BlockSecIoTNet: Blockchain-based decentralized security architecture for IoT network

Rathore, Shailendra,Wook Kwon, Byung,Park, Jong Hyuk Academic Press 2019 Journal of network and computer applications Vol.143 No.-

<P><B>Abstract</B></P> <P>The exponential growth of the use of insecure stationary and portable devices in the Internet of Things (IoT) network of the smart city has made the security of the smart city against cyber-attacks a vital issue. Various mechanisms for detecting security attacks that rely on centralized and distributed architectures have already been proposed, but they tend to be inefficient due to such problems as storage constraints, the high cost of computation, high latency, and a single point of failure. Moreover, existing security mechanisms are faced with the issue of monitoring and collecting historic data throughout the entire IoT network of the smart city in order to deliver optimal security and defense against cyberattacks. To address the current challenges, this paper proposes a decentralized security architecture based on Software Defined Networking (SDN) coupled with a blockchain technology for IoT network in the smart city that relies on the three core technologies of SDN, Blockchain, and Fog and mobile edge computing in order to detect attacks in the IoT network more effectively. Thus, in the proposed architecture, SDN is liable to continuous monitoring and analysis of traffic data in the entire IoT network in order to provide an optimal attack detection model; Blockchain delivers decentralized attack detection to mitigate the “single point of failure” problem inherent to the existing architecture; and Fog and mobile edge computing supports attack detection at the fog node and, subsequently, attack mitigation at the edge node, thus enabling early detection and mitigation with lesser storage constraints, cheaper computation, and low latency. To validate the performance of the proposed architecture, it was subjected to an experimental evaluation, the results of which show that it outperforms both centralized and distributed architectures in terms of accuracy and detection time.</P>

Real-time video processing for traffic control in smart city using Hadoop ecosystem with GPUs

Rathore, M. M.,Son, H.,Ahmad, A.,Paul, A. Springer Science + Business Media 2018 Soft computing Vol.22 No.5

<P>Advancement in technology causes the rise in smart systems. City authorities want to make their cities smarter by making an intelligent decision at real time without the involvement of humans. Monitoring and controlling city traffic is one of the major challenges faced by the authorities. These days, city traffic is monitored by static network cameras deployed on few places of highways. Most of the vehicles are also equipped with cameras to store the videos as a black box. However, monitoring and controlling city traffic by using these thousands of cameras produce an overwhelming amount of high-speed videos, which is challenging to process at real time. Therefore, in this paper, we proposed a system to control city traffic by identifying illegal traffic behaviour, such as illegal U-turn, through continuous monitoring of city traffic. The continuous city traffic is monitored by the network static cameras placed on the road as well as by all the vehicles' cameras. An architecture is proposed to handle high-speed vast volume of real-time videos efficiently. For that, the two-level parallelism is achieved with the combination of Hadoop and graphics processing unit (GPU) while processing each frame using parallel environment of Hadoop and each block of a frame using GPU. MapReduce Hadoop programming paradigm is not suitable for real-time processing. Thus, we proposed a parameter calculation algorithm that is equivalent to MapReduce mechanism for image processing while dividing the images/frames into fixed-size blocks. We analyzed the city road traffic, which is collected by static cameras placed on various roads and also by vehicles' cameras while running on the road. Later, the illegal traffic behaviour are recognized, e.g. illegal U-turn, drunken drive, zig-zag drive, over-speed, etc. Finally, the efficiency of the designed system and algorithms are tested by considering the overall running time and system's throughput with respect to video duration as well as the number of frames. The findings indicate that the proposed architecture with GPU-based algorithm over the Hadoop system perform extraordinary.</P>

Is the Tumor Infiltrating Natural Killer Cell (NK-TILs) Count in Infiltrating Ductal Carcinoma of Breast Prognostically Significant?

