Yazar "Abbood, Zainab Ali" seçeneğine göre listele

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    Öğe
    A survey on intrusion detection system in ad hoc networks based on machine learning
    (IEEE, 2021) Abbood, Zainab Ali; Atilla, Doğu Çağdaş; Aydın, Çağatay; Mahmoud, Mahmoud Shuker
    This advanced research survey aims to perform intrusion detection and routing in ad hoc networks in wireless MANET networks using machine learning techniques. The MANETs are composed of several ad-hoc nodes that are randomly or deterministically distributed for communication and acquisition and to forward the data to the gateway for enhanced communication securely. MANETs are used in many applications such as in health care for communication; in utilities such as industries to monitor equipment and detect any malfunction during regular production activity. In general, MANETs take measurements of the desired application and send this information to a gateway, whereby the user can interpret the information to achieve the desired purpose. The main importance of MANETs in intrusion detection is that they can be trained to detect intrusion and real-time attacks in the CIC-IDS 2019 dataset. MANETs routing protocols are designed to establish routes between the source and destination nodes. What these routing protocols do is that they decompose the network into more manageable pieces and provide ways of sharing information among its neighbors first and then throughout the whole network. The landscape of exciting libraries and techniques is constantly evolving, and so are the possibilities and options for experiments. Implementing the framework in python helps in reducing syntactic complexity, increases performance compared to implementations in scripting languages, and provides memory safety.
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    Automatic detection of vehicle congestion by using roadside unit
    (2021) Abbood, Zainab Ali; Ilyas, Muhammad; Aydın, Çağatay; Mahmoud, Mahmoud Shuker; Abdulredha, Nida
    The presence of Roadside Units (RSUs) helps network loads to be expanded to the other nodes that have already been far away from frequent node exposure. We proposed in this work utilizing the mobile node to operate as a roadside unit and operate data packet routing such as roadside units does. The main problem of utilizing the huge number of roadside units is the spending of huge time for data provision by a reduction in performance. Also, in this paper, we attempt using the various number of mobile nodes such as roadside units that are different from traditional roadside units such as the past is fixed and the second is active as a movement previous. The proposed method was executing by utilizing ad hoc on demand distance vector (AODV) routing is a path protocol for mobile ad-hoc networks (MANETs) and other wireless ad-hoc networks, this protocol designed for usage of ad-hoc mobile networks. Also, it's an active protocol, the routes are made only when they are needed, common routing tables, one entry single destination, and supplement numbers in conformity with determine whether routing information is up- to-date and to forestall routing loops. In this paper, mobile vehicles move randomly on highways, so in the event of a collision is too high, it is assumed that the vehicle will stop, and the collision site will be subject to accommodate more than one vehicle. Where vehicles are driven at high speed. Due to the driver's ignorance of the accident area, they can enter it, and thus the problem is magnified. The results achieved shows that numerous mobile nodes as a roadside unit may enhance the communication according to the computation of the average time delay and the link duration of the connection and reconnecting each node. Therefore, the results may reduce the delay time and maintain the connection for a longer period, as shown in the fourth simulation model.
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    Challenges and Future Directions for Intrusion Detection Systems Based on AutoML
    (Mesopotamian Academic Press, 2021) Abbood, Zainab Ali; Khaleel, Ismael; Aggarwal, Karan
    Recent use of computer systems and the Internet has contributed to severe protection, privacy and confidentiality problems due to the processes involved in the electronic data transformation. Much has been done to improve the security and privacy of information systems, but these issues remain in computer systems; there is, in fact, no system in the world That is early stable. Furthermore, various network attacks develop when the signature database incorporates a new signature with irregular behaviour. With many types of attacks emerging, many techniques are being built and used in many forms of network attacks. Intrusion detection systems ( IDS) are one of those methods. This method allows the management of several network networks, cloud storage and an information system. The IDS can track and detect attacks to breach a system's security features (confidentiality, availability, and integrity). This research aims at classifying IDS based on their intended goal and to compare different types of IDS in each class. © 2021, Mesopotamian Academic Press. All rights reserved.
