scholarly journals Impacted Cyber Attacks Assessment in Wide Range of Big Data Security Systems

2019 ◽  
Vol 8 (3S) ◽  
pp. 625-629
Keyword(s):  
Big Data ◽  
Cyber Security ◽  
Cyber Attacks ◽  
Cyber Attack ◽  
Security Systems ◽  
Computing Systems ◽  
Wide Range ◽  
Software Platforms ◽  
The Cost ◽  
And Storage

The technological advancements in image storage, data processing, and signal analysis of Big Data include (a) the fastly degrade the cost of storage and CPU power in recent arena; the flexibility and cost-effectiveness of data operating platforms and cloud computing systems for flexible computation and storage; and (c) the development of new frameworks , which allow users to take advantage of these divided computing systems storing large amount of data which is almost flexible parallel processing. The proposed survey work focused on discussing the various impacted cyber-attack critics available in industry and the trending algorithms available for cyber security etc. Big data in IoT clouds handling and software platforms which allow the malware enter into the working systems are analyzed, reliable methods to avoid the miscellaneous malwares are clearly depicted here.

Big Data Analytics With Machine Learning and Deep Learning Methods for Detection of Anomalies in Network Traffic

2020 ◽  
pp. 317-346 ◽  
Author(s):  
Valliammal Narayan ◽  
Shanmugapriya D.
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Traffic Flow ◽  
Cyber Security ◽  
Learning Algorithms ◽  
Cyber Attacks ◽  
Structured Data ◽  
Cyber Attack ◽  
Imbalance Problem

Information is vital for any organization to communicate through any network. The growth of internet utilization and the web users increased the cyber threats. Cyber-attacks in the network change the traffic flow of each system. Anomaly detection techniques have been developed for different types of cyber-attack or anomaly strategies. Conventional ADS protect information transferred through the network or cyber attackers. The stable prevention of anomalies by machine and deep-learning algorithms are applied for cyber-security. Big data solutions handle voluminous data in a short span of time. Big data management is the organization and manipulation of huge volumes of structured data, semi-structured data and unstructured data, but it does not handle a data imbalance problem during the training process. Big data-based machine and deep-learning algorithms for anomaly detection involve the classification of decision boundary between normal traffic flow and anomaly traffic flow. The performance of anomaly detection is efficiently increased by different algorithms.

Cyber Attacks and Cyber Security in Underwater Communication

2021 ◽  
pp. 187-193
Author(s):  
Sachin Umrao
Keyword(s):  
Risk Analysis ◽  
Cyber Security ◽  
Data Transfer ◽  
Cyber Attacks ◽  
Underwater Communication ◽  
Cyber Attack ◽  
Recent Past ◽  
The World ◽  
The Cost

This chapter is structured around the concepts of risk analysis due to underwater deployment of the cables for data transfer. Most of the organizations have deployed their networks below the water for shortening the distances between peers and also to reduce the physical destruction cost of cables. Furthermore, some organizations like Google kept their servers below the water because it reduced the cost of getting it cool, which in turn increases the efficiency. However, security consultants around the world in recent past expressed their considerations that a cyber-attack on these servers or cables might result in miserable economic collision. This might be overstated but there are infrequent situations in which cable breakage could be riotous. Although organizations cannot rule the threat of attacks on these apparatuses, there are fewer check measures that could reduce the possible attack chances in underwater communication.

Big Data Analytics With Machine Learning and Deep Learning Methods for Detection of Anomalies in Network Traffic

2022 ◽  
pp. 678-707
Author(s):  
Valliammal Narayan ◽  
Shanmugapriya D.
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Anomaly Detection ◽  
Traffic Flow ◽  
Cyber Security ◽  
Learning Algorithms ◽  
Cyber Attacks ◽  
Structured Data ◽  
Cyber Attack ◽  
Imbalance Problem

Information is vital for any organization to communicate through any network. The growth of internet utilization and the web users increased the cyber threats. Cyber-attacks in the network change the traffic flow of each system. Anomaly detection techniques have been developed for different types of cyber-attack or anomaly strategies. Conventional ADS protect information transferred through the network or cyber attackers. The stable prevention of anomalies by machine and deep-learning algorithms are applied for cyber-security. Big data solutions handle voluminous data in a short span of time. Big data management is the organization and manipulation of huge volumes of structured data, semi-structured data and unstructured data, but it does not handle a data imbalance problem during the training process. Big data-based machine and deep-learning algorithms for anomaly detection involve the classification of decision boundary between normal traffic flow and anomaly traffic flow. The performance of anomaly detection is efficiently increased by different algorithms.

