WarHammer

WarHammer

Prateek Kumar

Prateek Kumar

Bengaluru, Karnataka

Implementation of machine Learning in cyber security.

Networking, Modern Code, Artificial Intelligence

Description

With modern technology becoming obsolete in cyber security, we need to come up with innovative ideas to increase the notch up in the fight against unauthorized access to our information and data. I will be taking help of Theano,Torch,Microsoft CNTKA framework to start with and then modify the implementation as i move in later stages of the project. I will be trying to modify various protection algorithms and try to come up with a ML algorithm that takes help of deep learning frameworks. I will also be taking help of Gensim to validate my data collection as efficiently as possible. The changes to encryption and decryption techniques will be studied thoroughly in later stages. In a manner where DNA cannot be replicated , similarly developing a neural network net around my algorithm against network intrusion will be the deciding factor in the efficiency of the modified algorithm. I will also taking help of Apache Hadoop in analyzing my stored data from cyber attacks and storing the result of my predicament in Apache Spark to make real-time decisions. The main problem in using machine learning algorithms in cyber security is the creation of an overhead which has an adverse effect on algorithm's performance. To eliminate it to a appreciable stage I will be taking help of BlueData without any changes to Hadoop or Spark framework. Finally the performance of the algorithm will be tested with the help of BigBench kit to get a full report on the project.

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Prateek K. updated project WarHammer status

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Prateek Kumar

For security providers, sophisticated machine learning provides fast and accurate threat detection, including zero-day and previously unknown threats. Advanced heuristics and rules allow machine learning models to help determine in near real time if a file, URL, IP, or application is a threat, and then communicate that information broadly.

Despite an ever changing threat landscape, machine learning enables very high detection rates over time. Machine learning technology is now emerging as a critical component across all of the various domains of cybersecurity.

Machine learning can power next-generation endpoint protection, mobile protection, threat intelligence, web security and network anomaly detection offerings. It enables threat activity across multiple security domains to be contextually associated in real time.

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Prateek K. added photos to project WarHammer

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WarHammer

With modern technology becoming obsolete in cyber security, we need to come up with innovative ideas to increase the notch up in the fight against unauthorized access to our information and data. I will be taking help of Theano,Torch,Microsoft CNTKA framework to start with and then modify the implementation as i move in later stages of the project. I will be trying to modify various protection algorithms and try to come up with a ML algorithm that takes help of deep learning frameworks. I will also be taking help of Gensim to validate my data collection as efficiently as possible. The changes to encryption and decryption techniques will be studied thoroughly in later stages. In a manner where DNA cannot be replicated , similarly developing a neural network net around my algorithm against network intrusion will be the deciding factor in the efficiency of the modified algorithm. I will also taking help of Apache Hadoop in analyzing my stored data from cyber attacks and storing the result of my predicament in Apache Spark to make real-time decisions. The main problem in using machine learning algorithms in cyber security is the creation of an overhead which has an adverse effect on algorithm's performance. To eliminate it to a appreciable stage I will be taking help of BlueData without any changes to Hadoop or Spark framework. Finally the performance of the algorithm will be tested with the help of BigBench kit to get a full report on the project.

Medium 1

Prateek K. created project WarHammer

Medium 355b551b 3cf2 4f7d 979c d7303d310e2e

WarHammer

With modern technology becoming obsolete in cyber security, we need to come up with innovative ideas to increase the notch up in the fight against unauthorized access to our information and data. I will be taking help of Theano,Torch,Microsoft CNTKA framework to start with and then modify the implementation as i move in later stages of the project. I will be trying to modify various protection algorithms and try to come up with a ML algorithm that takes help of deep learning frameworks. I will also be taking help of Gensim to validate my data collection as efficiently as possible. The changes to encryption and decryption techniques will be studied thoroughly in later stages. In a manner where DNA cannot be replicated , similarly developing a neural network net around my algorithm against network intrusion will be the deciding factor in the efficiency of the modified algorithm. I will also taking help of Apache Hadoop in analyzing my stored data from cyber attacks and storing the result of my predicament in Apache Spark to make real-time decisions. The main problem in using machine learning algorithms in cyber security is the creation of an overhead which has an adverse effect on algorithm's performance. To eliminate it to a appreciable stage I will be taking help of BlueData without any changes to Hadoop or Spark framework. Finally the performance of the algorithm will be tested with the help of BigBench kit to get a full report on the project.

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