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User Authentication Method Based on MKL for Keystroke and Mouse Behavioral Feature Fusion
In order to improve the recognition rate of users with single behavioral feature and prevent impostors from restricting an input device to avoid detection, a dual-index user authentication method based on Multiple Kernel Learning (MKL) for keystroke and mouse behavioral feature fusion was proposed in this paper. Due to the heterogeneity between the keystroke features and the mouse features, we argue that each type of features is mapped to a suitable kernel and the weights of each kernel are obtained through computing and then summed to obtain a compound kernel that implements the multifeature fusion. The dataset used in this paper was collected under complete uncontrolled condition from some volunteers by using our data collection program. The experimental results show that the proposed method can obtain the best recognition accuracy of 89.6%. Compared to the traditional methods of single feature, the dual-index method can get more stable and effective authentication. Therefore, the proposed method in this paper fully demonstrates the reliability of dual-index user authentication.
Group Recommender Systems Based on Members’ Preference for Trusted Social Networks
With the development of the Internet of Things (IoT), the group recommender system has also been extended to the field of IoT. The entities in the IoT are linked through social networks, which constitute massive amounts of data. In group activities such as group purchases and group tours, user groups often exhibit common interests and hobbies, and it is necessary to make recommendations for certain user groups. This idea constitutes the group recommender system. However, group members’ preferences are not fully considered in group recommendations, and how to use trusted social networks based on their preferences remains unclear. The focus of this paper is group recommendation based on an average strategy, where group members have preferential differences and use trusted social networks to correct for their preferences. Thus, the accuracy of the group recommender system in the IoT and big data environment is improved.
Cryptographic Strength Evaluation of Key Schedule Algorithms
Key schedule algorithms play an important role in modern encryption algorithms, and their security is as crucial as the security of the encryption algorithms themselves. Many studies have been performed on the cryptographic strength evaluation of the encryption algorithms; however, strength evaluation of the key schedule algorithms often obtains less attention that can lead towards the possible loophole in the overall encryption process. In this paper, a criterion is proposed to evaluate the cryptographic strength of the key schedule algorithms. This criterion includes different methods of data generation from subkeys and a suitable set of statistical tests. The statistical tests are used to explore the cryptographic properties such as diffusion, confusion, independence, and randomness in the subkeys generated by the key schedule algorithm. The proposed criterion has been applied to some of the key schedule algorithms of different block ciphers. The results confirm that the proposed criterion can effectively differentiate between strong- and weak-key schedule algorithms.
An Efficient Integrity Verification and Authentication Scheme over the Remote Data in the Public Clouds for Mobile Users
The digitalization of the modern world and its applications seem to be integrated more with the mobile phones than with any other communication devices. Since the mobile phones have become ubiquitous with applications for nearly all users, they have become a preferred choice for uploading the sensitive information to the cloud servers. Though the drive for data storage in cloud servers is implicit due to its pay-per-use policies, the manipulation of the data present in the cloud servers by hackers and hardware failure incidents, as happened in Amazon cloud servers in 2011, necessitates the demand for data verification at regular intervals over the data stored in the remote servers. In this line, modern day researchers have proposed many novel schemes for ensuring the remote data integrity, but they suffer from attacks or overheads due to computation and communication. This research paper provides solutions in three dimensions. Firstly, a novel scheme is introduced to verify the integrity of the data stored in the remote cloud servers in the context of mobile users. The second dimension is that of reducing the computational and communication overheads during the auditing process than the previous works. The third dimension securely authenticates the mobile user during the auditing process and the dynamic data operations such as block modification, insertion, and deletion. Moreover, the proposed protocol is provably secure exhibiting soundness, completeness, and data privacy making it an ideal scheme for implementation in the real-world applications.
Exploiting the Relationship between Pruning Ratio and Compression Effect for Neural Network Model Based on TensorFlow
Pruning is a method of compressing the size of a neural network model, which affects the accuracy and computing time when the model makes a prediction. In this paper, the hypothesis that the pruning proportion is positively correlated with the compression scale of the model but not with the prediction accuracy and calculation time is put forward. For testing the hypothesis, a group of experiments are designed, and MNIST is used as the data set to train a neural network model based on TensorFlow. Based on this model, pruning experiments are carried out to investigate the relationship between pruning proportion and compression effect. For comparison, six different pruning proportions are set, and the experimental results confirm the above hypothesis.
Extension of Research on Security as a Service for VMs in IaaS Platform
To satisfy security concerns including infrastructure as a service (IaaS) security framework, security service access, network anomaly detection, and virtual machine (VM) monitoring, a layered security framework is built which composes of a physical layer, a virtualization layer, and a security management layer. Then, two security service access methods are realized for various security tools from the perspective of whether security tools generate communication traffic. One without generating traffic employs the VM traffic redirection technology and the other leveraged the mechanism of multitasking process access. Moreover, a stacked LSTM-based network anomaly detection agentless method is proposed, which has advantages of a higher ratio of precision and recall. Finally, a Hypervisor-based agentless monitoring method for VMs based on dynamic code injection is proposed, which has benefits of high security of the external monitoring method and good context analysis of the internal monitoring mechanism. The experimental results demonstrate the effectiveness of the proposed protection framework and the corresponding security mechanisms, respectively.