Biblio
Access authentication is a key technology to identify the legitimacy of mobile users when accessing the space-ground integrated networks (SGIN). A hierarchical identity-based signature over lattice (L-HIBS) based mobile access authentication mechanism is proposed to settle the insufficiencies of existing access authentication methods in SGIN such as high computational complexity, large authentication delay and no-resistance to quantum attack. Firstly, the idea of hierarchical identity-based cryptography is introduced according to hierarchical distribution of nodes in SGIN, and a hierarchical access authentication architecture is built. Secondly, a new L-HIBS scheme is constructed based on the Small Integer Solution (SIS) problem to support the hierarchical identity-based cryptography. Thirdly, a mobile access authentication protocol that supports bidirectional authentication and shared session key exchange is designed with the aforementioned L-HIBS scheme. Results of theoretical analysis and simulation experiments suggest that the L-HIBS scheme possesses strong unforgeability of selecting identity and adaptive selection messages under the standard security model, and the authentication protocol has smaller computational overhead and shorter private keys and shorter signature compared to given baseline protocols.
One of the basic behavioural biometric methods is keystroke element. Being less expensive and not requiring any extra bit of equipment is the main advantage of keystroke element. The primary concentration of this paper is to give an inevitable review of behavioural biometrics strategies, measurements and different methodologies and difficulties and future bearings specially of keystroke analysis and mouse dynamics. Keystrokes elements frameworks utilize insights, e.g. time between keystrokes, word decisions, word mixes, general speed of writing and so on. Mouse Dynamics is termed as the course of actions captured from the moving mouse by an individual when interacting with a GUI. These are representative factors which may be called mouse dynamics signature of an individual, and may be used for verification of identity of an individual. In this paper, we compare the authentication system based on keystroke dynamics and mouse dynamics.
A database is an organized collection of data. Though a number of techniques, such as encryption and electronic signatures, are currently available for the protection of data when transmitted across sites. Database security refers to the collective measures used to protect and secure a database or database management software from illegitimate use and malicious threats and attacks. In this paper, we create 6 types of method for more secure ways to store and retrieve database information that is both convenient and efficient. Confidentiality, integrity, and availability, also known as the CIA triad, is a model designed to guide policies for information security within the database. There are many cryptography techniques available among them, ECC is one of the most powerful techniques. A user wants to the data stores or request, the user needs to authenticate. When a user who is authenticated, he will get key from a key generator and then he must be data encrypt or decrypt within the database. Every keys store in a key generator and retrieve from the key generator. We use 256 bits of AES encryption for rows level encryption, columns level encryption, and elements level encryption for the database. Next two method is encrypted AES 256 bits random key by using 521 bits of ECC encryption and signature for rows level encryption and column level encryption. Last method is most secure method in this paper, which method is element level encryption with AES and ECC encryption for confidentiality and ECC signature use for every element within the database for integrity. As well as encrypting data at rest, it's also important to ensure confidential data are encrypted in motion over our network to protect against database signature security. The advantages of elements level are difficult for attack because the attacker gets a key that is lose only one element. The disadvantages need to thousands or millions of keys to manage.
Signature-based Intrusion Detection Systems (IDS) are a key component in the cybersecurity defense strategy for any network being monitored. In order to improve the efficiency of the intrusion detection system and the corresponding mitigation action, it is important to address the problem of false alarms. In this paper, we present a comparative analysis of two approaches that consider the false alarm minimization and alarm correlation techniques. The output of this analysis provides us the elements to propose a parallelizable strategy designed to achieve better results in terms of precision, recall and alarm load reduction in the prioritization of alarms. We use Prelude SIEM as the event normalizer in order to process security events from heterogeneous sensors and to correlate them. The alarms are verified using the dynamic network context information collected from the vulnerability analysis, and they are prioritized using the HP Arsight priority formula. The results show an important reduction in the volume of alerts, together with a high precision in the identification of false alarms.
