Malware variant detection using similarity search over sets of control flow graphs

Static detection of polymorphic malware variants plays an important role to improve system security. Control flow has shown to be an effective characteristic that represents polymorphic malware instances. In our research, we propose a similarity search of malware using novel distance metrics of malware signatures. We describe a malware signature by the set of control flow graphs the malware contains. We propose two approaches and use the first to perform pre-filtering. Firstly, we use a distance metric based on the distance between feature vectors. The feature vector is a decomposition of the set of graphs into either fixed size k-sub graphs, or q-gram strings of the high-level source after decompilation. We also propose a more effective but less computationally efficient distance metric based on the minimum matching distance. The minimum matching distance uses the string edit distances between programs' decompiled flow graphs, and the linear sum assignment problem to construct a minimum sum weight matching between two sets of graphs. We implement the distance metrics in a complete malware variant detection system. The evaluation shows that our approach is highly effective in terms of a limited false positive rate and our system detects more malware variants when compared to the detection rates of other algorithms.

Aesthetic anxieties : the problem of defining the migrant house in Australia

The interest of this paper concerns the problem about the migrant house – why has it been so difficult to define? In order to examine the problem of the lack of literature on the migrant house it is important to look at the literature on the Australian house, and to examine how the migrant house is positioned or not included in this literature. It will approach this through what is accepted as discourse analysis, but with a particular position informed by the work of Stuart Hall on representation.

A fuzzy technique to control congestion in WSN

Congestion in wireless sensor networks (WSNs) is a crucial issue. That is due to the relatively high node density and source-to-sink communication pattern. Congestion not only causes packet loss, but also leads to excessive energy consumption as well as delay. Therefore, in order to prolong network lifetime and improve fairness and provide better quality of service, developing a novel solution for congestion estimation and control is important to be considered. To address this problem, we propose a type-2 fuzzy logic based algorithm to detect and control congestion level in WSNs. The proposed algorithm considers local information such as packet loss rate and delay to control congestion in the network. Simulation results show that our protocol performs better than a recently developed protocol in prolonging network lifetime as well as decreasing packet loss.

Community views on responding to a local asbestos disease epidemic : implications for policy and practice

The Latrobe Valley region of Victoria, Australia, has the highest rate of asbestos disease in the state due to extensive past use of asbestos in the power industry. Current responses to asbestos disease epidemics in Australia and internationally are dominated by medical, scientific, legal and government perspectives. The voices and perspectives of those most directly affected – exposed and diseased workers, their families and communities – are relatively rarely heard.A qualitative interview study was conducted to determine what people in the Latrobe Valley community think could or should be done following their own asbestos disease epidemic. Analysis identified several themes. Notably, these represent a sophisticated community understanding of issues that is largely consistent with state-of-the-art occupational health and public health knowledge.Some themes are well known already, eg the need for fair and timely compensation, adequate healthcare facilities and services, and more education. Others point to neglected possibilities, such as the need for reconciliation and social healing to complement the dominant individual medico-legal focus. Employer suppression of hazard information and denial of asbestos-related disease in past decades continues to have a profound effect on people's views in the present. Reconciliation in some form, eg acknowledgement of or apology for past wrongs, was identified as a necessary first step in developing new and better policy and practice responses; action in this regard has important implications for the implementation and effectiveness of other policy and practice interventions. Further, a need for substantive community participation in the development of policy and practice responses – currently lacking – was identified. Findings suggest that community is an under-recognised and under-utilised resource in responding to a local asbestos disease epidemic.The Latrobe Valley situation is a microcosm of the broader Australian and international story. It offers insights on the perspectives of those most affected by asbestos issues, how such people and their views can be used to strengthen current policy and practice responses, and how their participation is essential to building comprehensive public and social health responses to this global problem.

Gambling participation and problem gambling severity in a stratified random survey : Findings from the second social and economic impact study of gambling in Tasmania

Demographic characteristics associated with gambling participation and problem gambling severity were investigated in a stratified random survey in Tasmania, Australia. Computer-assisted telephone interviews were conducted in March 2011 resulting in a representative sample of 4,303 Tasmanian residents aged 18 years or older. Overall, 64.8 % of Tasmanian adults reported participating in some form of gambling in the previous 12 months. The most common forms of gambling were lotteries (46.5 %), keno (24.3 %), instant scratch tickets (24.3 %), and electronic gaming machines (20.5 %). Gambling severity rates were estimated at non-gambling (34.8 %), non-problem gambling (57.4 %), low risk gambling (5.3 %), moderate risk (1.8 %), and problem gambling (.7 %). Compared to Tasmanian gamblers as a whole significantly higher annual participation rates were reported by couples with no children, those in full time paid employment, and people who did not complete secondary school. Compared to Tasmanian gamblers as a whole significantly higher gambling frequencies were reported by males, people aged 65 or older, and people who were on pensions or were unable to work. Compared to Tasmanian gamblers as a whole significantly higher gambling expenditure was reported by males. The highest average expenditure was for horse and greyhound racing ($AUD 1,556), double the next highest gambling activity electronic gaming machines ($AUD 767). Compared to Tasmanian gamblers as a whole problem gamblers were significantly younger, in paid employment, reported lower incomes, and were born in Australia. Although gambling participation rates appear to be falling, problem gambling severity rates remain stable. These changes appear to reflect a maturing gambling market and the need for population specific harm minimisation strategies. © 2014 Springer Science+Business Media New York.