SARCP - Exploiting Cyber-Attack Prediction Through Socially-Aware Recommendation

2022 ◽  
Vol 14 (1) ◽  
pp. 0-0
Keyword(s):  
Social Network ◽  
Real World ◽  
Cyber Security ◽  
Betweenness Centrality ◽  
Cyber Attacks ◽  
Experimental Results ◽  
Cyber Attack ◽  
Defence Mechanisms ◽  
Network Measure ◽  
Recommender Algorithm

In the domain of cyber security, the defence mechanisms of networks has traditionally been placed in a reactionary role. Cyber security professionals are therefore disadvantaged in a cyber-attack situation due to the fact that it is vital that they maneuver such attacks before the network is totally compromised. In this paper, we utilize the Betweenness Centrality network measure (social property) to discover possible cyber-attack paths and then employ computation of similar personality of nodes/users to generate predictions about possible attacks within the network. Our method proposes a social recommender algorithm called socially-aware recommendation of cyber-attack paths (SARCP), as an attack predictor in the cyber security defence domain. In a social network, SARCP exploits and delivers all possible paths which can result in cyber-attacks. Using a real-world dataset and relevant evaluation metrics, experimental results in the paper show that our proposed method is favorable and effective.

scholarly journals Cyber Attacks on Critical Infrastructure

2016 ◽  
pp. 448-465
Author(s):  
Ana Kovacevic ◽  
Dragana Nikolic
Keyword(s):  
Control Systems ◽  
Cyber Security ◽  
Critical Infrastructure ◽  
Cyber Attacks ◽  
Cyber Attack ◽  
Future Directions ◽  
Infrastructure Systems ◽  
Cyber Threats ◽  
Scada Systems ◽  
Insight Into

We are facing the expansion of cyber incidents, and they are becoming more severe. This results in the necessity to improve security, especially in the vulnerable field of critical infrastructure. One of the problems in the security of critical infrastructures is the level of awareness related to the effect of cyberattacks. The threat to critical infrastructure is real, so it is necessary to be aware of it and anticipate, predict, and prepare against a cyber attack. The main reason for the escalation of cyberattacks in the field of Critical Infrastructure (CI) may be that most control systems used for CI do not utilise propriety protocols and software anymore; they instead utilise standard solutions. As a result, critical infrastructure systems are more than ever before becoming vulnerable and exposed to cyber threats. It is important to get an insight into what attack types occur, as this may help direct cyber security efforts. In this chapter, the authors present vulnerabilities of SCADA systems against cyber attack, analyse and classify existing cyber attacks, and give future directions to achieve better security of SCADA systems.

scholarly journals Infrastructure and Energy Conservation in Big Data Computing: A Survey

2019 ◽  
Vol 2 ◽  
pp. 73-82
Author(s):  
Ewa Niewiadomska-Szynkiewicz ◽  
Michał P. Karpowicz
Keyword(s):  
Big Data ◽  
High Performance ◽  
Physical Sciences ◽  
Communication Management ◽  
Data Intensive ◽  
Efficient Data ◽  
Software Platforms ◽  
Allocation Algorithms ◽  
And Storage ◽  
Big Data Computing

Progress in life, physical sciences and technology depends on efficient data-mining and modern computing technologies. The rapid growth of data-intensive domains requires a continuous development of new solutions for network infrastructure, servers and storage in order to address Big Datarelated problems. Development of software frameworks, include smart calculation, communication management, data decomposition and allocation algorithms is clearly one of the major technological challenges we are faced with. Reduction in energy consumption is another challenge arising in connection with the development of efficient HPC infrastructures. This paper addresses the vital problem of energy-efficient high performance distributed and parallel computing. An overview of recent technologies for Big Data processing is presented. The attention is focused on the most popular middleware and software platforms. Various energy-saving approaches are presented and discussed as well.

