Integrated Cyber Crime and Cyber Security Model

2020 ◽  
pp. 227-260
Author(s):  
Balsing Rajput
Keyword(s):  
Cyber Security ◽  
Security Model ◽  
Cyber Crime

Security of Cyber-Physical Systems: A Generalized Algorithm for Intrusion Detection and Determining Security Robustness of Cyber Physical Systems using Logical Truth Tables

2013 ◽  
Vol 9 ◽  
Author(s):  
Curtis G. Northcutt
Keyword(s):  
Intrusion Detection ◽  
Cyber Security ◽  
Logical Truth ◽  
Cyber Physical Systems ◽  
Security Model ◽  
Security Measures ◽  
Physical Systems ◽  
Multiple Signal ◽  
Security Attack ◽  
Truth Tables

The recent proliferation of embedded cyber components in modern physical systems [1] has generated a variety of new security risks which threaten not only cyberspace, but our physical environment as well. Whereas earlier security threats resided primarily in cyberspace, the increasing marriage of digital technology with mechanical systems in cyber-physical systems (CPS), suggests the need for more advanced generalized CPS security measures. To address this problem, in this paper we consider the first step toward an improved security model: detecting the security attack. Using logical truth tables, we have developed a generalized algorithm for intrusion detection in CPS for systems which can be defined over discrete set of valued states. Additionally, a robustness algorithm is given which determines the level of security of a discrete-valued CPS against varying combinations of multiple signal alterations. These algorithms, when coupled with encryption keys which disallow multiple signal alteration, provide for a generalized security methodology for both cyber-security and cyber-physical systems.

A Survey on Artificial Intelligence Techniques to Prevent Cyber Crime

2018 ◽  
Vol 8 (5) ◽  
pp. 80
Author(s):  
Juveriya Afreen
Keyword(s):  
Artificial Intelligence ◽  
Cyber Security ◽  
Data Security ◽  
Human Reasoning ◽  
Cyber Crime ◽  
Artificial Intelligence Techniques

Abstract-- With increase in complexity of data, security, it is difficult for the individuals to prevent the offence. Thus, by using any automation or software it’s not possible by only using huge fixed algorithms to overcome this. Thus, we need to look for something which is robust and feasible enough. Hence AI plays an epitome role to defense such violations. In this paper we basically look how human reasoning along with AI can be applied to uplift cyber security.

scholarly journals Enhancing relationships between criminology and cybersecurity

2021 ◽  
pp. 000486582110039
Author(s):  
Benoît Dupont ◽  
Chad Whelan
Keyword(s):  
National Security ◽  
Cyber Security ◽  
Data Networks ◽  
Cyber Crime ◽  
Broad Concept ◽  
Diverse Field ◽  
Computer Scientists ◽  
Control And Prevention ◽  
Prevention Activities ◽  
The Moment

‘Cybercrime’ is an umbrella concept used by criminologists to refer to traditional crimes that are enhanced via the use of networked technologies (i.e. cyber-enabled crimes) and newer forms of crime that would not exist without networked technologies (i.e. cyber-dependent crimes). Cybersecurity is similarly a very broad concept and diverse field of practice. For computer scientists, the term ‘cybersecurity’ typically refers to policies, processes and practices undertaken to protect data, networks and systems from unauthorised access. Cybersecurity is used in subnational, national and transnational contexts to capture an increasingly diverse array of threats. Increasingly, cybercrimes are presented as threats to cybersecurity, which explains why national security institutions are gradually becoming involved in cybercrime control and prevention activities. This paper argues that the fields of cyber-criminology and cybersecurity, which are segregated at the moment, are in much need of greater engagement and cross-fertilisation. We draw on concepts of ‘high’ and ‘low’ policing ( Brodeur, 2010 ) to suggest it would be useful to consider ‘crime’ and ‘security’ on the same continuum. This continuum has cybercrime at one end and cybersecurity at the other, with crime being more the domain of ‘low’ policing while security, as conceptualised in the context of specific cybersecurity projects, falls under the responsibility of ‘high’ policing institutions. This unifying approach helps us to explore the fuzzy relationship between cyber- crime and cyber- security and to call for more fruitful alliances between cybercrime and cybersecurity researchers.

scholarly journals IntruDTree: A Machine Learning-Based Cyber Security Intrusion Detection Model

2020 ◽  
Author(s):  
Iqbal H. Sarker ◽  
Yoosef B. Abushark ◽  
Fawaz Alsolami ◽  
Asif Irshad Khan
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Cyber Security ◽  
Intrusion Detection System ◽  
Detection System ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Security Model ◽  
K Nearest Neighbor ◽  
Detection Model

Cyber security has recently received enormous attention in today’s security concerns, due to the popularity of the Internet-of-Things (IoT), the tremendous growth of computer networks, and the huge number of relevant applications. Thus, detecting various cyber-attacks or anomalies in a network and building an effective intrusion detection system that performs an essential role in today’s security is becoming more important. Artificial intelligence, particularly machine learning techniques, can be used for building such a data-driven intelligent intrusion detection system. In order to achieve this goal, in this paper, we present an Intrusion Detection Tree (“IntruDTree”) machine-learning-based security model that first takes into account the ranking of security features according to their importance and then build a tree-based generalized intrusion detection model based on the selected important features. This model is not only effective in terms of prediction accuracy for unseen test cases but also minimizes the computational complexity of the model by reducing the feature dimensions. Finally, the effectiveness of our IntruDTree model was examined by conducting experiments on cybersecurity datasets and computing the precision, recall, fscore, accuracy, and ROC values to evaluate. We also compare the outcome results of IntruDTree model with several traditional popular machine learning methods such as the naive Bayes classifier, logistic regression, support vector machines, and k-nearest neighbor, to analyze the effectiveness of the resulting security model.

