scholarly journals Bi-level Flow Based Anomalous Activity Identification System for IoT Devices

2021 ◽  
Author(s):  
Meenigi Ramesh Babu ◽  
K. N. Veena
Keyword(s):  
Local Network ◽  
Identification System ◽  
Local Domain ◽  
Statistical Features ◽  
Large Area ◽  
Flow Features ◽  
Wireless Medium ◽  
Iot Devices ◽  
Sensitivity Specificity ◽  
Level 2

With the advanced technologies, IoT has widely emerged with data collection, processing, and communication as well in smart applications. The wireless medium in the IoT devices would broadcast the data, which makes them easily targeted by the attacks. In the local network, the normal communication attack is restricted to small local domain or local nodes. However, the attack present in IoT devices gets expanded to a large area that would cause destructive effects. The heterogeneity and distribution of IoT services/applications make the security of IoT a more challenging and complex one. This paper aims to propose a bi-level flow based anomalous activity identification system in IoT. Initially, the flow based features get extracted along with the statistical features like mean, median, variance, correlation, and correntropy. Subsequently, Bi-level classification is carried out in this work. In level 1, the presence of attack is detected and the level 2 classification classifies the type of attack. A decision tree is used for detecting the attacks by checking whether the network traffic is anomalous traffic or normal traffic. In level 2, an Optimized Neural network (NN) is used for categorizing the attacks in IoT with the knowledge of flow features and statistical features. To make the detection and classification more accurate, the weight of NN will be optimally tuned by a new Combined Whale SeaLion Algorithm (CWSA) that hybridizes the concepts of both SLnO and WOA. At last, the performance of the adopted method is computed over other traditional models in terms of accuracy, sensitivity, specificity, precision, FPR, FDR, FNR, NPV, F1-score, and MCC.

scholarly journals Traffic flow features as metrics (TFFM): detection of application layer level DDOS attack scope of IOT traffic flows

2018 ◽  
Vol 7 (2.7) ◽  
pp. 203 ◽  
Author(s):  
Kalathiripi Rambabu ◽  
N Venkatram
Keyword(s):  
Traffic Flow ◽  
Traffic Flows ◽  
Application Layer ◽  
Continuous Growth ◽  
Loosely Coupled ◽  
Ddos Attack ◽  
Proposed Model ◽  
Flow Features ◽  
Target Network ◽  
Iot Devices

The phenomenal and continuous growth of diversified IOT (Internet of Things) dependent networks has open for security and connectivity challenges. This is due to the nature of IOT devices, loosely coupled behavior of internetworking, and heterogenic structure of the networks.  These factors are highly vulnerable to traffic flow based DDOS (distributed-denial of services) attacks. The botnets such as “mirae” noticed in recent past exploits the IoT devises and tune them to flood the traffic flow such that the target network exhaust to response to benevolent requests. Hence the contribution of this manuscript proposed a novel learning-based model that learns from the traffic flow features defined to distinguish the DDOS attack prone traffic flows and benevolent traffic flows. The performance analysis was done empirically by using the synthesized traffic flows that are high in volume and source of attacks. The values obtained for statistical metrics are evincing the significance and robustness of the proposed model

scholarly journals Availability of an RFID Object-Identification System in IoT Environments

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6220
Author(s):  
Cosmina Corches ◽  
Mihai Daraban ◽  
Liviu Miclea
Keyword(s):  
Radio Frequency Identification ◽  
Mobile Network ◽  
Object Identification ◽  
Stochastic Petri Nets ◽  
Identification System ◽  
Frequency Identification ◽  
Iot Devices ◽  
Edge Layer ◽  
Mobile Network Operators ◽  
The Internet Of Things

Through the latest technological and conceptual developments, the centralized cloud-computing approach has moved to structures such as edge, fog, and the Internet of Things (IoT), approaching end users. As mobile network operators (MNOs) implement the new 5G standards, enterprise computing function shifts to the edge. In parallel to interconnection topics, there is the issue of global impact over the environment. The idea is to develop IoT devices to eliminate the greenhouse effect of current applications. Radio-frequency identification (RFID) is the technology that has this potential, and it can be used in applications ranging from identifying a person to granting access in a building. Past studies have focused on how to improve RFID communication or to achieve maximal throughput. However, for many applications, system latency and availability are critical aspects. This paper examines, through stochastic Petri nets (SPNs), the availability, dependability, and latency of an object-identification system that uses RFID tags. Through the performed analysis, the optimal balance between latency and throughput was identified. Analyzing multiple communication scenarios revealed the availability of such a system when deployed at the edge layer.

