T-Alert: Analyzing Terrorism Using Python

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1 T-Alert: Analyzing Terrorism Using Python Neha Mhatre 1, Asmita Chaudhari 2, Prasad Bolye 3, Prof. Linda John 4 1,2,3,4 Department of Information Technology,St. John College Of Engineering and Management. Abstract In recent times, many newsgroups post their news headlines as short messages in micro blogging services such as Twitter, Facebook etc, Nowadays Twitter is the among most active and visited site worldwide lead to a huge repository of information. This information when extracted in a user readable format can prove useful for users to find security related information. This proposed system uses K-nearest neighbor classification algorithm for classification and optimization is performed based on Firefly Algorithm. Finally, an alert message is given to the user in the form of SMS so that they can avoid mishaps or going to the terror threat areas. The Performance of the system is measured with respect to Precision, Recall and Accuracy. Keywords Data Mining;Terrorism;Twitter;Twitter API I. INTRODUCTION Data mining uses the extraction of hidden predictive information from large database. It is defined as a process used to extract working data from larger set of any raw of data. In efficient analysis, data mining algorithm, simplyfing business decision making and other information requirements to ultimately cut costs and increase revenue. It is a systematic use of violence to terrify a population or government for political, religious goals. Social media has become such a crucial part of our lives that we find it almost impossible to survive without it. This system could warn people of terrorist strikes by text messages. The popularity of Social networks, including news providers used to split their news in various social networking sites and web blogs. The system is an early collects and analyses real time news of events such as terrorist attack, bomb blast etc. From Twitter and detects a target events. Data mining techniques is used to train the data.the data is training using KNN (knearest neighbor algorithm) data mining techniques. Use of social media like twitter sends awareness notification to peoples about terrorism activities. From that alerts, peoples can survive and avoid going that places. Social media sites blogs tweets about terrorism moments. User have to logging on twitter and follows newsgroups. We must use k-nearest neighbor, Firefly algorithm used for classification and optimization tweets. If user has online they will get notification by tweets on twitter and If user has offline, They will get terrorism alert notification by SMS. But users need to register their mobile number on newsgroups. The main purpose of our project is to give information about terrorism activities by twitter. II. LITERATURE SURVEY In this section, we will look at several face enabled mouse system that has been researched and implemented by other researchers. A brief survey of the following was made and these aggregated together which lead to our proposed system. It will give various alternatives to develop the system.. A. T-Vigilant: To Unmask Radical Attacks and Halt The Innocents: In this paper, they were proposed of terrorist attack by text message. In India many news groups place their news on twitter micro blogging service provider which provides real nature to the system. Machine learning techniques are used in this system. The system is early predecessor that collects and analysis real time news of events such as terrorist attacks, bomb blast, hijack etc. Data was trained using KNN algorithm.k nearest neighbors is a simplest algorithm that kept all available DOI: /IJRTER N3HEB 419

2 cases and classifies new cases based on a similarity measure. Data is collected from social media like Facebook; twitter provides real time happenings to great extent. Terrorism has been chosen as domain of study. Social media are widely used among terrorists to communicate and disseminate their activities. User-to-user interaction leads to the formation of complex networks in the terrorism domain. In this list which is crowd-sourced feature of twitter is to utilize in recognized the topical experts. In this system, whenever attack is done that video is found. For collecting tweets topical experts extraction method is used. Terrorism is a complex situation and the terrorist group are highly dynamic in terms of their unity and position.they developed a novel system that automatically collects the new data and analyse that data in real time for terrorism incident. They developed a safety assessment formula based on terrorism incident and time. Using formula to calculate the safety level of countries and cities to estimate their risks.[1] B. Identifying Trolls and Determining Terror Awareness Level in Social Networks Using a Scalable Framework: Trolls in social media are malicious users trying to propagate an opinion to the general perceptions. In this they were used troll detection method for identifying the trolls in social media. In this paper, they present a solution for troll detection and also the results of measuring terror awareness among social media users. They used Twitter platform only, and applied several machine learning techniques and big data methodologies. For machine learning they used k-nearest Neighbour (knn), Naive Bayes, and C4.5 decision tree algorithms. Hadoop/Mahout and Hadoop/Hive platforms were used for big data processing. Machine learning algorithm is to classify twitter users as troll or nontroll. Data collection is performed by mechanism in order to enable us to collect twitter data. To collect data set twitter rest API is used.[2] C.Named-Entity Techniques for Terrorism Event Extraction and Classification: In this paper they were compared several machine learning methods for implementing a Thai terrorism event extraction system. The main function of the system is to extract information related to terrorism events found in Thai news articles.the machine learning algorithms use for extracting event include Naive Bayes (NB), K Nearest Neighbor (KNN), Decision Tree (DTREE) and Support Vector Machines (SVM).precision measures the reliability of extracting information. Recall measures the amount of the relevant information that correctly extract from the test collection. Machine learning algorithms are suitable for information extraction investigation.[3] III. PROPOSED SYSTEM Basically the tweets which we extract from the twitter related to terror attack using the keyword are preprocessed using the K-Nearest Neighbor Algorithm. When the tweets are classified using the K- Nearest Algorithm based on accuracy and precision are optimized. When the optimization is done the alert message is sent to the users and users are All Rights Reserved 420

