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Cyber-attacks Are Exponentially Increasing Daily With The Advancements Of Technology. Therefore, The Detection And Prediction Of Cyber-attacks Are Very Important For Every Organization That Is Dealing With Sensitive Data For Business Purposes. In This Paper, We Present A Framework On Cyber Security Using A Data Mining Technique To Predict Cyber-attacks That Can Be Helpful To Take Proper Interventions To Reduce The Cyber-attacks. The Two Main Components Of The Framework Are The Detection And Prediction Of Cyber-attacks. The Framework First Extracts The Patterns Related To Cyber-attacks From Historical Data Using A J48 Decision Tree Algorithm And Then Builds A Prediction Model To Predict The Future Cyber-attacks. We Then Apply The Framework On Publicly Available Cyber Security Datasets Provided By The Canadian Institute Of Cybersecurity. In The Datasets, Several Kinds Of Cyber-attacks Are Presented Including DDoS, Port Scan, Bot, Brute Force, SQL Injection, And Heartbleed. The Proposed Framework Correctly Detects The Cyber-attacks And Provides The Patterns Related To Cyber-attacks. The Overall Accuracy Of The Proposed Prediction Model To Detect Cyber-attacks Is Around 99%. The Extracted Patterns Of The Prediction Model On Historical Data Can Be Applied To Predict Any Future Cyber-attacks. The Experimental Results Of The Prediction Model Indicate The Superiority Of The Model To Detect Any Future Cyber-attacks.