Control flow-based malware variant detection

Static detection of malware variants plays an important role in system security and control flow has been shown as an effective characteristic that represents polymorphic malware. In our research, we propose a similarity search of malware to detect these variants using novel distance metrics. We describe a malware signature by the set of control flowgraphs the malware contains. We use a distance metric based on the distance between feature vectors of string-based signatures. The feature vector is a decomposition of the set of graphs into either fixed size k-subgraphs, or q-gram strings of the high-level source after decompilation. We use this distance metric to perform pre-filtering. We also propose a more effective but less computationally efficient distance metric based on the minimum matching distance. The minimum matching distance uses the string edit distances between programs' decompiled flowgraphs, and the linear sum assignment problem to construct a minimum sum weight matching between two sets of graphs. We implement the distance metrics in a complete malware variant detection system. The evaluation shows that our approach is highly effective in terms of a limited false positive rate and our system detects more malware variants when compared to the detection rates of other algorithms. © 2013 IEEE.

Rapid Physarum Algorithm for shortest path problem

As shortest path (SP) problem has been one of the most fundamental network optimization problems for a long time, technologies for this problem are still being studied. In this paper, a new method by integrating a path finding mathematical model, inspired by Physarum polycephalum, with extracted one heuristic rule to solve SP problem has been proposed, which is called Rapid Physarum Algorithm (RPA). Simulation experiments have been carried out on three different network topologies with varying number of nodes. It is noted that the proposed RPA can find the optimal path as the path finding model does for most networks. What is more, experimental results show that the performance of RPA surpasses the path finding model on both iterations and solution time. © 2014 Elsevier B.V.

To shut them up or to clarify: restraining the spread of rumors in online social networks

Restraining the spread of rumors in online social networks (OSNs) has long been an important but difficult problem to be addressed. Currently, there are mainly two types of methods 1) blocking rumors at the most influential users or community bridges, or 2) spreading truths to clarify the rumors. Each method claims the better performance among all the others according to their own considerations and environments. However, there must be one standing out of the rest. In this paper, we focus on this part of work. The difficulty is that there does not exist a universal standard to evaluate them. In order to address this problem, we carry out a series of empirical and theoretical analysis on the basis of the introduced mathematical model. Based on this mathematical platform, each method will be evaluated by using real OSN data.We have done three types of analysis in this work. First, we compare all the measures of locating important users. The results suggest that the degree and betweenness measures outperform all the others in the Facebook network. Second, we analyze the method of the truth clarification method, and find that this method has a long-term performance while the degree measure performs well only in the early stage. Third, in order to leverage these two methods, we further explore the strategy of different methods working together and their equivalence. Given a fixed budget in the real world, our analysis provides a potential solution to find out a better strategy by integrating both types of methods together. From both the academic and technical perspective, the work in this paper is an important step towards the most practical and optimal strategies of restraining rumors in OSNs.

Joint resource allocation for max-min throughput in multicell networks

We investigate the resource-allocation problem in multicell networks targeting the max-min throughput of all cells. A joint optimization over power control, channel allocation, and user association is considered, and the problem is then formulated as a nonconvex mixed-integer nonlinear problem (MINLP). To solve this problem, we proposed an alternating-optimization-based algorithm, which applies branch-and-bound and simulated annealing in solving subproblems at each optimization step. We also demonstrate the convergence and efficiency of the proposed algorithms by thorough numerical experiments. The experimental results show that joint optimization over all resources outperforms the restricted optimization over individual resources significantly.

Certificate-based encryption resilient to key leakage

Certificate-based encryption (CBE) is an important class of public key encryption but the existing schemes are secure only under the premise that the decryption key (or private key) and master secret key are absolutely secret. In fact, a lot of side channel attacks and cold boot attacks can leak secret information of a cryptographic system. In this case, the security of the cryptographic system is destroyed, so a new model called leakage-resilient (LR) cryptography is introduced to solve this problem. While some traditional public key encryption and identity-based encryption with resilient-leakage schemes have been constructed, as far as we know, there is no leakage-resilient scheme in certificate-based cryptosystems. This paper puts forward the first certificate-based encryption scheme which can resist not only the decryption key leakage but also the master secret key leakage. Based on composite order bilinear group assumption, the security of the scheme is proved by using dual system encryption. The relative leakage rate of key is close to 1/3.

