Pradip Kumar Sharma

A Software Defined Fog Node Based Distributed Blockchain Cloud Architecture for IoT

The recent expansion of the Internet of Things (IoT) and the consequent explosion in the volume of data produced by smart devices have led to the outsourcing of data to designated data centers. However, to manage these huge data stores, centralized data centers, such as cloud storage cannot afford auspicious way. There are many challenges that must be addressed in the traditional network architecture due to the rapid growth in the diversity and number of devices connected to the internet, which is not designed to provide high availability, real-time data delivery, scalability, security, resilience, and low latency. To address these issues, this paper proposes a novel blockchain-based distributed cloud architecture with a software defined networking (SDN) enable controller fog nodes at the edge of the network to meet the required design principles. The proposed model is a distributed cloud architecture based on blockchain technology, which provides low-cost, secure, and on-demand access to the most competitive computing infrastructures in an IoT network. By creating a distributed cloud infrastructure, the proposed model enables cost-effective high-performance computing. Furthermore, to bring computing resources to the edge of the IoT network and allow low latency access to large amounts of data in a secure manner, we provide a secure distributed fog node architecture that uses SDN and blockchain techniques. Fog nodes are distributed fog computing entities that allow the deployment of fog services, and are formed by multiple computing resources at the edge of the IoT network. We evaluated the performance of our proposed architecture and compared it with the existing models using various performance measures. The results of our evaluation show that performance is improved by reducing the induced delay, reducing the response time, increasing throughput, and the ability to detect real-time attacks in the IoT network with low performance overheads.

DistBlockNet: A Distributed Blockchains-Based Secure SDN Architecture for IoT Networks

The rapid increase in the number and diversity of smart devices connected to the Internet has raised the issues of flexibility, efficiency, availability, security, and scalability within the current IoT network. These issues are caused by key mechanisms being distributed to the IoT network on a large scale, which is why a distributed secure SDN architecture for IoT using the blockchain technique (DistBlockNet) is proposed in this research. It follows the principles required for designing a secure, scalable, and efficient network architecture. The DistBlockNet model of IoT architecture combines the advantages of two emerging technologies: SDN and blockchains technology. In a verifiable manner, blockchains allow us to have a distributed peer-to-peer network where non-confident members can interact with each other without a trusted intermediary. A new scheme for updating a flow rule table using a blockchains technique is proposed to securely verify a version of the flow rule table, validate the flow rule table, and download the latest flow rules table for the IoT forwarding devices. In our proposed architecture, security must automatically adapt to the threat landscape, without administrator needs to review and apply thousands of recommendations and opinions manually. We have evaluated the performance of our proposed model architecture and compared it to the existing model with respect to various metrics. The results of our evaluation show that DistBlockNet is capable of detecting attacks in the IoT network in real time with low performance overheads and satisfying the design principles required for the future IoT network.

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

Abstract Social networks are very popular in todays 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 users information can occur, especially when the users 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.

A comprehensive study on APT attacks and countermeasures for future networks and communications: challenges and solutions

Recently in the connected digital world, targeted attack has become one of the most serious threats to conventional computing systems. Advanced persistent threat (APT) is currently one of the most important threats considering the information security concept. APT persistently collects data from a specific target by exploiting vulnerabilities using diverse attack techniques. Many researchers have contributed to find approaches and solutions to fight against network intrusion and malicious software. However, only a few of these solutions are particularly focused on APT. In this paper, we introduce a structured study on semantic-aware work to find potential contributions that analyze and detect APT in details. We propose modeling phase that discusses the typical steps in APT attacks to collect the desired information by attackers. Our research explores social network and web infrastructure exploitation as well as communication protocols and much more for future networks and communications. The paper also includes some recent Zero-day attacks, use case scenarios and cyber trends in southeastern countries. To overcome these challenges and attacks, we introduce a detailed comprehensive literature evaluation scheme that classifies and provides countermeasures of APT attack behavior. Furthermore, we discuss future research direction of APT defense framework of next-generation threat life cycle.

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

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 crosssite 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 learningbased 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.

DFA-AD: a distributed framework architecture for the detection of advanced persistent threats

Advanced persistent threats (APTs) are target-oriented and advanced cyber-attacks which often leverage the bot control and customized malware techniques in order to control and remotely access valuable information. APTs generally use various attack techniques to gain access to the unauthorized system and then progressively spread throughout the network. The prime objectives of APT attacks are to steal intellectual property, legal documents, sensitive internal business and other data. If an attack is successfully launched on a system, the timely detection of attack is extremely important to stop APTs from further spreading and for mitigating its impact. On the other hand, internet of things (IoT) devices quickly become ubiquitous while IoT services become pervasive. Their prosperity has not gone unnoticed, and the number of attacks and threats against IoT devices and services are also increasing. Cyber-attacks are not new to IoT, but as the IoT will be deeply intertwined in our societies and lives, it becomes essential to take cyber defense seriously. In this paper, we propose a novel distributed framework architecture for the detection of APTs named as distributed framework architecture for APTs detection (DFA-AD), which is a promising basis for modern intrusion detection systems. In contrast to other approaches, the DFA-AD technique for detecting APT attack is based on multiple parallel classifiers, which classify the events in a distributed environment and event correlation among those events. Each classifier method is focused on detecting the APT’s attack technique independently. The evaluation results show that the proposed approach achieves greater effectiveness and accuracy.

