RESEARCH ON SECURE VIRUS TROJAN IN CYBERSECURITY PLATFORM

Security is main issue of this generation of computing because many types of attacks are increasing day by day. Establishing a network is not a big issue for network administrators but protecting the entire network is a big issue. There are various methods and tools are available today for destroying the existing network. In this paper we mainly emphasize on the network security also we present some major issues that can affect our network, Trojan horse virus can give rise to the leakage of internal data. Keywords:Security, Trojan Horse, System, Network

Review on Data Transmission using Dynamic Routing And Reverse Encryption Algorithm

Abstract: Security has become one of the major issues for data communication over wired and wireless Network. Due to the transmission of valuable data over the network, security is the big issue for the information technology sector. For the data transmission in network may not be secure and is defenceless to many threats. The various security mechanisms have been incorporated in the recent times, which greatly improve the data security. In this paper the new way of transmission, the information using a routing algorithm such as DSDV or AODV with encryption algorithm (i.e. Reverse Encryption Algorithm (REA)) improve the data security over network DOI: 10.17762/ijritcc2321-8169.160414

Network Traffic Threat Detection and Reporting System Validation through UML

In today’s digital world, computer network security experts struggle to manage security issues effectively. Reporting the network data in graphical form helps the expert to take decision in more effective and efficient way. Visualizing the network traffic seamlessly is a big challenge but an integrated network traffic visualization approach can resolve such issues effectively. The work presented here focuses on structural, behavioral and architectural modeling of an Integrated Network Traffic Visualization System (INTVS) and validating  it through unified modeling language. The adopted modeling can accommodate the analysis and designing of INTVS effectively, which is demonstrated in this study. Keywords:  Network traffic visualization, Network Security, INTVS framework,  INTVS modeling

Autonomous Vehicles:The Cybersecurity Vulnerabilities and Countermeasures for Big Data Communication

The possible applications of communication based on big data have steadily increased in several industries, such as the autonomous vehicle industry, with a corresponding increase in security challenges, including cybersecurity vulnerabilities (CVs). The cybersecurity-related symmetry of big data communication systems used in autonomous vehicles may raise more vulnerabilities in the data communication process between these vehicles and IoT devices. The data involved in the CVs may be encrypted using an asymmetric and symmetric algorithm. Autonomous vehicles with proactive cybersecurity solutions, power-based cyberattacks, and dynamic countermeasures are the modern issues/developments with emerging technology and evolving attacks. Research on big data has been primarily focused on mitigating CVs and minimizing big data breaches using appropriate countermeasures known as security solutions. In the future, CVs in data communication between autonomous vehicles (DCAV), the weaknesses of autonomous vehicular networks (AVN), and cyber threats to network functions form the primary security issues in big data communication, AVN, and DCAV. Therefore, efficient countermeasure models and security algorithms are required to minimize CVs and data breaches. As a technique, policies and rules of CVs with proxy and demilitarized zone (DMZ) servers were combined to enhance the efficiency of the countermeasure. In this study, we propose an information security approach that depends on the increasing energy levels of attacks and CVs by identifying the energy levels of each attack. To show the results of the performance of our proposed countermeasure, CV and energy consumption are compared with different attacks. Thus, the countermeasures can secure big data communication and DCAV using security algorithms related to cybersecurity and effectively prevent CVs and big data breaches during data communication

Next-generation big data analytics: state of the art, challenges, and future research topics

The term big data occurs more frequently now than ever before. A large number of fields and subjects, ranging from everyday life to traditional research fields (i.e., geography and transportation, biology and chemistry, medicine and rehabilitation), involve big data problems. The popularizing of various types of network has diversified types, issues, and solutions for big data more than ever before. In this paper, we review recent research in data types, storage models, privacy, data security, analysis methods, and applications related to network big data. Finally, we summarize the challenges and development of big data to predict current and future trends.This work was supported in part by the “Open3D: Collaborative Editing for 3D Virtual Worlds” [EPSRC (EP/M013685/1)], in part by the “Distributed Java Infrastructure for Real-Time Big-Data” (CAS14/00118), in part by eMadrid (S2013/ICE-2715), in part by the HERMES-SMARTDRIVER (TIN2013-46801-C4-2-R), and in part by the AUDACity (TIN2016-77158-C4-1-R). Paper no. TII-16-1