Religion, Caste, and Displacement: The Matua Community

2020 ◽  
Author(s):  
Carola Erika Lorea
Keyword(s):  
West Bengal ◽  
Religious Community ◽  
Local Network ◽  
Hindu Nationalism ◽  
Large Area ◽  
Political Subject ◽  
The Social ◽  
History Of ◽  
Lower Castes ◽  
The 19Th Century

The struggle against untouchability, the religious history of Bengal, and the study of postcolonial displacement in South Asia can hardly be considered without paying attention to a roughly two-hundred-year-old low-caste religious and social movement called Matua. The Matua community counts at present fifty million followers, according to its leaders. It is scattered across a large area and connected through a trans-local network of preachers, pilgrims, institutions, print, and religious commodities. Most Matua followers are found in West Bengal; in southern Bangladesh, where the movement emerged in the 19th century; and in provinces where refugees from East Bengal have resettled since the 1950s, especially Assam; Tripura; the Andaman Islands; Uttarakhand; and the Dandakaranya area at the border of Orissa, Chhattisgarh, and Madhya Pradesh. Building upon an older Vaishnava devotional stream, the religious community initiated by Harichand Thakur (1812–1878) and consolidated by his son Guruchand Thakur (1847–1937) developed hand in hand with the Namashudra movement for the social upliftment of the lower castes. Rebelling against social marginalization and untouchability, and promising salvation through ecstatic singing and dancing, the Matua community triggered a massive mobilization in rural East Bengal. Partition and displacement have disrupted the unity of the Matua movement, now scattered on both sides of the hastily drawn Indo-Bangladesh border. The institutional side of the Matua community emerged as a powerful political subject, deeply entangled with refugee politics, borderland issues, and Hindu nationalism. In the 21st century, the Matua community represents a key element in electoral politics and a crucial factor for understanding the relation between religion, displacement, and caste, within and beyond Bengal.

ИДЕНТИФИКАЦИЯ УСТРОЙСТВ И СИСТЕМ УЗКОПОЛОСНЫХ БЕСПРОВОДНЫХ СЕТЕЙ СВЯЗИ ИНТЕРНЕТА ВЕЩЕЙ. ЧАСТЬ II

2020 ◽  
Author(s):  
A.S. BORODIN ◽  
R.V. KIRICHEK ◽  
D.D. SAZONOV ◽  
 M.A. ROZHKOV ◽  
 A.V. KOLESNIKOV ◽  
...  
Keyword(s):  
Simulation Model ◽  
Identification System ◽  
Digital Object ◽  
Device Identification ◽  
Transport Industry ◽  
Iot Devices

A description of an identification system for IoT devices based on the Digital Object Architecture (DOA) is given. An analysis of alternative identifiers is given and the advantages of DOA for both identification and anti-counterfeit purposes are shown. The second part of the article presents the implementation of DOA technology on a specific example - the device identification system in the Russian transport industry. It is also developed a simulation model of a network fragment. A series of optimization experiments are performed. Представлено описание системы идентификации на базе архитектуры цифровых объектов (Digital Object Architecture - DOA), которая в настоящее время рассматривается в качестве приоритетной для идентификации устройств и приложений интернета вещей. Приведен анализ альтернативных идентификаторов и показаны преимущества DOA какдля за -дач идентификации, так и для борьбы с контрафактом. Во второй части статьи представлена реализация данной технологии на конкретном примере - системе идентификации устройств в транспортной отрасли России, а также разработана имитационная модель фрагмента сети, на базе которой анализировались различные параметры функционирования системы. Дано базовое описание разработанной имитационной модели и проведена серия оптимизационных экспериментов с целью улучшения производительности текущей системы.

Energy-Efficient Computing Solutions for Internet of Things with ZigBee Reconfigurable Devices

2017 ◽  
pp. 441-459
Author(s):  
Grzegorz Chmaj ◽  
Henry Selvaraj
Keyword(s):  
Internet Of Things ◽  
High Efficiency ◽  
Local Network ◽  
Energy Efficient Computing ◽  
Distributed Structure ◽  
Reconfigurable Devices ◽  
Battery Power ◽  
Iot Devices ◽  
Logic Structures ◽  
High Level

Nowadays we are witnessing a trend with significantly increasing number of networked and computing-capable devices being integrated into everyday environment. This trend is expected to continue. With computing devices available as logic structures, they might use each other's processing capabilities to achieve a given goal. In this paper, the authors propose an architectural solution to perform the processing of tasks using a distributed structure of Internet of Things devices. They also include ZigBee devices that are not connected to the Internet, but participate with the processing swarm using local network. This significantly extends the flexibility and potential of the IoT structure, while being still not a well-researched area. Unlike many high-level realizations for IoT processing, the authors present a realization operating on the communications, computing and near protocol level that achieves energy consumption efficiency. They also include the reconfigurability of IoT devices. The authors' work is suitable to be the base for higher-level realizations, especially for systems with devices operating on battery power. At the same time, the architecture presented in this paper uses minimal centralization, moving maximum responsibilities to regular devices. The proposed realizations are described using linear programming models and their high efficiency is evaluated.