Nem tão racional assim: cognição social implícita, discriminação trabalhista e ações afirmativas

É um trabalho de interdisciplinaridade entre o Direito e a Psicologia Social dentro da temática da discriminação trabalhista. Será apresentada a visão da Psicologia Social sobre preconceitos automáticos (“Implicit Bias”), assim como será discutida qual seria a melhor solução para o problema exposto. Ações afirmativas é o remédio mais adequado para combater preconceitos automáticos. O trabalho mostra a visão da Psicologia Social sobre a melhor forma de introduzir e conduzir as Ações afirmativas no mercado de trabalho. Será também feita uma análise jurídica constitucional e infraconstitucional das Ações afirmativas no mercado de trabalho, procurando entender se o ordenamento constitucional acolhe estas políticas no Brasil, bem como investigar o interesse do Estado em legislar para tornar tais iniciativas uma realidade.

Activity modeling in practice: a case-based guide to human-centered software engineering

The purpose of this study was to identify whether activity modeling framework supports problem analysis and provides a traceable and tangible connection from the problem identification up to solution modeling. Methodology validation relied on a real problem from a Portuguese teaching syndicate (ASPE), regarding courses development and management. The study was carried out with a perspective to elaborate a complete tutorial of how to apply activity modeling framework to a real world problem. Within each step of activity modeling, we provided a summary elucidation of the relevant elements required to perform it, pointed out some improvements and applied it to ASPE’s real problem. It was found that activity modeling potentiates well structured problem analysis as well as provides a guiding thread between problem and solution modeling. It was concluded that activity-based task modeling is key to shorten the gap between problem and solution. The results revealed that the solution obtained using activity modeling framework solved the core concerns of our customer and allowed them to enhance the quality of their courses development and management. The principal conclusion was that activity modeling is a properly defined methodology that supports software engineers in problem analysis, keeping a traceable guide among problem and solution.

Heurísticas usando construção de vocabuilário aplicadas ao problema da atribuição de localidades a anéis em redes SONET/SDH

The SONET/SDH Ring Assignment Problem (PALAS) treats to group localities in form of some rings, being respected the traffic's limitations of the equipment. Each ring uses a DXC (Digital Cross Connect) to make the communication with the others, being the DXC the equipment most expensive of the net, minimizing the number total of rings, will minimize the total net cost, problem's objective . This topology in rings provides a bigger capacity of regeneration. The PALAS is a problem in Combinatorial Optimization of NP-hard Class. It can be solved through Heuristics and Metaheuristics. In this text, we use Taboo Search while we keep a set of elite solutions to be used in the formation of a part of the collection of vocabulary's parts that in turn will be used in the Vocabulary Building. The Vocabulary Building will be started case Taboo Search does not reach the best solution for the instance. Three approaches had been implemented: one that only uses vocabulary's parts deriving of Taboo Search, one that it only uses vocabulary's parts randomly generated and a last one that it uses half come of the elite and half randomly generated

An efficient neural approach to economic load dispatch in power systems

A neural approach to solve the problem defined by the economic load dispatch in power systems is presented in this paper, Systems based on artificial neural networks have high computational rates due to the use of a massive number of simple processing elements and the high degree of connectivity between these elements the ability of neural networks to realize some complex nonlinear function makes them attractive for system optimization the neural networks applyed in economic load dispatch reported in literature sometimes fail to converge towards feasible equilibrium points the internal parameters of the modified Hopfield network developed here are computed using the valid-subspace technique These parameters guarantee the network convergence to feasible quilibrium points, A solution for the economic load dispatch problem corresponds to an equilibrium point of the network. Simulation results and comparative analysis in relation to other neural approaches are presented to illustrate efficiency of the proposed approach.

Arquitetura multiagente baseada em nuvem de partículas para hibridização de metaheurísticas

This thesis proposes an architecture of a new multiagent system framework for hybridization of metaheuristics inspired on the general Particle Swarm Optimization framework (PSO). The main contribution is to propose an effective approach to solve hard combinatory optimization problems. The choice of PSO as inspiration was given because it is inherently multiagent, allowing explore the features of multiagent systems, such as learning and cooperation techniques. In the proposed architecture, particles are autonomous agents with memory and methods for learning and making decisions, using search strategies to move in the solution space. The concepts of position and velocity originally defined in PSO are redefined for this approach. The proposed architecture was applied to the Traveling Salesman Problem and to the Quadratic Assignment Problem, and computational experiments were performed for testing its effectiveness. The experimental results were promising, with satisfactory performance, whereas the potential of the proposed architecture has not been fully explored. For further researches, the proposed approach will be also applied to multiobjective combinatorial optimization problems, which are closer to real-world problems. In the context of applied research, we intend to work with both students at the undergraduate level and a technical level in the implementation of the proposed architecture in real-world problems