from denial of service (DoS) attacks to spam distribution and phishing. Each bot operates as a malicious binary code on vulnerable hosts, granting remote control to attackers who can harness the combined processing power of these compromised hosts for synchronized, highly destructive attacks while maintaining anonymity. This survey explores botnets and their evolution, covering aspects such as their life cycles, C&C models, botnet communication protocols, detection methods, the unique environments botnets operate in, and strategies to evade detection tools. It analyzes research challenges and future directions related to botnets, with a particular focus on evasion and detection techniques, including methods like encryption and the use of covert channels for detection and the reinforcement of botnets. By reviewing existing research, the survey provides a comprehensive overview of botnets, from their origins to their evolving tactics, and evaluates how botnets evade detection and how to counteract their activities. Its primary goal is to inform the research community about the changing landscape of botnets and the challenges in combating these threats, offering guidance on addressing security concerns effectively through the highlighting of evasion and detection methods. The survey concludes by presenting future research directions, including using encryption and covert channels for detection and strategies to strengthen botnets. This aims to guide researchers in developing more robust security measures to combat botnets effectively. Exploring botnets: evolution, tactics, countermeasures. This survey dives into botnets, covering life cycles, communication, and evasion tactics. It highlights challenges and future strategies for combating cyber threats. image
Citation - WoS: 34
Citation - Scopus: 38
Deep Q-Learning Technique for Offloading Offline/Online Computation in Blockchain-Enabled Green Iot-Edge Scenarios
(Mdpi, 2022) Heidari, Arash; Jafari Navimipour, Nima; Jamali, Mohammad Ali Jabraeil; Navimipour, Nima Jafari; Akbarpour, Shahin
The number of Internet of Things (IoT)-related innovations has recently increased exponentially, with numerous IoT objects being invented one after the other. Where and how many resources can be transferred to carry out tasks or applications is known as computation offloading. Transferring resource-intensive computational tasks to a different external device in the network, such as a cloud, fog, or edge platform, is the strategy used in the IoT environment. Besides, offloading is one of the key technological enablers of the IoT, as it helps overcome the resource limitations of individual objects. One of the major shortcomings of previous research is the lack of an integrated offloading framework that can operate in an offline/online environment while preserving security. This paper offers a new deep Q-learning approach to address the IoT-edge offloading enabled blockchain problem using the Markov Decision Process (MDP). There is a substantial gap in the secure online/offline offloading systems in terms of security, and no work has been published in this arena thus far. This system can be used online and offline while maintaining privacy and security. The proposed method employs the Post Decision State (PDS) mechanism in online mode. Additionally, we integrate edge/cloud platforms into IoT blockchain-enabled networks to encourage the computational potential of IoT devices. This system can enable safe and secure cloud/edge/IoT offloading by employing blockchain. In this system, the master controller, offloading decision, block size, and processing nodes may be dynamically chosen and changed to reduce device energy consumption and cost. TensorFlow and Cooja's simulation results demonstrated that the method could dramatically boost system efficiency relative to existing schemes. The findings showed that the method beats four benchmarks in terms of cost by 6.6%, computational overhead by 7.1%, energy use by 7.9%, task failure rate by 6.2%, and latency by 5.5% on average.
Citation - WoS: 2
Citation - Scopus: 1
Enhancing Solar Convection Analysis With Multi-Core Processors and Gpus
(Wiley, 2024) Jafari Navimipour, Nima; Amiri, Zahra; Jamali, Mohammad Ali Jabraeil; Navimipour, Nima Jafari
In the realm of astrophysical numerical calculations, the demand for enhanced computing power is imperative. The time-consuming nature of calculations, particularly in the domain of solar convection, poses a significant challenge for Astrophysicists seeking to analyze new data efficiently. Because they let different kinds of data be worked on separately, parallel algorithms are a good way to speed up this kind of work. A lot of this study is about how to use both multi-core computers and GPUs to do math work about solar energy at the same time. Cutting down on the time it takes to work with data is the main goal. This way, new data can be looked at more quickly and without having to practice for a long time. It works well when you do things in parallel, especially when you use GPUs for 3D tasks, which speeds up the work a lot. This is proof of how important it is to adjust the parallelization methods based on the size of the numbers. But for 2D math, computers with more than one core work better. The results not only fix bugs in models of solar convection, but they also show that speed changes a little based on the gear and how it is processed.
Citation - WoS: 2
Citation - Scopus: 2
Fuzzy Logic Multicriteria Decision-Making for Broadcast Storm Resolution in Vehicular Ad Hoc Networks
(Wiley, 2024) Jafari Navimipour, Nima; Jamali, Mohammad Ali Jabraeil; Navimipour, Nima Jafari
In vehicular ad hoc networks (VANETs), the challenge of broadcast storms during data transmission arises due to an exponential increase in message rebroadcasts. This problem is exacerbated by high-speed node movements, frequent topology changes, and repetitive discontinuities within these networks, hindering the development of efficient broadcasting protocols. Addressing this gap, our study introduces a pioneering approach utilizing a novel fuzzy method based on multicriteria decision-making (MCDM) to prioritize vehicles in selecting optimal neighbors for data broadcast. The aim of this work is to propose for VANETs a fuzzy MCDM-based re-broadcasting scheme (FMRBS). This method seeks to eliminate broadcast storms and raise data distribution efficiency. We choose the best vehicles for data transportation by using fuzzy logic. The FMRBS system excelled in many respects over UMB and 802.11-Distance. It decreased end-to-end latency and overhead while increasing packet delivery ratio (PDR) and network performance. By efficiently optimizing data distribution inside VANETs, FMRBS lowers broadcasting traffic and network congestion.
