Current Issue : July - September Volume : 2019 Issue Number : 3 Articles : 5 Articles
In the current intranet environment, information is becoming more readily accessed and replicated across a wide range of\ninterconnected systems. Anyone using the intranet computer may access content that he does not have permission to access. For an\ninsider attacker, it is relatively easy to steal a colleagueâ??s password or use an unattended computer to launch an attack. A common\none-time user authentication method may not work in this situation. In this paper, we propose a user authentication method\nbased on mouse biobehavioral characteristics and deep learning, which can accurately and efficiently perform continuous identity\nauthentication on current computer users, thus to address insider threats.We used an open-source dataset with ten users to carry\nout experiments, and the experimental results demonstrated the effectiveness of the approach. This approach can complete a user\nauthentication task approximately every 7 seconds, with a false acceptance rate of 2.94% and a false rejection rate of 2.28%....
Attribute-Based Encryption (ABE) must provide an efficient revocation mechanism since a userâ??s private key can be compromised\nor expired over time.The existing revocable ABE schemes have the drawbacks of heavy computational costs on key updates and\nencryption operations,whichmake the entities for performing these operations a possible bottleneck inpractice applications. In this\npaper, we propose an efficientCiphertext-PolicyAttribute-Based Online/Offline Encryption with userRevocation (R-CP-ABOOE).\nWe integrate the subset difference method with ciphertext-policyABE to significantly improve key-update efficiency on the side of\nthe trusted party from O(r log(N/r)) to O(r), whereN is the number of users and r is the number of revoked users. To reduce the\nencryption burden for mobile devices, we use the online/offline technology to shift the majority of encryption work to the offline\nphase, and thenmobile devices only need to execute a fewsimple computations to create a ciphertext. In addition, we exploit anovel\ntrick to prove its selective security under the q-type assumption. Performance analysis shows that our scheme greatly improves the\nkey-update efficiency for the trusted party and the encryption efficiency for mobile devices....
In order to obtain high quality and large-scale labelled data for information security research, we propose a new approach that\ncombines a generative adversarial network with the BiLSTM-Attention-CRF model to obtain labelled data fromcrowd annotations.\nWe use the generative adversarial network to find common features in crowd annotations and then consider them in conjunction\nwith the domain dictionary feature and sentence dependency feature as additional features to be introduced into the BiLSTMAttention-\nCRF model, which is then used to carry out named entity recognition in crowdsourcing. Finally, we create a dataset to\nevaluate our models using information security data.The experimental results show that our model has better performance than\nthe other baseline models....
Currently 5G communication networks are envisioned to offer in a near future a wide range of high-quality services and unfaltering\nuser experiences. In order to achieve this, several issues including security, privacy, and trust aspects need to be solved so that the\n5G networks can be widely welcomed and accepted. Considering above, in this paper, we take a step towards these requirements\nby proposing a dedicated SDN-based integrated security framework for the Internet of Radio Light (IoRL) system that is following\n5G architecture design. In particular, we present how TCP SYN-based scanning activities and DHCP-related network threats like\nDenial of Service (DoS), traffic eavesdropping, etc. can be detected and mitigated using such an approach. Enclosed experimental\nresults prove that the proposed security framework is effective and efficient and thus can be considered as a promising defensive\nsolution....
In this paper, in order to embed virtual networks by considering network security, we propose a virtual network embedding based\non security levelwithVNF placement. In this method, virtual networks are embedded in a substrate network by considering security\nand some security VNFs are placed in order to increase the security level of substrate networks. By using our proposed method,\nmany virtual networks can be embedded by considering security level. As a result, the reward can be increased and the cost of\nplacing VNFs is not increased so much.We evaluate the performance of our proposed method with simulation. The performance\nof this method is compared with the performance of a method that places VNFs randomly and the performance of a method\nwithout placing VNFs. From numerical examples, we investigate the effectiveness of this method. In numerical examples, we show\nthat the proposed method is effective in embedding virtual networks by considering network security....
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