A simple scheme for pseudo clustering algorithm for cross layer intrusion detection in MANET

Amouri, Amar; Jaimes, Luis G.; Manthena, Raju; Morgera, S.D.; Vergara-Laurens, Idalides

doi:10.1109/latincom.2015.7430139

Cited by 10 publications

(5 citation statements)

References 14 publications

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“…In [106], Amouri et al proposed a framework of intrusion detection for MANETs. The framework considered a hierarchical architecture where the intrusion detection is distributed through a set of promiscuous zones (PZ).…”

Section: Defense On Network Layermentioning

confidence: 99%

“…Error detection codes [18] TDM, Error correction codes [18] Denial of sleep and flooding MAC Anomaly detection on motes [66] N/A De-Synchronization MAC N/A 6TiSCH [97] Unfairness MAC N/A Usage of small frames [57] Blackhole Network Anomaly detection on motes [66], REWARD [98], ActiveTrust [103], Packet count [104], TinyOS beaconing [105], Honeypot [100], Watchdog [101], Pseudo clustering algorithm [106] REWARD routing [98], Multi-path routing [59], [93], [120], Mesh network topology [99], ActiveTrust routing [103], Isolation [104], BAMBi [105], MAODV [102] HELLO flooding Network Bidirectional verification technique [107] Identity verification protocol [59], Multi-path multibase station routing [107], µ-TESLA [84] Node-Replication (Clone)…”

Section: Macmentioning

confidence: 99%

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Security of the Internet of Things: Vulnerabilities, Attacks and Countermeasures

Butun,

Österberg,

Song

2019

Preprint

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Wireless Sensor Networks (WSNs) constitute one of the most promising third-millennium technologies and have wide range of applications in our surrounding environment. The reason behind the vast adoption of WSNs in various applications is that they have tremendously appealing features, e.g., low production cost, low installation cost, unattended network operation, autonomous and longtime operation. WSNs have started to merge with the Internet of Things (IoT) through the introduction of Internet access capability in sensor nodes and sensing ability in Internet-connected devices. Thereby, the IoT is providing access to huge amount of data, collected by the WSNs, over the Internet. However, owing to the absence of a physical line-of-defense, i.e. there is no dedicated infrastructure such as gateways to watch and observe the flowing information in the network, security of WSNs along with IoT is of a big concern to the scientific community. More specifically, for the application areas in which CIA (confidentiality, integrity, availability) has prime importance, WSNs and emerging IoT technology might constitute an open avenue for the attackers. Besides, recent integration and collaboration of WSNs with IoT will open new challenges and problems in terms of security. Hence, this would be a nightmare for the individuals using these systems as well as the security administrators who are managing those networks. Therefore, a detailed review of security attacks towards WSNs and IoT, along with the techniques for prevention, detection, and mitigation of those attacks are provided in this paper. In this text, attacks are categorized and treated into mainly two parts, most or all types of attacks towards WSNs and IoT are investigated under that umbrella: "Passive Attacks" and "Active Attacks". Understanding these attacks and their associated defense mechanisms will help paving a secure path towards the proliferation and public acceptance of IoT technology.

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Section: Defense On Network Layermentioning

confidence: 99%

Section: Macmentioning

confidence: 99%

Security of the Internet of Things: Vulnerabilities, Attacks and Countermeasures

Butun,

Österberg,

Song

2019

Preprint

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“…Machine learning and artificial intelligence-based IDSs were studied extensively during the last decade. Various machine algorithms were explored such as: Neural networks [1] and its newer version, deep learning [2], support vector machines (SVM) [3], decision trees [4], k-NN clustering [5], and Naïve Bayes [6]. However, a study presented by [7] shows several advantages for using random forest when it comes to the complexity, accuracy, and memory usage.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Based Intrusion Detection System for Mobile Internet of Things

Amouri

Alaparthy

Morgera

2020

Sensors

Self Cite

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Intrusion detection systems plays a pivotal role in detecting malicious activities that denigrate the performance of the network. Mobile adhoc networks (MANETs) and wireless sensor networks (WSNs) are a form of wireless network that can transfer data without any need of infrastructure for their operation. A more novel paradigm of networking, namely Internet of Things (IoT) has emerged recently which can be considered as a superset to the afore mentioned paradigms. Their distributed nature and the limited resources available, present a considerable challenge for providing security to these networks. The need for an intrusion detection system (IDS) that can acclimate with such challenges is of extreme significance. Previously, we proposed a cross layer-based IDS with two layers of detection. It uses a heuristic approach which is based on the variability of the correctly classified instances (CCIs), which we refer to as the accumulated measure of fluctuation (AMoF). The current, proposed IDS is composed of two stages; stage one collects data through dedicated sniffers (DSs) and generates the CCI which is sent in a periodic fashion to the super node (SN), and in stage two the SN performs the linear regression process for the collected CCIs from different DSs in order to differentiate the benign from the malicious nodes. In this work, the detection characterization is presented for different extreme scenarios in the network, pertaining to the power level and node velocity for two different mobility models: Random way point (RWP), and Gauss Markov (GM). Malicious activity used in the work are the blackhole and the distributed denial of service (DDoS) attacks. Detection rates are in excess of 98% for high power/node velocity scenarios while they drop to around 90% for low power/node velocity scenarios.

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“…Dhakne et al [12] DTBID enables the development of the trust model to prevent the malicious attack in the wireless sensor networks considering the energy, reliability and data. Amouri et al [13] the intrusion based on the cross layer feature collection from medium access control and network layers. With a hierarchical based approach that eludes the clustering and the sequentially transmits the packet within its communication range.…”

Section: Introductionmentioning

confidence: 99%

Energy-Aware Security Routing Protocol for WSN in Big-Data Applications

Smys

2019

JISMAC

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The dense deployment of the wireless sensor networks has caused enormous data flow leading to a Big-Data generation. To enable a continuous transmission for the huge volume of data packets, it becomes necessary to readapt the routing protocol to facilitate the routing in handling the Big-data scenario. Since energy is the main constraint in the wireless sensor network, as the sensor are battery powered, and the routing methods consuming enormous of the energy for route discovery thereby reducing the network life time, many conventional methods were developed to address the problem of energy consumption with the, increased network latency, overhead, security issues and delay. So the paper proposes the energy-aware routing protocol that could handle the security issues, arising in the enormous data generation satisfying the QOS requirements. The node with substantial resources are selected to overcome the problems of energy consumption and the network life time handling the security issues , delay, network latency and the overhead problem. Further validation of the proposed method in the Network Simulator-3 is performed to evaluate the network latency, packet delivery ratio, throughput, power consumption, network lifetime and Cost.

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A simple scheme for pseudo clustering algorithm for cross layer intrusion detection in MANET

Cited by 10 publications

References 14 publications

Security of the Internet of Things: Vulnerabilities, Attacks and Countermeasures

Security of the Internet of Things: Vulnerabilities, Attacks and Countermeasures

A Machine Learning Based Intrusion Detection System for Mobile Internet of Things

Energy-Aware Security Routing Protocol for WSN in Big-Data Applications

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