An App-Based IoT-NFC Controlled Remote Access Security Through Cryptographic Algorithm

Information security threats exploit vulnerabilities in communication networks. Remote access vulnerabilities are evident from the point of communication initialization following the communication channel to data or resources being accessed. These threats differ depending on the type of device used to procure remote access. One kind of these remote access devices can be considered as safe as the organization probably issues it to provide for remote access. The other type is risky and unsafe, as they are beyond the organization’s control and monitoring. The myriad of devices is, however, a necessary evil, be it employees on public networks like cyber cafes, wireless networks, vendors support, or telecommuting. Virtual Private Network (VPN) securely connects a remote user or device to an internal or private network using the internet and other public networks. However, this conventional remote access security approach has several vulnerabilities, which can take advantage of encryption. The significant threats are malware, botnets, and Distributed Denial of Service (DDoS). Because of the nature of a VPN, encryption will prevent traditional security devices such as a firewall, Intrusion Detection System (IDS), and antivirus software from detecting compromised traffic. These vulnerabilities have been exploited over time by attackers using evasive techniques to avoid detection leading to costly security breaches and compromises. We highlight numerous shortcomings for several conventional approaches to remote access security. We then adopt network tiers to facilitate vulnerability management (VM) in remote access domains. We perform regular traffic simulation using Network Security Simulator (NeSSi2) to set bandwidth baseline and use this as a benchmark to investigate malware spreading capabilities and DDoS attacks by continuous flooding in remote access. Finally, we propose a novel approach to remote access security by passive learning of packet capture file features using machine learning and classification using a classifier model.

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Face Searching in Large Databases

Advances in Face Image Analysis ◽

10.4018/978-1-61520-991-0.ch002 ◽

2010 ◽

pp. 16-41

Author(s):

Maria De Marsico ◽

Michele Nappi ◽

Daniel Riccio ◽

...

Keyword(s):

Face Recognition ◽

Recognition Rate ◽

3D Models ◽

Similarity Score ◽

Remote Access ◽

Private Companies ◽

Input Size ◽

Large Databases ◽

Iris Scanning ◽

Access Security

Both government agencies and private companies are investing significant resources to improve local/remote access security. Badge or password-based procedures have proven to be too vulnerable, while biometric research has significantly grown, mostly due to technological progresses that allow using increasingly efficient techniques, yet at decreasing costs. Suitable devices capture images of user’s face, iris, etc., or other biometric elements such as fingerprints or voice. Each biometry calls for specific procedures. Measures from user’s data make up the so called biometric key, which is stored in a database (enrolment) or used for recognition (testing). During recognition, a subject’s key is matched against those in the database, producing a similarity score for each match. However, some drawbacks exist. For example, iris scanning is very reliable but presently too intrusive, while fingerprints are more socially accepted but not applicable to non-consentient people. On the other hand, face recognition represents a good solution even under less controlled conditions. In the last decade, many algorithms based on linear/non-linear methods, neural networks, wavelets, etc. have been proposed. Nevertheless, during Face Recognition Vendor Test 2002 most of them encountered problems outdoors. This lowers their reliability compared to other biometries, and underlines the need for more research. This chapter provides a survey of recent outcomes on the topic, addressing both 2D imagery and 3D models, to provide a starting reference to potential investigators. Tables containing different collections of parameters (such as input size, recognition rate, number of addressed problems) simplify comparisons. Some future directions are finally proposed.

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