Rathore, Ankita Singh,Goel, Madhu Mati,Makker, Annu,Kumar, Sandeep,Srivastava, Anand Narain Asian Pacific Journal of Cancer Prevention 2014 Asian Pacific journal of cancer prevention Vol.15 No.8

Purpose: The aim of this study was to investigate the prognostic significance of the CD56+NK-TIL count in infiltrating ductal carcinoma (IDC) of breast. Material and Methods: Immunohistochemistry (IHC) was performed using antibodies specific for CD56 on formalin-fixed and paraffin-embedded tissue sections of 175 infiltrating ductal carcinomas (IDC) of breast. Distribution of intratumoral and stromal CD56+NK-TILs was assessed semi-quantitatively. Results: A low intratumoral CD56+count showed significant and inverse associations with tumor grade, stage, and lymph node status, whereas it had significant and direct association with response to treatment indicating good prognosis. These patients had better survival (${\chi}^2$=4.80, p<0.05) and 0.52 fold lower death rate (HR=0.52, 95% CI=0.28-0.93) as compared to patients with high CD56+ intratumoral count. The association of survival was insignificant with low CD56 stromal count as compared to high CD56 stromal count (${\chi}^2$=1.60, p>0.05). Conclusion: To conclude, although NK-TIL count appeared as a significant predictor of prognosis, it alone may not be sufficient for predicting the outcome considering the fact that there exists a crosstalk between NK-TILs and the other immune infiltrating TILs.

Real-time Medical Emergency Response System: Exploiting IoT and Big Data for Public Health

Rathore, M. Mazhar,Ahmad, Awais,Paul, Anand,Wan, Jiafu,Zhang, Daqiang Springer-Verlag 2016 Journal of Medical Systems Vol.40 No.12

Social network security: Issues, challenges, threats, and solutions

Rathore, S.,Sharma, P.K.,Loia, V.,Jeong, Y.S.,Park, J.H. Elsevier science 2017 Information sciences Vol.421 No.-

<P>Social networks are very popular in today's world. Millions of people use various forms of social networks as they allow individuals to connect with friends and family, and share private information. However, issues related to maintaining the privacy and security of a user's information can occur, especially when the user's uploaded content is multimedia, such as photos, videos, and audios. Uploaded multimedia content carries information that can be transmitted virally and almost instantaneously within a social networking site and beyond. In this paper, we present a comprehensive survey of different security and privacy threats that target every user of social networking sites. In addition, we separately focus on various threats that arise due to the sharing of multimedia content within a social networking site. We also discuss current state-of- the-art defense solutions that can protect social network users from these threats. We then present future direction and discuss some easy-to-apply response techniques to achieve the goal of a trustworthy and secure social network ecosystem. (C) 2017 Elsevier Inc. All rights reserved.</P>

ON BIRECURRENT KÄHLER AND ALMOST TACHIBANA MANIFOLDS

Rathore, Mahendra Pal Singh Department of Mathematics 1974 Kyungpook mathematical journal Vol.14 No.2

Real-time secure communication for Smart City in high-speed Big Data environment

Rathore, M. Mazhar,Paul, Anand,Ahmad, Awais,Chilamkurti, Naveen,Hong, Won-Hwa,Seo, HyunCheol Elsevier 2018 Future generation computer systems Vol.83 No.-

<P><B>Abstract</B></P> <P>The recent development in the technology brings the concept of Smart City that is achieved through real-time city related intelligent decisions by analyzing the data harvested from various smart systems in the city using millions of sensors and devices connected over the Internet, termed as Internet of Things (IoT). These devices generate the overwhelming volume of high-speed streaming data, termed as Big Data. However, the generation of city data at a remote location and then transmitting it to central city servers for analysis purpose raises the concerns of security and privacy. On the other hand, providing security to such Big Data streaming requires a high-speed security system that can work in a real-time environment without providing any delay that may slow down the overall performance of the Smart City System. To overthrown these challenges, in this paper, we proposed an efficient and real-time Smart City security system by providing strong intrusion detection at intelligent city building (ICB) and also a security protocol to protect the communication between the remote smart system(RSS)/User and the city analysis building, i.e., ICB. The proposed communication security protocol consists of various phases, i.e., registration phase, session key exchange phase, session key revocation phase, and data transmission phases from RSS to ICB as well as from User to ICB. Vast security analyses are performed to evaluate the credibility of the system. The proposed system is also evaluated on efficiency in terms of computation cost and throughput of overall functions used in the system. The system’s evaluation and the comparative study with existing system show that the prosed system is secure, more efficient, and able to work in a real-time, high-speed Smart City environment.</P> <P><B>Highlights</B></P> <P> <UL> <LI> This paper presents a system architecture that integrate Smart City with technical network. </LI> <LI> A Novel notion of Smart-City Network is defined by Communication Security. </LI> <LI> Intelligent Smart Building Architecture with Remote Smart System. </LI> <LI> The system is also implemented of Smart City Decision are done on top of Hadoop parallel nodes. </LI> </UL> </P>