Citation - WoS: 50
Citation - Scopus: 51
A Green, Secure, and Deep Intelligent Method for Dynamic Iot-Edge Offloading Scenarios
(Elsevier, 2023) Heidari, Arash; Jafari Navimipour, Nima; Navimipour, Nima Jafari; Jamali, Mohammad Ali Jabraeil; Akbarpour, Shahin
To fulfill people's expectations for smart and user-friendly Internet of Things (IoT) applications, the quantity of processing is fast expanding, and task latency constraints are becoming extremely rigorous. On the other hand, the limited battery capacity of IoT objects severely affects the user experience. Energy Harvesting (EH) technology enables green energy to offer a continuous energy supply for IoT objects. It provides a solid assurance for the proper functioning of resource-constrained IoT objects when combined with the maturation of edge platforms and the development of parallel computing. The Markov Decision Process (MDP) and Deep Learning (DL) are used in this work to solve dynamic online/offline IoT-edge offloading scenarios. The suggested system may be used in both offline and online contexts and meets the user's quality of service expectations. Also, we investigate a blockchain scenario in which edge and cloud could work toward task offloading to address the tradeoff between limited processing power and high latency while ensuring data integrity during the offloading process. We provide a double Q-learning solution to the MDP issue that maximizes the acceptable offline offloading methods. During exploration, Transfer Learning (TL) is employed to quicken convergence by reducing pointless exploration. Although the recently promoted Deep Q-Network (DQN) may address this space complexity issue by replacing the huge Q-table in standard Q-learning with a Deep Neural Network (DNN), its learning speed may still be insufficient for IoT apps. In light of this, our work introduces a novel learning algorithm known as deep Post-Decision State (PDS)-learning, which combines the PDS-learning approach with the classic DQN. The system component in the proposed system can be dynamically chosen and modified to decrease object energy usage and delay. On average, the proposed technique outperforms multiple benchmarks in terms of delay by 4.5%, job failure rate by 5.7%, cost by 4.6%, computational overhead by 6.1%, and energy consumption by 3.9%.
Citation - WoS: 37
Citation - Scopus: 44
A hybrid approach for latency and battery lifetime optimization in IoT devices through offloading and CNN learning
(Elsevier, 2023) Jafari Navimipour, Nima; Navimipour, Nima Jafari; Jamali, Mohammad Ali Jabraeil; Akbarpour, Shahin
Offloading assists in overcoming the resource constraints of specific elements, making it one of the primary technical enablers of the Internet of Things (IoT). IoT devices with low battery capacities can use the edge to offload some of the operations, which can significantly reduce latency and lengthen battery lifetime. Due to their restricted battery capacity, deep learning (DL) techniques are more energy-intensive to utilize in IoT devices. Because many IoT devices lack such modules, numerous research employed energy harvester modules that are not available to IoT devices in real-world circumstances. Using the Markov Decision Process (MDP), we describe the offloading problem in this study. Next, to facilitate partial offloading in IoT devices, we develop a Deep Reinforcement learning (DRL) method that can efficiently learn the policy by adjusting to network dynamics. Convolutional Neural Network (CNN) is then offered and implemented on Mobile Edge Computing (MEC) devices to expedite learning. These two techniques operate together to offer the proper offloading approach throughout the length of the system's operation. Moreover, transfer learning was employed to initialize the Qtable values, which increased the system's effectiveness. The simulation in this article, which employed Cooja and TensorFlow, revealed that the strategy outperformed five benchmarks in terms of latency by 4.1%, IoT device efficiency by 2.9%, energy utilization by 3.6%, and job failure rate by 2.6% on average.
Citation - WoS: 0
Citation - Scopus: 0
An Innovative Performance Assessment Method for Increasing the Efficiency of Aodv Routing Protocol in Vanets Through Colored Timed Petri Nets
(Wiley, 2025) Heidari, Arash; Jamali, Mohammad Ali Jabraeil; Navimipour, Nima Jafari
Routing protocols are pivotal in Vehicular Ad hoc Networks (VANETs), serving as the backbone for efficient routing discovery, particularly within the realm of Intelligent Transportation Systems (ITS). However, ensuring their seamless functionality within VANET environments necessitates rigorous verification and formal modeling. Colored Timed Petri Nets (CTPNs) stand out as a valuable mathematical and formal method for this purpose. This study shows a new way to describe the Ad hoc On-Demand Distance Vector (AODV) routing system in VANETs using CTPNs. There are nine pages of detailed analysis using this new modeling method, which allows you to examine success across many levels of a hierarchy. This study provides a strong foundation for building and testing the AODV routing system in VANETs, showing how well it functions in real-life situations. It is interesting to see how the results of the CTPN-based model and simulations compare. Notably, the model finds routes in an average of 32 s, while tests show that it takes 56 s. Additionally, the model's overall number of sent and received packets closely matches the results from the exercise. Furthermore, the suggested plan shows a yield of 41%. Strict T-tests indicate that the modeling results are highly reliable.