Although Vehicle Named Data Network (VNDN) possess the communication benefits of Named Data Network and Vehicle Opportunity Network, it also introduces some new privacy problems, including the identity security of Data Requesters and Data Providers. Data providers in VNDN need to sign data packets directly, which will leak the identity information of the providers, while the vicinity malicious nodes can access the sensitive information of Data Requesters by analyzing the relationship between Data Requesters and the data names in Interest Packages that are sent directly in plaintext. In order to solve the above privacy problems, this paper presents an identity privacy protection strategy for Data Requesters and Data Providers in VNDN. A ring signature scheme is used to hide the correlation between the signature and the data provider and the anonymous proxy idea is used to protect the real identity of the data requester in the proposed strategy. Security Analysis and experiments in the ONE-based VNDN platform indicate that the proposed strategy is effective and practical.
Vehicle ad-hoc network (VANET) is the main driving force to alleviate traffic congestion and accelerate the construction of intelligent transportation. However, the rapid growth of the number of vehicles makes the construction of the safety system of the vehicle network facing multiple tests. This paper proposes an identity-based aggregate signature scheme to protect the privacy of vehicle identity, receive messages in time and authenticate quickly in VANET. The scheme uses aggregate signature algorithm to aggregate the signatures of multiple users into one signature, and joins the idea of batch authentication to complete the authentication of multiple vehicular units, thereby improving the verification efficiency. In addition, the pseudoidentity of vehicles is used to achieve the purpose of vehicle anonymity and privacy protection. Finally, the secure storage of message signatures is effectively realized by using reliable cloud storage technology. Compared with similar schemes, this paper improves authentication efficiency while ensuring security, and has lower storage overhead.
the more (IoT) scales up with promises, the more security issues raise to the surface and must be tackled down. IoT is very vulnerable against DoS attacks. In this paper, we propose a hybrid design of signature-based IDS and anomaly-based IDS. The proposed hybrid design intends to enhance the intrusion detection and prevention systems (IDPS) to detect any DoS attack at early stages by classifying the network packets based on user behavior. Simulation results prove successful detection of DoS attack at earlier stages.
In the modern day and age, credential based authentication systems no longer provide the level of security that many organisations and their services require. The level of trust in passwords has plummeted in recent years, with waves of cyber attacks predicated on compromised and stolen credentials. This method of authentication is also heavily reliant on the individual user's choice of password. There is the potential to build levels of security on top of credential based authentication systems, using a risk based approach, which preserves the seamless authentication experience for the end user. One method of adding this security to a risk based authentication framework, is keystroke dynamics. Monitoring the behaviour of the users and how they type, produces a type of digital signature which is unique to that individual. Learning this behaviour allows dynamic flags to be applied to anomalous typing patterns that are produced by attackers using stolen credentials, as a potential risk of fraud. Methods from statistics and machine learning have been explored to try and implement such solutions. This paper will look at an Autoencoder model for learning the keystroke dynamics of specific users. The results from this paper show an improvement over the traditional tried and tested statistical approaches with an Equal Error Rate of 6.51%, with the additional benefits of relatively low training times and less reliance on feature engineering.
Malware is one of the threats to information security that continues to increase. In 2014 nearly six million new malware was recorded. The highest number of malware is in Trojan Horse malware while in Adware malware is the most significantly increased malware. Security system devices such as antivirus, firewall, and IDS signature-based are considered to fail to detect malware. This happens because of the very fast spread of computer malware and the increasing number of signatures. Besides signature-based security systems it is difficult to identify new methods, viruses or worms used by attackers. One other alternative in detecting malware is to use honeypot with machine learning. Honeypot can be used as a trap for packages that are suspected while machine learning can detect malware by classifying classes. Decision Tree and Support Vector Machine (SVM) are used as classification algorithms. In this paper, we propose architectural design as a solution to detect malware. We presented the architectural proposal and explained the experimental method to be used.