Browsing by Author "Jamali, Mohammad Ali Jabraeil"

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Botnets Unveiled: a Comprehensive Survey on Evolving Threats and Defense Strategies
(Wiley, 2024) Asadi, Mehdi; 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 Count: 27
Deep Q-Learning Technique for Offloading Offline/Online Computation in Blockchain-Enabled Green IoT-Edge Scenarios
(Mdpi, 2022) Heidari, Arash; 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 Count: 28
A green, secure, and deep intelligent method for dynamic IoT-edge-cloud offloading scenarios
(Elsevier, 2023) Heidari, Arash; 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 Count: 23
A hybrid approach for latency and battery lifetime optimization in IoT devices through offloading and CNN learning
(Elsevier, 2023) Heidari, Arash; 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.