Detecting packet dropping nodes using machine learning techniques in Mobile ad-hoc network: A survey

Patel, Nirav J.; Jhaveri, Rutvij H.

doi:10.1109/spaces.2015.7058308

Cited by 20 publications

(6 citation statements)

References 20 publications

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“…As controllers are independent of each other and eNB is not involved in any traffic exchange, we call it a distributed approach. Our scenario is the case of multiple agents with multiple actions; therefore, we use Multi-Agent Reinforcement Learning (MARL) algorithm to solve it [157,158]. There are two distinct ways in which Q-learning could be applied to a multi-agent system.…”

Section: Distributed Slot Allocationmentioning

confidence: 99%

Resource allocation in clustered M2M networks: a q-learning approach

Hussain¹

2021

Preprint

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Machine to machine (M2M) communication has received increasing attention in recent years. A M2M network exhibits salient features such as large number of machines/devices, low data rates, delay tolerant/sensitive, small sized packets, energy-constrained and low or no mobility. A large number of M2M terminals may exist in a small area with many trying to simultaneously and randomly access for channel resources - which will result in overload and access problem. This increased signaling overhead and diverse requirements of machine type communication devices (MTCDs) call for the development of flexible and efficient scheduling and random access techniques. In this thesis, we first review and compare various scheduling and random access techniques in LTE-based cellular networks for M2M communication. We also discuss how successful they are to fulfill the unique requirements of M2M communication and networking. Resource management in M2M networks with a large number devices is also reviewed from the access point of view. We propose a multi-objective optimization based solution to the problem of resource allocation in interference-limited M2M communication. We consider MTCDs in a clustered network structure, where they are divided into clusters and the devices belonging to a cluster communicate to cluster head (or controller). We maximize the number of admitted MTCD controllers and throughput with least interference caused to conventional primary users. We formulate the problem as a mixed-integer non-linear problem with multiple objectives and solve it using meshed adaptive direct search (MADS) algorithm. Simulation results show the effects of varying different parameters on cumulative throughput and the number of admitted iii MTCD controllers. We then formulate the slot selection problem in M2M networks with admitted MTCDs as an optimization problem. We present a solution using the Q-learning algorithm to select conflict-free slot assignment in a random access network with MTCD controllers. The performance of the solution is dependent on parameters such as learning rate and reward. We thoroughly analyze the performance of the proposed algorithm considering different parameters related to its operation. We also compare it with simple ALOHA and channel-based scheduled allocation and show that the proposed Q-learning based technique has a higher probability of assigning slots compared to these techniques. We then present a block based Q-learning algorithm for the scheduling of MTCDs in clustered M2M communication networks. At first centralized slot assignment is done and an algorithm is proposed for minimizing the inter-cluster interference. Then we propose to use an Q-learning algorithm to assign slots in a distributed manner and comparison is made between the two schemes. Afterwards, we show the effects of distributed slot-assignment with respect to varying signal-to-interference ratio on convergence rate and convergence probability. Cumulative distribution function is used to study the effect of various SIR threshold levels on the convergence probability. With the increase in SIR threshold levels, increase in convergence time and decrease in convergence probability are observed, as less block configuration fulfills the required threshold in the M2M network.

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Section: Distributed Slot Allocationmentioning

confidence: 99%

Resource allocation in clustered M2M networks: a q-learning approach

Hussain¹

2021

Preprint

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“…Security properties play a key role in any distributed systems such as military infrastructure, banking, e-commerce, safety-critical autonomous systems, mobile ad-hoc and more [140]. Many analysis and modeling techniques have been proposed to make sure the correctness of the security protocol.…”

Section: Testing Security Propertiesmentioning

confidence: 99%

Applications in Security and Evasions in Machine Learning: A Survey

2020

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In recent years, machine learning (ML) has become an important part to yield security and privacy in various applications. ML is used to address serious issues such as real-time attack detection, data leakage vulnerability assessments and many more. ML extensively supports the demanding requirements of the current scenario of security and privacy across a range of areas such as real-time decision-making, big data processing, reduced cycle time for learning, cost-efficiency and error-free processing. Therefore, in this paper, we review the state of the art approaches where ML is applicable more effectively to fulfill current real-world requirements in security. We examine different security applications’ perspectives where ML models play an essential role and compare, with different possible dimensions, their accuracy results. By analyzing ML algorithms in security application it provides a blueprint for an interdisciplinary research area. Even with the use of current sophisticated technology and tools, attackers can evade the ML models by committing adversarial attacks. Therefore, requirements rise to assess the vulnerability in the ML models to cope up with the adversarial attacks at the time of development. Accordingly, as a supplement to this point, we also analyze the different types of adversarial attacks on the ML models. To give proper visualization of security properties, we have represented the threat model and defense strategies against adversarial attack methods. Moreover, we illustrate the adversarial attacks based on the attackers’ knowledge about the model and addressed the point of the model at which possible attacks may be committed. Finally, we also investigate different types of properties of the adversarial attacks.

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“…Meenakshi patel et al [5] [13] proposed as, detecting malicious attacks happing in AODV rule of conduct. Misbehavior as environmental factor, movement speed and communication range these types of misbehaving pattern detecting and classify them with SVM method of supervised learning method of machine learning.…”

Section: Related Workmentioning

confidence: 99%

“…MANET is a wireless and decentralized network [13] [19]. MANET routing depends on routing rules of conduct that divides into three categories: proactive, reactive and hybrid [17].…”

Section: Introductionmentioning

confidence: 99%

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Detecting Packet Dropping Misbehaving Nodes using Support Vector Machine (SVM) in MANET

Patel¹,

Jhaveri²

2015

IJCA

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Mobile ad-hoc network is suffering with various attacks due to the infrastructure-less network. Hence, MANET needs very specific security methods to detect false entrance of the misbehavior nodes. The networks work well if the nodes are trusty and act rightly cooperatively. In this paper, we are identifying and detecting packet dropping nodes using Support vector machine. Support vector machine is used reactively to classify nodes in two different classes either normal or malicious nodes. SVM takes as input the neighbor trust value, calculated with data packets and control packets. Our technique is implemented with AODV (Ad-hoc on demand vector routing) protocol. Our experimental results evaluated using packet delivery ratio (PDR), End-To-End delay, Average throughput, Normalized Routing Overhead, Average Energy Consumption.

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Detecting packet dropping nodes using machine learning techniques in Mobile ad-hoc network: A survey

Cited by 20 publications

References 20 publications

Resource allocation in clustered M2M networks: a q-learning approach

Resource allocation in clustered M2M networks: a q-learning approach

Applications in Security and Evasions in Machine Learning: A Survey

Detecting Packet Dropping Misbehaving Nodes using Support Vector Machine (SVM) in MANET

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