DEAR-2: An Energy-Aware Routing Protocol with Guaranteed Delivery in Wireless Ad-hoc Networks

Hassan, Muhammad Umair; Shahzaib, Muhammad; Shaukat, Kamran; Hussain, Syed Nakhshab; Mubashir, Muhammad; Karim, Sabiha; Shabir, Muhammad

doi:10.1007/978-3-319-99966-1_20

Cited by 9 publications

(8 citation statements)

References 13 publications

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“…Nowadays, Internet of Things (IoT) and cloud computing networks are dominant in the Internet. The fast development in wireless sensor network technology has made the IoT network becomes more complex in term of applications [2] and routing [3]. Naturally, security turns out to be extremely important, especially in cloud environment [4].…”

Section: Related Workmentioning

confidence: 99%

User Behavior Traffic Analysis Using a Simplified Memory-Prediction Framework

Budiarto¹,

Alqarni²,

Alzahrani³

et al. 2022

Computers, Materials &Amp; Continua

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As nearly half of the incidents in enterprise security have been triggered by insiders, it is important to deploy a more intelligent defense system to assist enterprises in pinpointing and resolving the incidents caused by insiders or malicious software (malware) in real-time. Failing to do so may cause a serious loss of reputation as well as business. At the same time, modern network traffic has dynamic patterns, high complexity, and large volumes that make it more difficult to detect malware early. The ability to learn tasks sequentially is crucial to the development of artificial intelligence. Existing neurogenetic computation models with deep-learning techniques are able to detect complex patterns; however, the models have limitations, including catastrophic forgetfulness, and require intensive computational resources. As defense systems using deep-learning models require more time to learn new traffic patterns, they cannot perform fully online (on-the-fly) learning. Hence, an intelligent attack/malware detection system with on-the-fly learning capability is required. For this paper, a memory-prediction framework was adopted, and a simplified single cell assembled sequential hierarchical memory (s.SCASHM) model instead of the hierarchical temporal memory (HTM) model is proposed to speed up learning convergence to achieve onthe-fly learning. The s.SCASHM consists of a Single Neuronal Cell (SNC) model and a simplified Sequential Hierarchical Superset (SHS) platform. The s.SCASHM is implemented as the prediction engine of a user behavior analysis tool to detect insider attacks/anomalies. The experimental results show that the proposed memory model can predict users' traffic behavior with accuracy level ranging from 72% to 83% while performing on-the-fly learning.

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Section: Related Workmentioning

confidence: 99%

User Behavior Traffic Analysis Using a Simplified Memory-Prediction Framework

Budiarto¹,

Alqarni²,

Alzahrani³

et al. 2022

Computers, Materials &Amp; Continua

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“…RSS (Received Signal Strength) is used in many schemes to generate the key. Such schemes are also available in different scenarios such as an increase in the size of the network [13] or mobile, in which many channel variations are assured by the movement of intermediate objects or entities. This means that such solutions are incapable of static scenarios without a considerable channel variation.…”

Section: Introductionmentioning

confidence: 99%

Sustainable Network by Enhancing Attribute-Based Selection Mechanism Using Lagrange Interpolation

Monga

Gupta

Prasad³

et al. 2022

Sustainability

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The security framework in Ad-hoc Networks (ANET) continues to attract the attention of researchers, although significant work has been accomplished already. Researchers in the last couple of years have shown quite an improvement in Identity Dependent Cryptography (IDC). Security in ANET is hard to attain due to the vulnerability of links (Wireless). IDC encompasses Polynomial Interpolations (PI) such as Lagrange, curve-fitting, and spline to provide security by implementing Integrated Key Management (IKM). The PI structure trusts all the available nodes in the network and randomly picks nodes for the security key generation. This paper presents a solution to the trust issues raised in Lagrange’s-PI (LI) utilizing an artificial neural network and attribute-based tree structure. The proposed structure not only improves the trust factor but also enhances the accuracy measures of LI to provide a sustainable network system. Throughput, PDR, noise, and latency have been increased by 47%, 50%, 34%, and 30%, respectively, by using LI and incorporating the aforementioned techniques.

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“…In this case, the information can be embedded by manipulating the packet timing information (i.e., covert timing channel) [6][7][8][9][10] or putting some bits into the packet headers (i.e., covert storage channel) [11,12]. So-called network steganography understood in a broad sense can be applied to both physical layer symbol frames [13], protocols of medium access control (MAC) [14], routing [15], networks [16], or higher layers, e.g., Transmission Control Protocol/Internet Protocol (TCP/IP) [11], Hypertext Transfer Protocol (HTTP) [17], and Domain Name System (DNS) [18]. These methods can be applied in homogeneous wired and wireless networks (e.g., accordant with the IEEE 802.11 standard [12,13]) as well as in heterogeneous ad hoc networks (e.g., [15]).…”

Section: Introductionmentioning

confidence: 99%

“…So-called network steganography understood in a broad sense can be applied to both physical layer symbol frames [13], protocols of medium access control (MAC) [14], routing [15], networks [16], or higher layers, e.g., Transmission Control Protocol/Internet Protocol (TCP/IP) [11], Hypertext Transfer Protocol (HTTP) [17], and Domain Name System (DNS) [18]. These methods can be applied in homogeneous wired and wireless networks (e.g., accordant with the IEEE 802.11 standard [12,13]) as well as in heterogeneous ad hoc networks (e.g., [15]). Along with the development of network steganography techniques, we may notice novel solutions of network steganalysis, e.g., [19].…”

Section: Introductionmentioning

confidence: 99%

A Wireless Covert Channel Based on Dirty Constellation with Phase Drift

2021

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Modern telecommunications systems require the use of various transmission techniques, which are either open or hidden. The open transmission system uses various security techniques against its unauthorized reception, and cryptographic solutions ensure the highest security. In the case of hidden transmissions, steganographic techniques are used, which are based on the so-called covert channels. In this case, the transparency and stealth of the transmission ensure its security against being picked up by an unauthorized user. These covert channels can be implemented in multimedia content, network protocols, or physical layer transmissions. This paper focuses on wireless covert channels. We present a novel method of steganographic transmission which is based on phase drift in phase-shift keying or quadrature amplitude modulation (QAM) and is included in the so-called dirty constellation techniques. The proposed approach is based on the drift correction modulation method, which was previously used in the watermarking of audio-signals. The developed solution is characterized by a variable bit rate, which can be adapted to the used modulation type and transmission conditions occurring in radio channels. In the paper, we present the method of generating and receiving hidden information, simulation research, and practical implementation of the proposed solution using the software-defined radio platform for selected QAM.

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DEAR-2: An Energy-Aware Routing Protocol with Guaranteed Delivery in Wireless Ad-hoc Networks

Cited by 9 publications

References 13 publications

User Behavior Traffic Analysis Using a Simplified Memory-Prediction Framework

User Behavior Traffic Analysis Using a Simplified Memory-Prediction Framework

Sustainable Network by Enhancing Attribute-Based Selection Mechanism Using Lagrange Interpolation

A Wireless Covert Channel Based on Dirty Constellation with Phase Drift

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