Cyber Security Model of Artificial Social System Man-Machine

2015 ◽  
pp. 500-514
Author(s):  
Calin Ciufudean
Keyword(s):  
Cyber Security ◽  
Social System ◽  
Social Systems ◽  
Discrete Event ◽  
Shared Environment ◽  
Security Model ◽  
Automatic Data ◽  
Optimum Trajectory ◽  
Multi Agent ◽  
And Control

Cyber Security Model of Artificial Social System Man-Machine takes advantage of an important chapter of artificial intelligence, discrete event systems applied for modelling and simulation of control, logistic supply, chart positioning, and optimum trajectory planning of artificial social systems. “An artificial social system is a set of restrictions on agents` behaviours in a multi-agent environment. Its role is to allow agents to coexist in a shared environment and pursue their respective goals in the presence of other agents” (Moses & Tennenholtz, n.d.). Despite conventional approaches, Cyber Security Model of Artificial Social System Man-Machine is not guided by rigid control algorithms but by flexible, event-adaptable ones that makes them more lively and available. All these allow a new design of artificial social systems dotted with intelligence, autonomous decision-making capabilities, and self-diagnosing properties. Heuristics techniques, data mining planning activities, scheduling algorithms, automatic data identification, processing, and control represent as many trumps for these new systems analyzing formalism. The authors challenge these frameworks to model and simulate the interaction of man-machine in order to have a better look at the human, social, and organizational privacy and information protection.

Government's Dynamic Approach to Addressing Challenges of Cybersecurity in South Africa

2019 ◽  
pp. 139-156
Author(s):  
Thokozani Ian Nzimakwe
Keyword(s):  
Cyber Security ◽  
Modern Society ◽  
Daily Basis ◽  
Cyber Crime ◽  
Security Strategy ◽  
Dynamic Approach ◽  
Sensitive Data ◽  
Unique Challenge ◽  
The Public ◽  
Cyber Space

Cybersecurity is the practice of making the networks that constitute cyber space secure against intrusions. The aim is to maintain the confidentiality, the availability and integrity of information, by detecting interferences. Traditionally, cybersecurity has focused on preventing intrusions and monitoring ports. The evolving threat landscape, however, calls for a more dynamic approach. It is increasingly clear that total cybersecurity is impossible, unless government develops a cyber-security strategy. The aim of this chapter is to discuss government's dynamic approach to addressing challenges of cybersecurity. The chapter looks at the co-ordination of cyber-security activities so as to have a coordinated approach to cyber-crime. This chapter also highlights the idea of protecting sensitive data for the public good. It is generally accepted that technology has become indispensable in modern society. Government's cybersecurity presents a unique challenge simply because of the volume of threats that agencies working for government face on a daily basis.

Cybercrime Investigation

2017 ◽  
pp. 96-120
Author(s):  
Sujitha S. ◽  
Parkavi R.
Keyword(s):  
Law Enforcement ◽  
Cyber Security ◽  
Security And Privacy ◽  
Malware Analysis ◽  
Cyber Crime ◽  
Ddos Attacks ◽  
Enforcement Policy ◽  
Control Devices ◽  
The Law

This book chapter will be an introduction to hacking, DDOS attacks and Malware Analysis. This chapter will also describe about the cyber-crime against properties and Persons and will give a detailed description about the cyber security and privacy. This chapter will deal with the cyber-crime investigations, law enforcement policy and procedures. This chapter will also describe about the peer supporting programs for the law enforcement authorities and a detailed description about the control devices and techniques that are used by an officer. This chapter will give an opportunity to know about the evidence collecting procedures in cyber-crime and also the barriers to cybercrime investigations.

A Multi-level Evidence-based Cyber Crime Prosecution Information System

2018 ◽  
Vol 7 (3.19) ◽  
pp. 39 ◽  
Author(s):  
Moses Adah Agana ◽  
Ruth Wario
Keyword(s):  
Real Time ◽  
Cyber Security ◽  
Research Work ◽  
Geographical Location ◽  
Cyber Crime ◽  
Law Enforcement Agencies ◽  
Crime Detection ◽  
Multi Level ◽  
And Control ◽  
Cyber Criminals

This research work was designed to utilize multi-level cyber crime detection and control system to provide enhanced real-time evidence to cyber crime investigators to aid them in prosecuting cyber criminals. The design was based on a robust system combining user-identity, device identity, geographical location and user activities to provide evidences to uniquely identify a cyber user and detect crimes committed. The system captures the user’s facial image and biometric finger print as mandatory login parameters in addition to username and password before granting access. The system was tested and implemented in a real time cyber security website www.ganamos.org.  The results showed that it is possible to divulge the identity of cyber users and associate their activities with the devices they use, the date, time and location of operation. These can provide real-time evidences to law enforcement agencies to track down and prosecute cyber criminals. 

Sign in / Sign up

Export Citation Format

Share Document