IRJET - Survey on Malware Detection using Deep Learning Methods
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This document discusses various machine learning methods for malware detection, including support vector machines (SVM), random forests, and decision trees. It provides an overview of each method and related works that have applied these techniques. Specifically, it examines analyses that used linear SVM, random forests on Android apps, and an improved decision tree algorithm to classify malware families. The document concludes that machine learning methods have become important for malware detection as signatures alone cannot keep up with new malware variants.
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Metamorphic Malware Analysis and DetectionGrijesh Chauhan ABSTRACT :
--------------------
Modern malware that are metamorphic or polymorphic in nature mutate their code by employing code obfuscation and encryption methods to thwart detection. Thus, conventional signature based scanners fail to detect these malware. In order to address the problems of detecting known variants of metamorphic malware, we propose a method using bioinformatics techniques effectively used for Protein and DNA matching. Instead of using exact signature matching methods, more sophisticated signature(s) are extracted using multiple sequence alignment (MSA). The results show that the proposed method is capable of identifying malware variants with minimum false alarms and misses. Also, the detection rate achieved with our proposed method is better compared to commercial antivirus products used in the study.
Status:
----------
This work has been accepted by 8th IEEE International Conference on Innovations in Information Technology (Innovations'12).
Link:
-------
http://ieeexplore.ieee.org/xpl/login.jsp?reload=true&tp=&arnumber=6207739&url=http://ieeexplore.ieee.org/iel5/6203543/6207707/06207739.pdf?arnumber=6207739
e-mail: [email protected]
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computer systems are increasing. Implementing intrusion detection systems have become extremely
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researchers nowadays due to the dramatic growth in
network-based technologies. Policy violations and
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approaches to improve intrusion detection systems focus on feature selection or reduction since some features are
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and poor in accuracy, there is a need to automatize the bug triage process. In order to automate the bug triage
process, text classification techniques are applied using stopword removal and stemming. In our proposed work
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and keyword selection are used to obtain bug report and words. This will help the system to predict only those
developers who are expertise in solving the assigned bug. We will also provide the change of status of bug
report i.e. if the bug is solved then the bug report will be updated. If a particular developer fails to solve the bug
then the bug will go back to another developer.
20170412 om patri pres 153pdf
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Om P. Patri
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Los Angeles, CA 90089
[email protected]
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Implementation of reducing features to improve code change based bug predicti...eSAT Journals Abstract Today, we are getting plenty of bugs in the software because of variations in the software and hardware technologies. Bugs are nothing but Software faults, existing a severe challenge for system reliability and dependability. To identify the bugs from the software bug prediction is convenient approach. To visualize the presence of a bug in a source code file, recently, Machine learning classifiers approach is developed. Because of a huge number of machine learned features current classifier-based bug prediction have two major problems i) inadequate precision for practical usage ii) measured prediction time. In this paper we used two techniques first, cos-triage algorithm which have a go to enhance the accuracy and also lower the price of bug prediction and second, feature selection methods which eliminate less significant features. Reducing features get better the quality of knowledge extracted and also boost the speed of computation. Keywords: Efficiency, Bug Prediction, Classification, Feature Selection, Accuracy
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[IJET-V1I2P2] Authors :Karishma Pandey, Madhura Naik, Junaid Qamar,Mahendra P...IJET - International Journal of Engineering and Techniques
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on the subject of using machine learning for malware classification to understand the impact of
this technology on malware classification. Throughout this study, we seek to answer three main
research questions: RQ1: Is the application of machine learning for malware classification
growing? RQ2: What is the most common machine-learning application for malware
classification? RQ3: What are the outcomes of the most common machine learning
applications? The analysis of 2186 articles resulting from a data collection process from peerreviewed databases shows the trajectory of the application of this technology on malware
classification as well as trends in both the machine learning and malware classification fields of
study. This study performs quantitative and qualitative analysis using statistical and N-gram
analysis techniques and a formal literature review to answer the proposed research questions.
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or categorize malware by family. Machine learning is a highly researched technology with
many applications, from malware classification and beyond.
J034057065
J034057065ijceronline International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
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Discharge to Boiler:
Pressurized water is then supplied to the boiler drum or economizer section, depending on design.
🌀 Types of Boiler Feed Pumps
Centrifugal Pumps (most common):
Multistage for higher pressure.
Used in large thermal power stations.
Positive Displacement Pumps (less common):
For smaller or specific applications.
Precise flow control but less efficient for large volumes.
🛠️ Key Operations and Controls
Recirculation Line: Protects the pump from overheating at low flow.
Throttle Valve: Regulates flow based on boiler demand.
Control System: Often automated via DCS/PLC for variable load conditions.
Sealing & Cooling Systems: Prevent leakage and maintain pump health.
⚠️ Common BFP Issues
Cavitation due to low NPSH (Net Positive Suction Head).
Seal or bearing failure.
Overheating from improper flow or recirculation.
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