scholarly journals Towards Improved Random Forest based Feature Selection for Intrusion Detection in Smart IOT Environment

2019 ◽  
Vol 8 (11) ◽  
pp. 749-757
Keyword(s):  
Feature Selection ◽  
Random Forest ◽  
Intrusion Detection ◽  
Performance Metrics ◽  
Detection System ◽  
Denial Of Service ◽  
Learning Approaches ◽  
Iot Devices ◽  
Sensitivity Specificity ◽  
Research Challenge

Internet of Things (IoT) is raised as most adaptive technologies for the end users in past few years. Indeed of being popular, security in IoT turned out to be a crucial research challenge and a sensible topic which is discussed very often. Denial of Service (DoS) attack is encountered in IoT sensor networks by perpetrators with numerous compromised nodes to flood certain targeted IoT device and thus resulting in vulnerability or service unavailability. Features that are encountered from the malicious node can be utilized effectually to recognize recurring patterns or attack signature of network based or host based attacks. Henceforth, feature extraction using machine learning approaches for modelling of Intrusion detection system (IDS) have been cast off for identification of threats in IoT devices. In this investigation, Kaggle dataset is measured as benchmark dataset for detecting intrusion is considered initially. These dataset includes 41 essential attributes for intrusion identification. Next, selection of features for classifiers is done with an improved Weighted Random Forest Information extraction (IW-RFI). This proposed WRFI approach evaluates the mutual information amongst the attributes of features and select the optimal features for further computation. This work primarily concentrates on feature selection as effectual feature selection leads to effectual classification. Finally, performance metrics like accuracy, sensitivity, specificity is computed for determining enhanced feature selection. The anticipated model is simulated in MATLAB environment, which outperforms than the existing approaches. This model shows better trade off in contrary to prevailing approaches in terms of accurate detection of threats in IoT devices and offers better transmission over those networks.

scholarly journals Synergy between CALIOP and MODIS instruments for aerosol monitoring: application to the Po Valley

2010 ◽  
Vol 3 (2) ◽  
pp. 1323-1359 ◽  
Author(s):  
P. Royer ◽  
J.-C. Raut ◽  
G. Ajello ◽  
S. Berthier ◽  
P. Chazette
Keyword(s):  
Signal To Noise Ratio ◽  
Extinction Ratio ◽  
Orthogonal Polarization ◽  
Aerosol Extinction ◽  
Large Area ◽  
Total Uncertainty ◽  
Aerosol Model ◽  
Po Valley ◽  
The Mean ◽  
Level 2

Abstract. We propose here a synergy between Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations/Cloud-Aerosol LIdar with Orthogonal Polarization (CALIPSO/CALIOP) and Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua and Terra in order to retrieve aerosol optical properties over the Po Valley from June 2006 to February 2009. Such an approach gives simultaneously access to the aerosol extinction vertical profile and to the equivalent backscatter-to-extinction ratio at 532 nm (BER, inverse of the lidar ratio). The choice of the Po valley has been driven by the great occurrences of pollutant events leading to a mean MODIS-derived aerosol optical thickness of 0.27(±0.17) at 550 nm over a large area of ~120 000 km2. In such area, a significant number of CALIOP level-1 vertical profiles can be averaged (~200 individual laser shots) leading to a signal-to-noise ratio greater than 10 in the planetary boundary layer (PBL) sufficient to perform a homemade inversion of the mean lidar profiles. The mean BER (together with the associated variabilities) over the Po Valley retrieved from the coupling between CALIOP/MODIS-Aqua and CALIOP/MODIS-Terra are ~0.014(±0.003) sr−1 and ~0.013(±0.004) sr−1, respectively. The total uncertainty on BER retrieval has been assessed to be ~0.003 sr−1 using a Monte Carlo approach. These mean BER values retrieved have been compared with those given by the level-2 operational products of CALIOP ~0.016(±0.003) sr−1. The values we assessed appear close to what is expected above urban area. A seasonal cycle has been observed with higher BER values in spring, summer and fall, which can be associated to dust event occurring during this period. In most of cases, the mean aerosol extinction coefficient in the PBL diverges significantly between the level-2 operational products and the result of our own inversion procedure. Indeed, mean differences of 0.10 km−1 (~50%) and 0.13 km−1 (~60%) have been calculated using MODIS-Aqua/CALIOP and MODIS-Terra/CALIOP synergies, respectively. Such differences may be due to the identification of the aerosol model by the operational algorithm and thus to the choice of the BER.