Cyber Security in Banking Sector

2019 ◽  
Vol 8 (2) ◽  
pp. 39-52
Author(s):  
Michael BEST ◽  
Lachezar KRUMOV ◽  
Ioan BACIVAROV
Keyword(s):  
Financial Institutions ◽  
Cyber Security ◽  
Banking Sector ◽  
Paper Analysis ◽  
Cyber Attacks ◽  
Cyber Attack ◽  
Network Graph ◽  
Bottom Line ◽  
Security Controls ◽  
Bank Robbery

Because banks are very often target of a cyber-attack, they have also good security controls in place. This paper analysis modern threats to banks and proposes an approach to detect and visualize the risk of data leakage. In the first part of this paper, a comparative analysis of the most common threats to the banking sector is made, based on both bank reports and cyber security companies. The authors came to the conclusion that at the bottom line, insider knowledge is necessary, which is the result of data leakage. This paper comparatively analysis modern threats to banks and shows an approach to detect and visualize the risk of data leakage. In the second part of the paper, a model - based on network graph - that can enumerate the risk of data leakage is proposed. Graphing a network of an organization with the connections of data flow between assets and actors can identify insecure connections that may lead to data leakage. As is demonstrated in this paper, financial institutions are important targets of cyber attacks. Consequently, the financial sector must invest heavily in cybersecurity and find the best ways to counter cyber attacks and cyber bank robbery attempts.

Cyber Attacks on Critical Infrastructure

2015 ◽  
pp. 1-18 ◽  
Author(s):  
Ana Kovacevic ◽  
Dragana Nikolic
Keyword(s):  
Control Systems ◽  
Cyber Security ◽  
Critical Infrastructure ◽  
Cyber Attacks ◽  
Cyber Attack ◽  
Future Directions ◽  
Infrastructure Systems ◽  
Cyber Threats ◽  
Scada Systems ◽  
Insight Into

We are facing the expansion of cyber incidents, and they are becoming more severe. This results in the necessity to improve security, especially in the vulnerable field of critical infrastructure. One of the problems in the security of critical infrastructures is the level of awareness related to the effect of cyberattacks. The threat to critical infrastructure is real, so it is necessary to be aware of it and anticipate, predict, and prepare against a cyber attack. The main reason for the escalation of cyberattacks in the field of Critical Infrastructure (CI) may be that most control systems used for CI do not utilise propriety protocols and software anymore; they instead utilise standard solutions. As a result, critical infrastructure systems are more than ever before becoming vulnerable and exposed to cyber threats. It is important to get an insight into what attack types occur, as this may help direct cyber security efforts. In this chapter, the authors present vulnerabilities of SCADA systems against cyber attack, analyse and classify existing cyber attacks, and give future directions to achieve better security of SCADA systems.

Artificial Intelligence-Based Solutions for Cyber Security Problems

2021 ◽  
pp. 68-86
Author(s):  
Merve Yildirim
Keyword(s):  
Artificial Intelligence ◽  
Cyber Security ◽  
Cyber Attacks ◽  
Network Systems ◽  
Security Systems ◽  
Artificial Intelligence Techniques ◽  
Security Issues ◽  
Security Applications ◽  
Cyber Threats ◽  
Legal Risks

Due to its nature, cyber security is one of the fields that can benefit most from the techniques of artificial intelligence (AI). Under normal circumstances, it is difficult to write software to defend against cyber-attacks that are constantly developing and strengthening in network systems. By applying artificial intelligence techniques, software that can detect attacks and take precautions can be developed. In cases where traditional security systems are inadequate and slow, security applications developed with artificial intelligence techniques can provide better security against many complex cyber threats. Apart from being a good solution for cyber security problems, it also brings usage problems, legal risks, and concerns. This study focuses on how AI can help solve cyber security issues while discussing artificial intelligence threats and risks. This study also aims to present several AI-based techniques and to explain what these techniques can provide to solve problems in the field of cyber security.

Cyber Security Aspects of Virtualization in Cloud Computing Environments

2021 ◽  
pp. 1658-1671
Author(s):  
Darshan Mansukhbhai Tank ◽  
Akshai Aggarwal ◽  
Nirbhay Kumar Chaubey
Keyword(s):  
Cloud Computing ◽  
Cyber Security ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Point Of View ◽  
Cyber Attack ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Detection Approach ◽  
Detection Response

Cybercrime continues to emerge, with new threats surfacing every year. Every business, regardless of its size, is a potential target of cyber-attack. Cybersecurity in today's connected world is a key component of any establishment. Amidst known security threats in a virtualization environment, side-channel attacks (SCA) target most impressionable data and computations. SCA is flattering major security interests that need to be inspected from a new point of view. As a part of cybersecurity aspects, secured implementation of virtualization infrastructure is very much essential to ensure the overall security of the cloud computing environment. We require the most effective tools for threat detection, response, and reporting to safeguard business and customers from cyber-attacks. The objective of this chapter is to explore virtualization aspects of cybersecurity threats and solutions in the cloud computing environment. The authors also discuss the design of their novel ‘Flush+Flush' cache attack detection approach in a virtualized environment.

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