Blockchain based hybrid network architecture for the smart city

Abstract Recently, the concept of “Smart Cities” has developed considerably with the rise and development of the Internet of Things as new form of sustainable development. Smart cities are based on autonomous and distributed infrastructure that includes intelligent information processing and control systems heterogeneous network infrastructure, and ubiquitous sensing involving millions of information sources Due to the continued growth of data volume and number of connected IoT devices, however, issues such as high latency, bandwidth bottlenecks, security and privacy, and scalability arise in the current smart city network architecture. Designing an efficient, secure, and scalable distributed architecture by bringing computational and storage resources closer to endpoints is needed to address the limitations of today’s smart city network. In this paper, we propose a novel hybrid network architecture for the smart city by leveraging the strength of emerging Software Defined Networking and blockchain technologies. To achieve efficiency and address the current limitations, our architecture is divided into two parts: core network and edge network. Through the design of a hybrid architecture, our proposed architecture inherits the strength of both centralized and distributed network architectures. We also propose a Proof-of-Work scheme in our model to ensure security and privacy. To evaluate the feasibility and performance of our proposed model, we simulate our model and evaluate it based on various performance metrics. The result of the evaluation shows the effectiveness of our proposed model.

OpCloudSec: Open cloud software defined wireless network security for the Internet of Things

Abstract Cutting-edge cloud frameworks will require a paradigm shift in regards to how they are built and managed. Traditional management and control platforms face significant challenges in terms of security, reliability, and flexibility that these cutting-edge frameworks must deal with. On the other hand, Distributed Denial of Service (DDoS) attacks have become a weapon of choice for cyber-terrorists, cyber-extortionists, and hackers. Recently, the simplicity of programmability in Software-Defined Networking (SDN) makes it a good platform for the implementation of various initiatives that includes decentralized network management, dynamic topology changes, and application deployment in a multi-tenant data center environment. Motivated by the capabilities of SDN, we are proposing a mitigation architecture for security attacks that incorporates a highly programmable monitoring network so as to make it possible to identify attacks. It has a flexible control structure to quickly define the reaction of attacks and particular side, and we show how SDN can be used as a key application in the cloud IoT. We evaluated the performance of our proposed architecture and compared it with the existing models to obtain various performance measures. The results of our evaluation show that our OpCloudSec architecture model can efficiently and effectively meet the security challenges created by the new network paradigm.

EH-HL: Effective Communication Model by Integrated EH-WSN and Hybrid LiFi/WiFi for IoT

Technological advances over the last decade in the field of wireless communications have resulted in the improvement of small and low cost sensor nodes outfitted with wireless communication abilities capable of establishing wireless sensor network (WSN). Due to the expansion of Internet of Things (IoT), there are many areas in IoT application where WSN applications are found. These applications generally impose severe constraints on the lifetime of the WSN, which is expected to last several years. It is necessary to diminish the overall energy consumption of the sensor node and to find an additional source of energy for achieving this objective. On the other hand, due to the imminent crisis of the radio frequency spectrum, light fidelity (LiFi) offers many key benefits and effective solutions for these issues that have been postured in the most recent decade. In this paper, we propose a novel EH-HL model for future smart homes and industries based on the integration of energy harvesting WSN (EH-WSN) and hybrid LiFi/WiFi communication techniques. The proposed model is capable of efficiently transmitting data at high speed for bidirectional multidevice and by harvesting energy, we provide the power to the sensor nodes. To synchronize multidevice transmissions, transmit data and provide low-cost wireless communication, we used the color beams of the red, green, and blue LEDs. The result of the evaluation shows that the hybrid communication scheme is proposed in the EH-HL model. It also offers superior performance and achieves a data rate of 25 Mb/s for multiaccess/multiusers.

Novel assessment method for accessing private data in social network security services

Social network services (SNSs) have become one of the core Internet-based application services in recent years. Through SNSs, diverse kinds of private data are shared with users’ friends and SNS plug-in applications. However, these data can be exposed via abnormal private data access. For example, the addition of fake friends to a user’s account is one approach to gain access to a private user’s data. Private user data can be protected from being accessed by using an automated method to assess information. This paper proposes a method that evaluates private data accesses for social network security. By defining normal private data access patterns in advance, abnormal private data access patterns can be exposed. Normal private data access patterns are generated by analyzing all of the consecutive private data accesses of users based on Bayesian probability. We have proven the effectiveness of our approach by conducting experiments where the private data access signals of Twitter accounts were collected and analyzed.