RESEARCH ON SECURE VIRUS TROJAN IN CYBERSECURITY PLATFORM

Security is main issue of this generation of computing because many types of attacks are increasing day by day. Establishing a network is not a big issue for network administrators but protecting the entire network is a big issue. There are various methods and tools are available today for destroying the existing network. In this paper we mainly emphasize on the network security also we present some major issues that can affect our network, Trojan horse virus can give rise to the leakage of internal data. Keywords:Security, Trojan Horse, System, Network

Generation of a dataset for network intrusion detection in a real 5G environment

Abstract. As 5G technology is widely implemented on a global scale, both the complexity of networks and the amount of data created have exploded. Future mobile networks will incorporate artificial intelligence as a crucial enabler for intelligent wireless communications, closed-loop network optimization, and big data analytics. In these future mobile networks, network security would be of the utmost importance, as many applications expect a higher level of network security from the networking infrastructure. Therefore, conventional procedures in which action is taken following the detection of an attack would be insufficient, and self-adaptive intelligent security systems would be required. This paves the door for AI-based network security strategies in the future. In AI-based security research, the lack of comprehensive, valid datasets is a persistent issue. Publicly accessible data sets are either obsolete or insufficient for 5G security research. In addition, mobile network providers are hesitant to share actual network datasets due to privacy issues. Hence, a genuine data set from a real network is extremely beneficial to AI-based network security research. This study will describe the creation of a genuine dataset containing several attack scenarios implemented on a real 5G network with real mobile users. Since a fully operational 5G network is utilized to generate the data, this dataset is characterized by its close resemblance to real-world situations. In addition, data is collected from multiple base stations and made available as independent datasets for federated learning-based research to build a global model of intelligence for the entire network. The obtained data will be processed to identify the optimal features, and the accuracy of intrusion detection will be validated using several common machine learning and neural network models such as Decision Tree, Random Forest, K-Nearest Neighbor, Support Vector Machines and Multi Layer Perceptron. A detailed analysis of a binary classification to detect malicious and non-malicious flows as well as a multi class classification to detect different attack types is presented

On the cyber security issues of the internet infrastructure

The Internet network has received huge attentions by the research community. At a first glance, the network optimization and scalability issues dominate the efforts of researchers and vendors. Many results have been obtained in the last decades: the Internet’s architecture is optimized to be cheap, robust and ubiquitous. In contrast, such a network has never been perfectly secure. During all its evolution, the security threats of the Internet persist as a transversal and endless topic. Nowadays, the Internet network hosts a multitude of mission critical activities. The electronic voting systems and financial services are carried out through it. Governmental institutions, financial and business organizations depend on the performance and the security of the Internet. This role confers to the Internet network a critical characterization. At the same time, the Internet network is a vector of malicious activities, like Denial of Service attacks; many reports of attacks can be found in both academic outcomes and daily news. In order to mitigate this wide range of issues, many research efforts have been carried out in the past decades; unfortunately, the complex architecture and the scale of the Internet make hard the evaluation and the adoption of such proposals. In order to improve the security of the Internet, the research community can benefit from sharing real network data. Unfortunately, privacy and security concerns inhibit the release of these data: its suffices to imagine the big amount of private information (e.g., political preferences or religious belief) it is possible to get while reading the Internet packets exchanged between users and web services. This scenario motivates my research, and represents the context of this dissertation which contributes to the analysis of the security issues of the Internet infrastructures and describes relevant security proposals. In particular, the main outcomes described in this dissertation are: • the definition of a secure routing protocol for the Internet network able to provide cryptographic guarantees against false route announcement and invalid path attack; • the definition of a new obfuscation technique that allow the research community to publicly release their real network flows with formal guarantees of security and privacy; • the evidence of a new kind of leakage of sensitive informations obtained hacking the models used by sundry Machine Learning Algorithms