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    Enhancement of the performance of MANET using machine learning approach based on SDNs
    (Elsevier GmbH, 2023) Abbood, Zainab Ali; Atilla, Doğu Çağdaş; Aydın, Çağatay
    Deep learning (DL) is a subdivision of machine learning (ML) that employs numerous algorithms, each of which provides various explanations of the data it consumes; mobile ad-hoc networks (MANET) are growing in prominence. For reasons including node mobility, due to the potential wireless sensor network (WSN) to provide a small-cost solution to real-world contact challenges. But the lifespan in this network is restricted lifespan. Therefore, the wireless sensor network (WSN) is more vulnerable to battery consumption. On the other hand, routing packets in a Wireless Sensor Network (WSN) is a challenging task, according to the limited resources available on the nodes of these networks, especially their energy sources. The use of Machine Learning (ML) techniques in a Software-Defined Network (SDN) topology has shown good potential for solving such a complex task. However, existing techniques emphasize finding the shortest paths to deliver the packets, which can overload certain nodes in the network, depending on their positioning. In this study, a new method is proposed to extend the lifetime of the WSN by balancing the loading on the nodes, using a Deep Reinforcement Learning (DRL) approach. By emphasizing the lifetime of the network, the proposed method has been able to discover and use alternative routes to deliver the packets, avoiding the use of nodes with low energy. Hence, the average number of hops the packets travel through has been increased, but the time required for the first node to exhaust its energy has been significantly increased.
  • Küçük Resim
    Öğe
    Intrusion detection system through deep learning in routing manet networks
    (Tech Science Press, 2023) Abbood, Zainab Ali; Atilla, Doğu Çağdaş; Aydın, Çağatay
    Deep learning (DL) is a subdivision of machine learning (ML) that employs numerous algorithms, each of which provides various explanations of the data it consumes; mobile ad-hoc networks (MANET) are growing in prominence. For reasons including node mobility, due to MANET’s potential to provide small-cost solutions for real-world contact challenges, decentralized management, and restricted bandwidth, MANETs are more vulnerable to security threats. When protecting MANETs from attack, encryption and authentication schemes have their limits. However, deep learning (DL) approaches in intrusion detection systems (IDS) can adapt to the changing environment of MANETs and allow a system to make intrusion decisions while learning about its mobility in the environment. IDSs are a secondary defiance system for mobile ad-hoc networks vs. attacks since they monitor network traffic and report anything unusual. Recently, many scientists have employed deep neural networks (DNNs) to address intrusion detection concerns. This paper used MANET to recognize complex patterns by focusing on security standards through efficiency determination and identifying malicious nodes, and mitigating network attacks using the three algorithms presented Cascading Back Propagation Neural Network (CBPNN), Feedforward-Neural-Network (FNN), and Cascading-Back-Propagation-Neural- Network (CBPNN) (FFNN). In addition to Convolutional-Neural-Network (CNN), these primary forms of deep neural network (DNN) building designs are widely used to improve the performance of intrusion detection systems (IDS) and the use of IDS in conjunction with machine learning (ML). Furthermore, machine learning (ML) techniques than their statistical and logical methods provide MANET network learning capabilities and encourage adaptation to different environments. Compared with another current model, The proposed model has better average receiving packet (ARP) and end-to-end (E2E) performance. The results have been obtained from CBP, FFNN and CNN 74%, 82% and 85%, respectively, by the time (27, 18, and 17 s).
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    SPEAKER IDENTIFICATION MODEL BASED ON DEEP NURAL NETWOKS
    (College of Education, Al-Iraqia University, 2022) Ahmed, Saadaldeen Rashid; Abbood, Zainab Ali; Farhan, hameed Mutlag; Yasen, Baraa Taha; Ahmed, Mohammed Rashid; Duru, Adil Deniz
    This study aims is to establish a small system of text-independent recognition of speakers for a relatively small group of speakers at a sound stage. The fascinating justification for the International Space Station (ISS) to detect if the astronauts are speaking at a specific time has influenced the difficulty. In this work, we employed Machine Learning Applications. Accordingly, we used the Direct Deep Neural Network (DNN)-based approach, in which the posterior opportunities of the output layer are utilized to determine the speaker's presence. In line with the small footprint design objective, a simple DNN model with only sufficient hidden units or sufficient hidden units per layer was designed, thereby reducing the cost of parameters through intentional preparation to avoid the normal overfitting problem and optimize the algorithmic aspects, such as context-based training, activation functions, validation, and learning rate. Two commercially available databases, namely, TIMIT clean speech and HTIMIT multihandset communication database and TIMIT noise-added data framework, were tested for this reference model that we developed using four sound categories at three distinct signal-to-noise ratios. Briefly, we used a dynamic pruning method in which the conditions of all layers are simultaneously pruned, and the pruning mechanism is reassigned. The usefulness of this approach was evaluated on all the above contact databases. © 2022 Iraqi Journal for Computer Science and Mathematics. All rights reserved.