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Classification for DGA-Based Malicious Domain Names with Deep Learning Architectures
Feng Zeng,
Shuo Chang,
Xiaochuan Wan
Issue:
Volume 6, Issue 6, December 2017
Pages:
67-71
Received:
5 December 2017
Published:
6 December 2017
Abstract: The preemptive defenses against various malware created by domain generation algorithms (DGAs) have traditionally been solved using manually-crafted domain features obtained by heuristic process. However, it is difficult to achieve real-world deployment with most research on detecting DGA-based malicious domain names due to poor performance and time consuming. Based on the recent overwhelming success of deep learning networks in a broad range of applications, this article transfers five advanced learned ImageNet models from Alex Net, VGG, Squeeze Net, Inception, Res Net to classify DGA domains and non-DGA domains, which: (i) is suited to automate feature extraction from raw inputs; (ii) has fast inference speed and good accuracy performance; and (iii) is capable of handling large-scale data. The results show that the proposed approach is effective and efficient.
Abstract: The preemptive defenses against various malware created by domain generation algorithms (DGAs) have traditionally been solved using manually-crafted domain features obtained by heuristic process. However, it is difficult to achieve real-world deployment with most research on detecting DGA-based malicious domain names due to poor performance and tim...
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Multiagent Cooperative Reinforcement Learning by Expert Agents (MCRLEA)
Deepak Annasaheb Vidhate,
Parag Arun Kulkarni
Issue:
Volume 6, Issue 6, December 2017
Pages:
72-84
Received:
2 October 2017
Accepted:
17 October 2017
Published:
7 December 2017
Abstract: The paper gives novel approach Multiagent Cooperative Reinforcement Learning by Expert Agents (MCRLEA) for dynamic decision making in the retail application. Furthermore, it put up different cooperation schemes for multiagent cooperative reinforcement learning i.e. EQ learning, EGroup, EDynamic, EGoal driven and Expert agents scheme. Implementation outcome includes a demonstration of recommended cooperation schemes that are competent enough to speedup the collection of agents that achieve excellent action policies. Accordingly this approach presents three retailer stores in the retail market place. Retailers can help to each other and can obtain profit from cooperation knowledge through learning their own strategies that just stand for their aims and benefit. The vendors are the knowledgeable agents in the hypothesis to employ cooperative learning to train in the circumstances. Assuming significant hypothesis on the vendor’s stock policy, restock period, arrival process of the consumers, the approach is formed as Markov decision process model that makes it possible to design learning algorithms. The proposed algorithms noticeably learn dynamic consumer performance. Moreover, the paper illustrates results of Cooperative Reinforcement Learning Algorithms of three shop agents for the period of one year sale duration and then demonstrated the results using proposed approach for three shop agents for the period of one year sale duration. The results obtained by the proposed expert agent based cooperation approach show that such methods can put into a quick convergence of agents in the dynamic environment.
Abstract: The paper gives novel approach Multiagent Cooperative Reinforcement Learning by Expert Agents (MCRLEA) for dynamic decision making in the retail application. Furthermore, it put up different cooperation schemes for multiagent cooperative reinforcement learning i.e. EQ learning, EGroup, EDynamic, EGoal driven and Expert agents scheme. Implementation...
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The Innovation of Communication Planning Idea——Based on the Background of the Application of Artificial Intelligence in Media
Issue:
Volume 6, Issue 6, December 2017
Pages:
85-89
Received:
17 September 2017
Accepted:
10 October 2017
Published:
11 December 2017
Abstract: The idea of planning of communication is getting lost in the context of media convergence, but a hope of improving the humanity of news through technology emerged because of the arrival of the era of intelligent communication. There are still a lot of shortcomings in the current algorithm used in news production, however, intelligent communication has become an irreversible trend, journalists need to change the planning idea as soon as possible. This paper discusses this problem from the following aspects such as driving technology with humanity, from catering to guiding, diversification of news forms, striding across from information attributes to intelligent attributes.
Abstract: The idea of planning of communication is getting lost in the context of media convergence, but a hope of improving the humanity of news through technology emerged because of the arrival of the era of intelligent communication. There are still a lot of shortcomings in the current algorithm used in news production, however, intelligent communication ...
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