There are some security issues in Mobile Ad hoc Networks (MANETs) due to mobility, dynamic topology changes, and lack of any infrastructure. In MANETs, it is of great importance to detect anomaly and malicious behavior. In order to detect malicious attacks via intrusion detection systems and analyze the data set, we need to select some features. Hence, feature selection plays critical role in detecting various attacks. In the literature, there are several proposals to select such features. Usually, Principal Component Analysis (PCA) analyzes the data set and the selected features. In this paper, we have collected a feature set from some state-of-the-art works in the literature. Actually, our simulation shows this feature set detect anomaly behavior more accurate. In addition, for the first time, we use robust PCA for analyzing the data set instead of PCA in MANET. By means of robust PCA, we have an unsupervised algorithm versus semi-supervised provided by PCA. In contrast to PCA, our results show robust PCA cannot be affected by outlier data within the network. In this paper, normal and attack states are simulated and the results are analyzed.
The exponentially increasing number of ubiquitous wireless devices connected to the Internet in Internet of Things (IoT) networks highlights the need for a new paradigm of data flow management in such large-scale networks under software defined wireless networking (SDWN). The limited power and computation capability available at IoT devices as well as the centralized management and decision making approach in SDWN introduce a whole new set of security threats to the networks. In particular, the authentication mechanism between the controllers and the forwarding devices in SDWNs is a key challenge from both secrecy and integrity aspects. Conventional authentication protocols based on public key infrastructure (PKI) are no longer sufficient for these networks considering the large-scale and heterogeneity nature of the networks as well as their deployment cost, and security vulnerabilities due to key distribution and storage. We propose a novel security protocol based on physical unclonable functions (PUFs) known as hardware security primitives to enhance the authentication security in SDWNs. In this approach, digital PUFs are developed using the inherent randomness of the nanomaterials of Resistive Random Access Memory (ReRAM) that are embedded in most IoT devices to enable a secure authentication and access control in these networks. These PUFs are developed based on a novel approach of multi states, in which the natural drifts due to the physical variations in the environment are predicted to reduce the potential errors in challenge-response pairs of PUFs being tested in different situations. We also proposed a PUF-based PKI protocol to secure the controller in SDWNs. The performance of the developed ReRAM-based PUFs are evaluated in the experimental results. Moreover, the effectiveness of the proposed multi-state machine learning technique to predict the drifts of the PUFs' responses in different temperature and biased conditions is presented. of such networks. In these networks, whenever the network administrators need to change or update a parameter of the protocols, they may need to re-configure all related devices (i.e. routers, switches, and firewalls) throughout the network. Depending on the size of the network, it can be a burdensome and time-consuming process.The recently developed SDN technology aims at addressing the aforementioned challenges by separating the control and data planes. In the SDN paradigm, instead of designating the decision making to every active components in the network, this will be handled by a centralized controller called the network operating system (NOS). For instance, when a switch receives a packet, it chooses the proper action (forward, drop, modify, sending to the controller, etc.) based on the rules (flow table), which are defined by the programmable network applications at the centralized controller that rely on the NOS [39]. The communication between the NOS and the forwarding layer or the data plane is established by some protocols such as OpenFlow [49]. In contrast to ...
Device-to-device (D2D) communication in cellular networks is defined as direct communication between two mobile users without traversing the base station (BS) or core network. D2D communication can occur on the cellular frequencies (i.e., inband) or unlicensed spectrum (i.e., outband). A high capacity IEEE 802.11-based outband device-todevice communication system for cellular networks is introduced in this paper. Transmissions in device-to-device connections are managed using our proposed medium access control (MAC) protocol. In the proposed MAC protocol, backoff window size is adjusted dynamically considering the current network status and utilizing an appropriate transmission attempt rate. We have considered both cases that the request to send/clear to send (RTS/CTS) mechanism is and is not used in our protocol design. Describing mechanisms for guaranteeing quality of service (QoS) and enhancing reliability of the system is another part of our work. Moreover, performance of the system in the presence of channel impairments is investigated analytically and through simulations. Analytical and simulation results demonstrate that our proposed system has high throughput, and it can provide different levels of QoS for its users.
Masih Abedini, candidate for the degree of Master of Applied Science in Electronic Systems Engineering, has presented a thesis titled, Active eavesdroppers detection system in multi-hop wireless sensor networks, in an oral examination held on July 25, 2022. The following committee members have found the thesis acceptable in form and content, and that the candidate demonstrated satisfactory knowledge of the subject material.
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