The invention belongs to the technical field of information safety and discloses an android malicious software detecting platform oriented to the mobile internet. The Android malicious software detecting platform aims at overcoming the shortcoming that existing Android malicious software lacks dynamic detection, and the Android malicious software detecting platform achieves organic combination of static detection and dynamic detection of the Android malicious software. An application program is installed into an Android sandbox, the Android application program is automatically started and controlled, and various practical operations of a user to the application program are simulated. The operation situation of the application program is monitored to obtain API information of the application program and various dynamic behaviors so as to achieve the aim of detecting the malicious behaviors of the application program. According to the android malicious software detecting platform oriented to the mobile internet, the operations of the user to the application program in the Android sandbox is simulated, the practical behaviors of the application program are practically restored, and the efficiency and accuracy of the Android malicious software detection are improved.

Android malicious software detecting platform oriented to mobile internet

The invention relates to a distributed monitoring system based on a hadoop cluster and a method thereof The system comprises a collection agency module, a resource updating module, a network monitoring module, a collection scheduling module, a warning generating module and a hadoop cluster monitoring module, wherein the hadoop cluster monitoring module is connected with the collection agency module, the resource updating module, the network monitoring module, the collection scheduling module and the warning generating module The method includes the steps of (1) collecting needed detection resources and all index information, (2) storing and caching the collected data, (3) scheduling and updating collected information, (4) timely sampling to generate a data graph needed by a foreground web, (5) monitoring the network state of nodes in the cluster, and (6) determining whether a warning needs to be sent out according to a threshold value and generating warning information if the warning is needed The distributed monitoring system can serve as an independent cluster monitoring system, a basic index database can be conveniently extended, a current index value can be taken back, a convergence view of indexes can be checked, and the trend of data timing sequences can be observed

Distributed monitoring system based on hadoop cluster and method thereof

In a distributed file system with a number of replicas of a file set to two or more replicas, a computer sets a timer to track a time since a last access to the file, wherein the replicas of the file are distributed across two or more nodes within the distributed file system. Responsive to an access to the file prior to the timer reaching a first timer window threshold, the computer resets the timer. Responsive to the timer matching a first timer window threshold, the computer automatically reduces a number of replicas of the file within the distributed file system, wherein the probability that the file will be accessed prior to the first timer window threshold is greater than the probability that the file will be accessed after the first timer window threshold.

Dynamically adjusting the number of replicas of a file according to the probability that the file will be accessed within a distributed file system

The invention discloses a method for anomaly detection in a MapReduce environment. The method comprises the following steps of: 1, acquiring performance index values of each slave node in the MapReduce environment, and transmitting the performance index values to a master node; 2, categorizing the acquired performance index values of each slave node according to the categories of performance indexes; 3, clustering each category of performance index values of each slave node by using a density-based clustering method; and 4, analyzing clustering results, and recording and outputting an obtained anomalous result. Algorithms are simple, and the method is low in complexity; and in a task execution process, reported data can be instantly analyzed and processed without waiting for the finishing of task execution, the anomalous result can be instantly output, and online detection can be realized, so that the method is high in real-time performance.

Method for anomaly detection in MapReduce environment

The invention discloses a BLOG system running method based on cloud storage, and belongs to the technical field of cloud computing. The BLOG system running method is mainly implemented by a blog system and a cloud storage system, wherein the cloud storage system consists of a distributed file system and a virtualized server cluster; the distributed file system adopts an HADOOP and comprises Hadoop distributed file system (HDFS) architecture at bottom layer, a MapReduce algorithm at the upper layer, and a client; the HADOOP receives a request from the client; a NameNode of the HDFS responds to the request and distributes specific operation tasks onto subordinate DateNode; and according to MapReduce function calculation, a final result is obtained and returned to the client. According to the BLOG system running method, the technical problems that a server wastes resources, is low in storage efficiency, is low in system running speed and the like during storage of the blog system are solved, and the problems of the scalability of a storage space of the server and the safety and the stability of storage resources in the server are solved.