Statistical Features-Based Diagnosis of Alzheimer's Disease using MRI

2014 ◽  
pp. 38-53 ◽  
Author(s):  
Namita Aggarwal ◽  
Bharti Rana ◽  
R.K. Agrawal
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Brain Slices ◽  
Statistical Features ◽  
Extraction Techniques ◽  
Brain Images ◽  
Appropriate Treatment ◽  
Sensitivity Specificity

Early detection of Alzheimer's Disease (AD), a neurological disorder, may help in development of appropriate treatment to slow down the disease's progression. In this chapter, a method is proposed that may assist in diagnosis of AD using T1 weighted MRI brain images. In the proposed method, first-and-second-order-statistical features were extracted from multiple trans-axial brain slices covering hippocampus and amygdala regions, which play a significant role in AD diagnosis. Performance of the proposed approach is compared with the state-of-the-art feature extraction techniques in terms of sensitivity, specificity, and accuracy. The experiment was carried out on two datasets built from publicly available OASIS data, with four well-known classifiers. Experimental results show that the proposed method outperforms all the other existing feature extraction techniques irrespective of the choice of classifier and dataset. In addition, the statistical test demonstrates that the proposed method is significantly better in comparison to the existing methods. The authors believe that this study will assist clinicians/researchers in classification of AD patients from controls based on T1-weighted MRI.

scholarly journals A Two-Level Flow-Based Anomalous Activity Detection System for IoT Networks

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 530 ◽  
Author(s):  
Imtiaz Ullah ◽  
Qusay H. Mahmoud
Keyword(s):  
Network Flow ◽  
Detection System ◽  
Activity Detection ◽  
Malicious Activity ◽  
Proposed Model ◽  
Tree Classifier ◽  
Level 1 ◽  
Iot Devices ◽  
The Impact ◽  
Level 2

The significant increase of the Internet of Things (IoT) devices in smart homes and other smart infrastructure, and the recent attacks on these IoT devices, are motivating factors to secure and protect IoT networks. The primary security challenge to develop a methodology to identify a malicious activity correctly and mitigate the impact of such activity promptly. In this paper, we propose a two-level anomalous activity detection model for intrusion detection system in IoT networks. The level-1 model categorizes the network flow as normal flow or abnormal flow, while the level-2 model classifies the category or subcategory of detected malicious activity. When the network flow classified as an anomaly by the level-1 model, then the level-1 model forwards the stream to the level-2 model for further investigation to find the category or subcategory of the detected anomaly. Our proposed model constructed on flow-based features of the IoT network. Flow-based detection methodologies only inspect packet headers to classify the network traffic. Flow-based features extracted from the IoT Botnet dataset and various machine learning algorithms were investigated and tested via different cross-fold validation tests to select the best algorithm. The decision tree classifier yielded the highest predictive results for level-1, and the random forest classifier produced the highest predictive results for level-2. Our proposed model Accuracy, Precision, Recall, and F score for level-1 were measured as 99.99% and 99.90% for level-2. A two-level anomalous activity detection system for IoT networks we proposed will provide a robust framework for the development of malicious activity detection system for IoT networks. It would be of interest to researchers in academia and industry.

A Matrix-Based Pair-Wise Key Establishment for Secure and Energy Efficient WSN-Assisted IoT

2019 ◽  
Vol 13 (3) ◽  
pp. 91-105 ◽  
Author(s):  
Anurag Shukla Shukla ◽  
Sarsij Tripathi
Keyword(s):  
Energy Efficient ◽  
Secure Communication ◽  
Symmetric Matrix ◽  
Routing Algorithm ◽  
Physical World ◽  
Secure Routing ◽  
Large Area ◽  
Hierarchical Topology ◽  
Wireless Medium ◽  
Efficient Scheme

The Internet of Things (IoT) is getting the reputation as one of the most optimistic networking paradigms that is reducing the gap between the cyber world and physical world. Most of the participating nodes in IoT network are sensors, which are limited in terms of resource such as energy, computation power, memory and so on. In IoT network, nodes communicate with each other via wireless medium, which makes the IoT network vulnerable to many security threats including eavesdropping. The IoT network is deployed in a large area and work on 24/7 hours, so an energy efficient scheme is one of the major issues in IoT. To achieve secure and energy efficient network, this article contributes: (1) A hierarchical topology for IoT network deployment; (2) symmetric matrix-based pair-wise key generation for secure communication; (3) A secure and energy efficient secure routing algorithm for the proposed model.

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