We propose a novel hybrid method to analyze the security vulnerabilities in Android *** method combines static analysis,which consists of metadata and data flow analyses with dynamic analysis,which includes dynamic ex...
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We propose a novel hybrid method to analyze the security vulnerabilities in Android *** method combines static analysis,which consists of metadata and data flow analyses with dynamic analysis,which includes dynamic executable scripts and application program interface *** hybrid method can effectively analyze nine major categories of important security vulnerabilities in Android *** design dynamic executable scripts that record and perform manual operations to customize the execution path of the target *** dynamic executable scripts can replace most manual operations,simplify the analysis process,and further verify the corresponding security *** successfully statically analyze 5547 malwares in Drebin and 10 151real-world *** average analysis time of each application in Drebin is 4.52 s,whereas it reaches 92.02 s for real-word *** system can detect all the labeled vulnerabilities among 56 labeled *** dynamic verification shows that our static analysis accuracy approximates 95%for real-world *** show that our dynamic analysis can effectively detect the vulnerability named input unverified,which is difficult to be detected by other *** addition,our dynamic analysis can be extended to detect more types of vulnerabilities.
Two undergraduate students will work with the faculty to implement machine learning models on extracting meaningful information from building sensor data. As a part of the LU-PRISM (Longwood University Perspectives on...
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Peer-to-peer (P2P) systems enable users to share resources in a networked environment without worrying about issues such as scalability and load balancing. Unlike exchange of goods in a traditional market, resource ex...
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Peer-to-peer (P2P) systems enable users to share resources in a networked environment without worrying about issues such as scalability and load balancing. Unlike exchange of goods in a traditional market, resource exchange in P2P networks does not involve monetary transactions. This makes P2P systems vulnerable to problems including the free-rider problem that enables users to acquire resources without contributing anything, collusion between groups of users to incorrectly promote or malign other users, and zero-cost identity that enables nodes to obliterate unfavorable history without incurring any expenditure. Previous research addresses these issues using user-reputation, referrals, and shared history based techniques. Here, we describe a multi-agent based reciprocity mechanism where each user's agent makes the decision to share a resource with a requesting user based on the amount of resources previously provided by the requesting user to the providing user and globally in the system. A robust reputation mechanism is proposed to avoid the differential exploitations by the free-riders and to prevent collusion. Experimental results on a simulated P2P network addresses the problems identified above and shows that users adopting the reciprocative mechanism outperform users that do not share resources in the P2P network. Hence, our proposed reciprocative mechanism effectively suppresses free-riding. Copyright 2005 ACM.
We study agent societies where self-interested agents interact repeatedly over extended time periods. In particular, we are interested in environments where agents can form mutually beneficial relationships by exchang...
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We study agent societies where self-interested agents interact repeatedly over extended time periods. In particular, we are interested in environments where agents can form mutually beneficial relationships by exchanging help but an agent would rather receive help than give it. Evolutionary tournaments with competing help-giving strategies can model scenarios where agents periodically adopt strategies that are outperforming others in the population. Such experiments, however, can be computationally costly and hence it is difficult to prescribe a rational strategy choice given environmental conditions like task mix, strategy distribution in the population, etc. A preferred approach, pursued in this paper, is to analytically capture the dynamics of the strategy mix in the population under an evolutionary tournament. Such an analytical model can be used to predict the evolutionarily dominant strategy, the rational strategy choice. Copyright 2005 ACM.
Multiple Sequence Alignment (MSA) is among the best important tasks in computational biology. Genetic Algorithms are stochastic approaches for efficient and robust search. With the increasing rate of determination of ...
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ISBN:
(纸本)9781627482769
Multiple Sequence Alignment (MSA) is among the best important tasks in computational biology. Genetic Algorithms are stochastic approaches for efficient and robust search. With the increasing rate of determination of sequences, the emphasis in biological sequence comparison has turned to the simultaneous alignment of several sequences. The consistency of align able sequence fragments for a set of sequences can be implemented by using dot matrices. A set of algorithms has been developed (a) to discover the consistency and have MSA using dot matrices (CDM), (b) to construct the MSA algorithm (CSAA) based on an extension of CDM ,(c) to create a MSA algorithm based on the divide and conquer method and the use of a Genetic Algorithm (DCGA). CDM with its internal phase can minimize the number of pair wise comparisons in MSA giving better performance. CDM is also efficient in finding conserved motifs (short sequence segments that are important in protein structure) among very distantly related sequences and using also CSAA that can be used for the MSA. Finally, the DCGA framework that contains CDM or CSAA and Genetic Algorithm (GA) provides better performance than the GA. Simulation experiments are produced.
In many negotiation and bargaining scenarios, a particular agent may need to interact repeatedly with another agent. Typically, these interactions take place under incomplete information, i.e., an agent does not know ...
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In many negotiation and bargaining scenarios, a particular agent may need to interact repeatedly with another agent. Typically, these interactions take place under incomplete information, i.e., an agent does not know exactly which offers may be acceptable to its opponent or what other outside options are available to that other agent. In such situations, an agent can benefit by learning its opponent's decision model based on its past experience. In particular, being able to accurately predict opponent decisions can enable an agent to generate offers to optimize its own utility. In this paper, we present a learning mechanism using Chebychev's polynomials by which an agent can approximately model the decision function used by the other agent based on the decision history of its opponent. We study a repeated one-shot negotiation model which incorporates uncertainty about opponent's valuation and outside options. We evaluate the proposed modeling mechanism for optimizing agent utility when negotiating with different class of opponents. Copyright 2005 ACM.
As a fundamental and ubiquitous combinatorial notion, species has attracted sustained interest, generalizing from set-theoretical combinatorial to algebraic combinatorial and beyond. The Rota-Baxter algebra is one of ...
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We study the problem of broadcasting in a system where nodes are equipped with radio transmitters with constant radius of transmission. A message originating at a node has to be transmitted to all the other nodes in t...
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As the metaverse grows in popularity and complexity, securing its virtual environment is critical. Metaverse intrusion detection involves identifying and preventing unauthorized access, malicious activities, and poten...
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The authors have derived what they termed quasi-Newton multi step methods in [2]. These methods have demonstrated substantial numerical improvements over the standard single step Secant-based BFGS. Such methods use a ...
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