BLOG system running method based on cloud storage

The invention discloses a distributed monitoring system based on a Hadoop cluster and a distributed monitoring method thereof. The system comprises a client, a master computer and a slave computer. The system is characterized in that: the master computer and the slave computer in the system are constructed based on a MapReduce framework of the Hadoop cluster; the client is used for submitting a monitoring work request to the master computer; after responding to the monitoring work request, the master computer divides monitoring work and distributes the divided monitoring work to the salve computer for independent completion; and the slave computer is used for completing an independent monitoring work task, integrating a result through the master computer and returning data to the client. By the invention, data such as the task schedule and the like of a distributed computing network MapReduce can be effectively monitored, the monitoring result can be precisely obtained in real time, and independent dynamic switching is realized under the condition of not influencing work execution.

Distributed monitoring system based on Hadoop cluster and monitoring method thereof

The invention discloses an active response system based on intrusion detection for Android mobile phones. The active response system and an intrusion detection system of Android (IDSA) share the same system architecture, namely a client side-server side mode, and the system is simple in structure and easy to implement. The client side is responsible for acquiring application program information installed by users from users' mobile phones, primarily estimating the application program according to abnormal behaviors when receiving the abnormal information detected by IDSA, sending the uncertain suspicious program information to the server side, waiting for receiving feedback information after being analyzed by the service side and taking appropriate active response measurements according to the final feedback results. The server side is mainly responsible for receiving request information about reanalyzing the suspicious programs of the client side, performing static analysis to executable files in the information, providing the final analysis results according to data and methods and finally sending the results to the client side.

Active response system based on intrusion detection for Android mobile phones

The recent next-generation sequencing machines generate sequences at an unprecedented rate, and a sequence is not short any more called read. The reference sequences which are aligned reads against are also increasingly large. Efficiently mapping large number of long sequences with big reference sequences poses a new challenge to sequence alignment. Sequence alignment algorithms become to match on two big data. To address the above problem, we propose a new parallel sequence alignment algorithm called Bwasw-Cloud, optimized for aligning long reads against a large sequence data (e.g. the human genome). It is modeled after the widely used BWA-SW algorithm and uses the open-source Hadoop implementation of MapReduce. The results show that Bwasw-Cloud can effectively and quickly match two big data in common cluster.

Bwasw-Cloud: Efficient sequence alignment algorithm for two big data with MapReduce

The invention discloses a method for designing an HBase enumeration system based on low latency. When data needing to be stored in an HBase is variable in limited types and lots of data items exist, if the data is directly stored in the HBase, quite large redundancy can be caused, and huge storage space cost and data retrieval cost can be caused. The method disclosed by the invention is used for providing optimization of HBase data storage and data retrieval by adopting a mechanism named as an enumeration system. When the data is written into the HBase, firstly the to-be-stored data is mapped into an internal storage type by an internal mapping strategy of the enumeration system, and the transformed data is stored in a data table. To maintain the consistency of data transformation, one data mapping table needs to be maintained in the internal memory of each node so as to provide the transformation between the internal data and actual data during data retrieval.

Method for designing HBase enumeration system based on low latency

The invention discloses a method for high-concurrence access based on multi-node backup HBase data. The method comprises the following steps of when hotspot data which is barely changed exists in the HBase for a long time, initiatively making two or more substitutes for the hotspot data; when the data is changed, executing alteration operation in a data body and synchronizing the alteration operation to each substitute; initiatively evenly allocating read applications to each node where the substitute data is when reading data so as to guarantee high concurrence of data reading, reduce the possibility that the hotspot data becomes a choke point and simultaneously reduce the possibility of over-heated storage nodes caused by aggregation of the hotspot data, so the read-write speed of the data is increased and the stability and performances of a system are guaranteed.

High-expansibility database design method based on Hbase

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