Romil Rawat

Bio: Romil Rawat is an academic researcher from Shri Vaishnav Institute of Technology and Science. The author has contributed to research in topics: The Internet & Computer science. The author has an hindex of 6, co-authored 21 publications receiving 179 citations. Previous affiliations of Romil Rawat include Graphic Era University & University of Mumbai.

Intrusion detection system in cloud computing environment

Abstract: In recent years, with the growing popularity of cloud computing, security in cloud has become an important issue. As "Prevention is better than cure", detecting and blocking an attack is better than responding to an attack after a system has been compromised. This paper proposes architecture capable of detecting intrusions in a distributed cloud computing environment, and safeguarding it from possible security breaches. It deploys a separate instance of IDS for each user and uses a single controller to manage the instances. IDS in this architecture can use signature based as well as learning based method.

SQL injection attack Detection using SVM

Abstract: Web application has various input functions which are susceptible to SQL-Injection attack. SQL-Injection occurs by injecting suspicious code or data fragments in a web application.Personal information disclosure ,loss of authenticity, data theft and site fishing falls under this attack category. It is impossible to check original data code and suspicious data code using available algorithms and approaches because of inefficient and proper training techniques of dataset or design aspects. In this paper we will use SVM (Support Vector Machine) for classification and prediction of SQL-Injection attack. In our propose algorithm, SQL-Injection attack detection accuracy is (96.47% and which is the highest among the existing SQL-Injection detection Techniques.

Vulnerability Analysis at Industrial Internet of Things Platform on Dark Web Network Using Computational Intelligence

Abstract: Due to the potentially catastrophic effects in the event of an attack, security-enabled design and algorithms are required to protect automated applications and instruments based on Internet Industries of Thing called as IIoT. The most potential developed techniques for analyzing, designing, and protecting the Internet of Things (IoT) technologies are computational intelligence and big data analysis. These strategies will also help to enhance the protection of IIoT networks (home automation, traffic lighting, power stations, oil and gas stations, smart warehouses, automated vehicles, smart robotics). First, we present the popular IIoT computational intelligence (CIA) algorithm and its related vulnerabilities in this article. We then conduct a cyber-threat-vulnerability review by investigating the use of CIA model to combat illicit behaviors of dark Web environment. The proposed work is based on the literature data analysis within the available solutions for the prevention of cyber terrorism threats using algorithm models of computational intelligence (CIA) is then discussed. Finally, we address our work, which provides scenario of a real-world hidden cyber world activities designed to carry out a cyber terrorist attack and to build a structure for a cyber threat. Device attacks to illustrate how a CIA-based vulnerability analysis system will do well to detect these attacks. To have a rational point of view on the success of the approaches, we have measured the performance across representative metrics.

Suspicious Big Text Data Analysis for Prediction—On Darkweb User Activity Using Computational Intelligence Model

Abstract: In this paper, we work on suspicious big text data analysis technique for prediction of terrorism activity like financial fraud, money laundering, recruitment, radicalization, fundraising, violent and illegal post and video sharing on darkweb environment also called as cosmic web due to hidden content attributes. The consequent activity prognosis (CAP) is required for minimizing the risk associated with cyber information and personal security compromise for collectively data analysis referred as big data. The cyberterrorist and criminal hackers generated denial of service attack (DoS), distributed DoS attack (DDoS) and ransom-related DoS attack (RDoS) attack thereby overloading the server and increasing and blocking the server execution. The cyber threats and activities could only be reducing the execution time of activities marked suspicious and not safe. The propose model is based on computational intelligence technique using MapReduce technique, by classifying the malicious patterns found in big data sets collected from authentic channels and designed using machine learning supporting languages to implement the enhanced model and magnify the existing Intelligent techniques of computation with evaluated parameters. The work is highly adaptable for analysis and outline terrorist and criminal activities and would be beneficial for cyber police and security agencies.

Malware Threat Affecting Financial Organization Analysis Using Machine Learning Approach

Abstract: Since 2014, Emotet has been using Man-in-the-Browsers (MITB) attacks to target companies in the finance industry and their clients. Its key aim is to steal victims' online money-lending records and vital credentials as they go to their banks' websites. Without analyzing network packet payload computing (PPC), IP address labels, port number traces, or protocol knowledge, we have used Machine Learning (ML) modeling to detect Emotet malware infections and recognize Emotet related congestion flows in this work. To classify emotet associated flows and detect emotet infections, the output outcome values are compared by four separate popular ML algorithms: RF (Random Forest), MLP (Multi-Layer Perceptron), SMO (Sequential Minimal Optimization Technique), and the LRM (Logistic Regression Model). The suggested classifier is then improved by determining the right hyperparameter and attribute set range. Using network packet (computation) identifiers, the Random Forest classifier detects emotet-based flows with 99.9726 percent precision and a 92.3 percent true positive rating.

Security issues in cloud environments: a survey

Abstract: In the last few years, the appealing features of cloud computing have been fueling the integration of cloud environments in the industry, which has been consequently motivating the research on related technologies by both the industry and the academia. The possibility of paying-as-you-go mixed with an on-demand elastic operation is changing the enterprise computing model, shifting on-premises infrastructures to off-premises data centers, accessed over the Internet and managed by cloud hosting providers. Regardless of its advantages, the transition to this computing paradigm raises security concerns, which are the subject of several studies. Besides of the issues derived from Web technologies and the Internet, clouds introduce new issues that should be cleared out first in order to further allow the number of cloud deployments to increase. This paper surveys the works on cloud security issues, making a comprehensive review of the literature on the subject. It addresses several key topics, namely vulnerabilities, threats, and attacks, proposing a taxonomy for their classification. It also contains a thorough review of the main concepts concerning the security state of cloud environments and discusses several open research topics.

Security for 5G and Beyond

Abstract: The development of the fifth generation (5G) wireless networks is gaining momentum to connect almost all aspects of life through the network with much higher speed, very low latency and ubiquitous connectivity. Due to its crucial role in our lives, the network must secure its users, components, and services. The security threat landscape of 5G has grown enormously due to the unprecedented increase in types of services and in the number of devices. Therefore, security solutions if not developed yet must be envisioned already to cope with diverse threats on various services, novel technologies, and increased user information accessible by the network. This paper outlines the 5G network threat landscape, the security vulnerabilities in the new technological concepts that will be adopted by 5G, and provides either solutions to those threats or future directions to cope with those security challenges. We also provide a brief outline of the post-5G cellular technologies and their security vulnerabilities which is referred to as future generations (XG) in this paper. In brief, this paper highlights the present and future security challenges in wireless networks, mainly in 5G, and future directions to secure wireless networks beyond 5G.