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[PDF] Top 20 An evaluation of N gram system call sequence in mobile malware detection

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An evaluation of N gram system call 
		sequence in mobile malware detection

An evaluation of N gram system call sequence in mobile malware detection

... The Linear SVM applies in this experiment is the L2-SVM classifier from the Liblinear package and the experiment is performed using Weka 3.7.10 [26]. The experiment is done on Windows 7 that runs on a desktop computer ... See full document

5

An Approach on the Distributed Detection Algorithm for Wormholes in Wireless Network Systems

An Approach on the Distributed Detection Algorithm for Wormholes in Wireless Network Systems

... in mobile ad-hoc network to provide protected communication between mobile ...the evaluation of the Sequence Number Attack Detection and countermeasure ...wormhole detection ... See full document

7

Android Based Malware Propagation and Detection in Mobile System

Android Based Malware Propagation and Detection in Mobile System

... A new static-evaluation framework to facilitate vulnerability discovery for apps by means of extracting detailed and precise information from apps and easy for the identification process. Moreover, the framework ... See full document

5

Ranking fraudulent and malware detection for mobile apps

Ranking fraudulent and malware detection for mobile apps

... Signature-based detection works by scanning the contents of computer files and cross- referencing their contents with the ―code signaturesǁ belonging to known ...user’s system from ...based detection ... See full document

7

Android Mobile Malware Classification using Tokenization Approach based on System Call Sequence

Android Mobile Malware Classification using Tokenization Approach based on System Call Sequence

... Android mobile malware classification based on system call sequence patterns expected to exploit user call logs using tokenization ...new system call ... See full document

6

Mobile Malware Detection using Anomaly Based Machine Learning Classifier Techniques

Mobile Malware Detection using Anomaly Based Machine Learning Classifier Techniques

... an evaluation using machine- learning classifiers to effectively detect mobile malware by choosing the appropriate networking features for classifier inspections, as well as to find the ideal ... See full document

8

SVM Based Effective Malware Detection System

SVM Based Effective Malware Detection System

... opcode sequence the accuracy is ...opcode n- gram sequences for categorizing malicious and benign files with different feature selection and classification ...good detection rate while keeping ... See full document

5

Ranking fraudulent and malware detection for mobile apps

Ranking fraudulent and malware detection for mobile apps

... Signature-based detection works by scanning the contents of computer files and cross- referencing their contents with the ―code signaturesǁ belonging to known ...user’s system from ...based detection ... See full document

7

Survey on Host Based Ids System by Using Data Mining and Forensics Technique

Survey on Host Based Ids System by Using Data Mining and Forensics Technique

... This system can be used to detect the host intrusion detection where host machine comprises the confidential ...the system and updated files can be recovered by system. This system can ... See full document

6

Host Based Internal Intrusion Detection and Protection System

Host Based Internal Intrusion Detection and Protection System

... of system, As using intrusion detection systems and firewalls identify and isolate harmful behaviors generated from the outside world we can find out internal attacker of the system ...that ... See full document

7

Secure +, An Intrusion Detection System

Secure +, An Intrusion Detection System

... corporate system and need to join the Wi-Fi, your Personal Computer will be appointed with an , in any case, your Internet Protocol address may look like ... See full document

6

Behavior Classification based Self-learning Mobile Malware Detection

Behavior Classification based Self-learning Mobile Malware Detection

... more mobile malware appears on mobile internet and pose great threat to mobile ...anti-malware system to detect the polymorphic and metamorphic mobile ...A mobile ... See full document

8

Class Based n gram Models of Natural Language

Class Based n gram Models of Natural Language

... We estimate the parameters of an n-gram model by examining a sample of text, t~, which we call the training text, in a process called training.. To estimate the parameters of an n-gram m[r] ... See full document

14

Malware Detection using Windows API Sequence and Machine Learning

Malware Detection using Windows API Sequence and Machine Learning

... The rule generator uses the proposed Association mining algorithm to generate classification rules which consists of the integer sequence 4gram, support value, confidence value and the c[r] ... See full document

5

Polymorphic malware detection using sequence classification methods and ensembles

Polymorphic malware detection using sequence classification methods and ensembles

... detect malware in a manner robust to attacker ...gene sequence classifier for malware ...gene sequence classification tools are suitable for classifying malware, we apply Strand to ... See full document

12

Detection of Malicious Android Mobile Applications Based on Aggregated System Call Events

Detection of Malicious Android Mobile Applications Based on Aggregated System Call Events

... Malicious System Call Events Among the malicious app, BaseBridge and ArtifactDataCable app can change the Wi-Fi option without the user knowing and another application is additionally installed to damage by ... See full document

6

A Data Mining Based Malware Detection Model using Distinct API Call Sequences

A Data Mining Based Malware Detection Model using Distinct API Call Sequences

... 430 malware and 200 benign ...operating system and other application software, immediately after their fresh ...The malware sample are collected from VX heaven virus collection dataset and other ... See full document

7

Intrinsic Plagiarism Detection using N gram Classes

Intrinsic Plagiarism Detection using N gram Classes

... When it is not possible to compare the suspi- cious document to the source document(s) plagiarism has been committed from, the evi- dence of plagiarism has to be looked for in- trinsically in the document itself. In this ... See full document

6

Parallel DNA Sequence Approximate Matching with Multi Length Sequence Aware Approach

Parallel DNA Sequence Approximate Matching with Multi Length Sequence Aware Approach

... random sequence generator was used to generate a random target ...parallel n-gram, and dynamic programming have a close performance when the sequence length was short as Min value (45 long), ... See full document

6

Survey on representation techniques for malware detection system

Survey on representation techniques for malware detection system

... the detection algorithm used by a malware detector, they can better target their evasion ...the detection algorithm is important (Christodorescu and Jha, ...used malware and this certainly ... See full document

21

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