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You wake up one nice morning and open up your laptop to check the mails and find out that you won a lottery worth $1 million. “Wow”, you say and get excited, you open it up, it demands for some of your personal details but who cares for tiny details when you are getting a million dollars. Or you might have got an email for security updates. There my friend, you are just another victim to those cyber criminals. This is just a very basic example of cyber crime. How to solve such problems? Here comes in Natural Language processing for our rescue. NLP has various applications in Cyber security. Lets break it down to the three utmost areas were NLP can solve infosec problems. 1. Domain Generation Algorithm classification (DGA or domain generation algorithm is a specific method deployed by cyber attacker to generate large number of domains which can be used as points (Command and control servers )of propagating malicious code To detect Advanced Persistent Threat(APT) ...

Introduction The term entity refers to a thing or a being with unique & independent existence. Named entity thus refers to a proper noun uniquely identifying an object. Named Entity Recognition (NER) refers to the process of identification of named entities and their classification into categories like person names, organisational names, locations, dates, time, monetary descriptions etc. NER plays an integral part in Information Extraction in NLP. Approaches NER identification can be broadly classified into two approaches as follows: Rule-based (Linguistic) approach This approach consists of a set of hand-crafted rules derived from a language’s grammatical and syntactical features. For example, consider a simple rule to identify person , office & organization in a text. [person] , [office] of [organization] Vuk Draskovic , leader of the Serbian Renewal Movement Machine Learning (Statistical) approach Th...

With the extensive use of e-commerce platforms, online reviews have emerged as a major criteria for critical decision making regarding product purchases, design and business. At the same time, it has also become a way to promote or defame a target product or services by posting fake reviews and giving unfair ratings. Such reviewers are called opinion spammers and their activities are called opinion spamming . In recent years, researchers have also studied the problem and proposed several techniques. However, the problem is still wide open. Unlike many other forms of spamming, the key difficulty for solving the opinion spam problem is that it is hard to find gold-standard data of fake and non-fake reviews for building a model. But since it is also difficult to manually label each review as true or fake, some automation is needed. This blog intend to mention few of the tactics to filter out fake reviews from the chunk. Various heuristic approaches divided into 3 phases are used...