3 3.1 System Architecture Figure 1. System architecture 3.2 Description Tweets Extraction As we all know Twitter is the huge collection repository when it comes to data. When we talk about tweets the first thing which comes into mind is the Twitter. There are different social media sites such as Twitter, Face book etc. from where we can get the large amount of data. In our proposed system we have selected Terrorism as a Domain of study and data which we required to alert the users will be extracted from the Twitter. With the help of keyword we will extract the tweets related to terrorism and train the data accordingly Tweets Processing Large amount of Data would be extracted from the Twitter. So the data which is been extracted must be converted in the correct form so that we can train the data according to our system requirements. Pre-processing phase consists of two parts Data Cleaning and Data Transformation. Data extracted sometimes would not be in the correct form which means it can contains some null values, missing parameters. So such kind of data cannot be trained so Data Cleaning is required. Once the Data is cleaned it has to be transformed in the right manner and trained KNN Algorithm Once the Pre-Processing of the data is done we need to classify the data based on some parameters. For Classification process k-nearest Neighbor (knn) is being used in our proposed system. There are different classifications of algorithm but knn algorithm is easy to implement amongst all for classification purpose Accuracy & Precision When tweets are classified using the K-nearest neighbor algorithm based on accuracy and precision are All Rights Reserved 421

4 3.2.5 Optimization In the previous papers, there was no optimization level. So in this, we are using optimization level for the Optimize tweets using firefly algorithm. IV. DESCRIPTION OF ALGORITHMS 4.1 K-Nearest Neighbors Algorithm K-nearest neighbor classification is a normal non-parametric classifier, which is used in many Classification problems. It is based on the measuring the test data and each of the training data to decide the final classification output. In knn, the training data set is stored, so that classification for a new unclassified record may be found by comparing it to most similar records in the training set. 1. knn can get very computationally expensive when trying to determine the nearest neighbors on a large data set. 2. Noisy data can throw of knn classifications. 3. Features with a larger range of values can dominate the distance metric relative to features that have a smaller range, so feature scaling is important 4. In knn generally data process is postpone and it requires greater storage requirements. 5. In KNN accuracy selecting a good distance metric is critical. KNN builds no such classification model. Instead, it just stores the labelled Training data. When new unlabeled data comes in, KNN operates in 2 basic steps: a. First, it looks at the k closest labeled training data points in other words, the k-nearest neighbors. b. Second, using the neighbors classes, knn gets a better idea of how the new data should be classified KNN algorithm steps:- It has a training data set. For every training example is xi, 1. Find the K nearest neighbors algorithm based on the Euclidean distance n. 2. Return class that represents the maximum of the k instances n. 3. If actual class!= predicted class then apply gradient descent n. 4. Error = Actual Class Predicted Class n. 5. For every Wk 6. Wk = Wk + α * Error * * Error * Wk (where Wk is the query attribute value) is the query attribute value). 7. Calculate the accuracy as Accuracy = (# of correctly classified examples / # of training examples) X Repeat the process till required validity is reached. 9. Return the class that represents the maximum of the k instances and also Calculate the accuracy as n Accuracy = (# of correctly classified examples / # of testing examples) X Firefly Algorithm Firefly algorithm is a nature-inspired met heuristic algorithm inspired by the flashing behaviour of fireflies. It is originally proposed for continuous problems. However, due to its effectiveness and success in solving continuous problems, different studies are conducted in modifying the algorithm to suit discrete problems. There is lots of engineering as well as optimization problems from other training involve individual variables. Recent reviews on the application and modifications of firefly algorithm mainly focus on continuous problems. This paper is devoted to the detailed. Ffirefly algorithm is used for the optimization problems with binary; integer as well as mixed variables will be discussed. Possible future works will also be highlighted. Firefly algorithm inspired by All Rights Reserved 422

5 behaviour of flashing bugs also called fireflies. It is proposed for optimization problems with continuous variables. Randomly generated feasible solutions will be considered as fireflies where their brightness is determined by their performance on the objective function. The algorithm is guided by three rules: 1. The first rule is that fireflies are unisex, which means any firefly can be attracted to any other firefly. 2. The second rule is the brightness of a firefly depends on its performance in the objective function. 3. The attraction of a firefly depends on its brightness and decreases with distance. This means, since we are considering a minimization. V. FUTURE SCOPE The scope of the project is to provide high level of security to an ordinary system, providing help in all day to day log in activities. our system applies data mining techniques to deliver high accuracy without the administrative overhead and technology weakness of older techniques. Terrorism has been chosen as the domain of study.. List, which is a crowd-sourced feature of Twitter, is utilized in recognizing the topical experts.speculating the significance and enormous generation of OSN data during disasters like floods, epidemics, attacks etc. narrating real-time information, many studies have utilized Twitter feeds (tweets) to generate intelligence in various ways. The study focused on information extraction from tweets automatically. authors focused on categorizing incoming tweets into personal, informative (direct), informative (indirect) categories to segregate actionable tweets. VI. CONCLUSION In our system, we are using the offline text SMS to the alert peoples from preventing to go the terror strike areas. When terrorist attack takes place, this system will give notification to us about terrorist activities. Classifier is used for identify that terrorist events and finally we do our system which is send a terrorism alert in form of SMS to registered users. REFERENCES I. K.P.Shirsat, P.J.Koyande, T - Vigilant: To Unmask Radical Attacks and Halt the Innocents, in proceeding of 1st International Conference on Next Generation Computing Technologies (NGCT-2015) Dehradun,India, 4-5 September 2015, pp II. B.Mutlu, E.Dogdu, K.Oztoprak,and M.Mutlu Identifying Trolls and Determining Terror Awareness Level in Social Networks Using a Scalable Framework,in Proceedings of IEEE International Conference on Big Data (Big III. Data),2016, pp. 1-7 C.Haruechaiyasak, P.Meesad and U.Inyaem,, Named-Entity Techniques for Terrorism Event Extraction and Classification,in proceeding of Eighth International Symposium on Natural Language Processing,2009, pp IV. G.D. Georgiev,,P. Nakov, and T. Mihaylov, Finding Opinion Manipulation Trolls in News Community Forums in Proceeding of the Nineteenth Conference of Computational National Language Learning, CoNLL, All Rights Reserved 423