A Two-Photon Probe for Near-Membrane Zinc Ions

Rathore, Kailash,Lim, Chang Su,Lee, Young,Park, Hee Jung,Cho, Bong Rae Wiley Blackwell (John Wiley Sons) 2014 ASIAN JOURNAL OF ORGANIC CHEMISTRY Vol.3 No.10

Exploiting IoT and big data analytics: Defining Smart Digital City using real-time urban data

Rathore, M. Mazhar,Paul, Anand,Hong, Won-Hwa,Seo, HyunCheol,Awan, Imtiaz,Saeed, Sharjil Elsevier 2018 Sustainable cities and society Vol.40 No.-

<P><B>Abstract</B></P> <P>Integration of all smart systems (such as smart home, smart parking, etc.) and the IoT devices (such as sensors, actuators, and smartphones) in the city can play a vital role to develop the urban services by building their city digital and smarter. However, interconnection of lots of IoT objects to collect urban data over the Internet to launch a smart digital city, effects vast volume of data generation, termed as Big Data. Thus, it is a challenging task to integrate IoT devices and smart systems in order to harvest and process such big amount of real-time city data in an effective manner aimed at creating a Smart Digital City. Therefore, in this paper, we have established an IoT-based Smart City by using Big Data analytics while harvesting real-time data from the city. We used sensors’ deployment including sensors at smart home, smart parking, vehicular networking, surveillance, weather and water monitoring system, etc., for real time data collection. The complete system is described by its proposed architecture and implementation prototype using Hadoop ecosystem in a real environment. In addition, the Smart Digital City services are extended by developing the intelligent Smart Transportation System by means of big graph processing to facilitate citizens while providing real-time traffic information and alerts. The proposed system consists of number of stages including data generation and collection, aggregation, filtration, classification, preprocessing, computing, and decision making. The efficiency of the system is extended by applying Big Data processing using Apache Spark over Hadoop. Whereas, the big city graph processing is achieved by using Giraph over Hadoop. The system is practically implemented by taken existing smart systems and IoT devices as city data sources to develop the Smart Digital City. The proposed system is evaluated with respect to efficiency in terms of scalability and real-time data processing.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Data Generation, Collection, Aggregation, Filtration, Classification, Preprocessing Computing and Decision Making. </LI> <LI> Impementation of Smart IoT based Digital City using Real-Time Urban Data. </LI> <LI> Big Data Analytics for City Planning using Hadoop Ecosystem. </LI> <LI> Big Graph Processing for Traffic Information and Alert. </LI> <LI> System is Evaluated for its Scalability and Real-time Data Processing. </LI> </UL> </P>

XSSClassifier: An Efficient XSS Attack Detection Approach Based on Machine Learning Classifier on SNSs

Rathore, Shailendra,Sharma, Pradip Kumar,Park, Jong Hyuk Korea Information Processing Society 2017 Journal of information processing systems Vol.13 No.4

Social networking services (SNSs) such as Twitter, MySpace, and Facebook have become progressively significant with its billions of users. Still, alongside this increase is an increase in security threats such as cross-site scripting (XSS) threat. Recently, a few approaches have been proposed to detect an XSS attack on SNSs. Due to the certain recent features of SNSs webpages such as JavaScript and AJAX, however, the existing approaches are not efficient in combating XSS attack on SNSs. In this paper, we propose a machine learning-based approach to detecting XSS attack on SNSs. In our approach, the detection of XSS attack is performed based on three features: URLs, webpage, and SNSs. A dataset is prepared by collecting 1,000 SNSs webpages and extracting the features from these webpages. Ten different machine learning classifiers are used on a prepared dataset to classify webpages into two categories: XSS or non-XSS. To validate the efficiency of the proposed approach, we evaluated and compared it with other existing approaches. The evaluation results show that our approach attains better performance in the SNS environment, recording the highest accuracy of 0.972 and lowest false positive rate of 0.87.