Intelligent Deep Learning Based Cybersecurity Phishing Email Detection and Classification

Phishing is a type of cybercrime in which cyber-attackers pose themselves as authorized persons or entities and hack the victims’sensitive data.E-mails,instant messages and phone calls are some of the common modes used in cyberattacks.Though the security models are continuously upgraded to prevent cyberattacks,hackers find innovative ways to target the victims.In this background,there is a drastic increase observed in the number of phishing emails sent to potential targets.This scenario necessitates the importance of designing an effective classification model.Though numerous conventional models are available in the literature for proficient classification of phishing emails,the Machine Learning(ML)techniques and the Deep Learning(DL)models have been employed in the literature.The current study presents an Intelligent Cuckoo Search(CS)Optimization Algorithm with a Deep Learning-based Phishing Email Detection and Classification(ICSOA-DLPEC)model.The aim of the proposed ICSOA-DLPEC model is to effectually distinguish the emails as either legitimate or phishing ones.At the initial stage,the pre-processing is performed through three stages such as email cleaning,tokenization and stop-word elimination.Then,the N-gram approach is;moreover,the CS algorithm is applied to extract the useful feature vectors.Moreover,the CS algorithm is employed with the Gated Recurrent Unit(GRU)model to detect and classify phishing emails.Furthermore,the CS algorithm is used to fine-tune the parameters involved in the GRU model.The performance of the proposed ICSOA-DLPEC model was experimentally validated using a benchmark dataset,and the results were assessed under several dimensions.Extensive comparative studies were conducted,and the results confirmed the superior performance of the proposed ICSOA-DLPEC model over other existing approaches.The proposed model achieved a maximum accuracy of 99.72%.

Optimal Deep Belief Network Enabled Cybersecurity Phishing Email Classification

Recently,developments of Internet and cloud technologies have resulted in a considerable rise in utilization of online media for day to day lives.It results in illegal access to users’private data and compromises it.Phishing is a popular attack which tricked the user into accessing malicious data and gaining the data.Proper identification of phishing emails can be treated as an essential process in the domain of cybersecurity.This article focuses on the design of bio-geography based optimization with deep learning for Phishing Email detection and classification(BBODL-PEDC)model.The major intention of the BBODL-PEDC model is to distinguish emails between legitimate and phishing.The BBODL-PEDC model initially performs data pre-processing in three levels namely email cleaning,tokenization,and stop word elimination.Besides,TF-IDF model is applied for the extraction of useful feature vectors.Moreover,optimal deep belief network(DBN)model is used for the email classification and its efficacy can be boosted by the BBO based hyperparameter tuning process.The performance validation of the BBODL-PEDC model can be performed using benchmark dataset and the results are assessed under several dimensions.Extensive comparative studies reported the superior outcomes of the BBODL-PEDC model over the recent approaches.

Comparative Analysis of Machine Learning Algorithms for Email Phishing Detection Using TF-IDF, Word2Vec, and BERT

Cybercriminals often use fraudulent emails and fictitious email accounts to deceive individuals into disclosing confidential information,a practice known as phishing.This study utilizes three distinct methodologies,Term Frequency-Inverse Document Frequency,Word2Vec,and Bidirectional Encoder Representations from Transform-ers,to evaluate the effectiveness of various machine learning algorithms in detecting phishing attacks.The study uses feature extraction methods to assess the performance of Logistic Regression,Decision Tree,Random Forest,and Multilayer Perceptron algorithms.The best results for each classifier using Term Frequency-Inverse Document Frequency were Multilayer Perceptron(Precision:0.98,Recall:0.98,F1-score:0.98,Accuracy:0.98).Word2Vec’s best results were Multilayer Perceptron(Precision:0.98,Recall:0.98,F1-score:0.98,Accuracy:0.98).The highest performance was achieved using the Bidirectional Encoder Representations from the Transformers model,with Precision,Recall,F1-score,and Accuracy all reaching 0.99.This study highlights how advanced pre-trained models,such as Bidirectional Encoder Representations from Transformers,can significantly enhance the accuracy and reliability of fraud detection systems.

Ransomware Attack:Rescue-checklist Cyber Security Awareness Program

Ransomware attacks have been spreading broadly in the last few years,where attackers deny users’access to their systems and encrypt their files until they pay a ransom,usually in Bitcoin.Of course,that is the worst thing that can happen;especially for organizations having sensitive information.In this paper we proposed a cyber security awareness program intended to provide end-users with a rescue checklist in case of being attacked with a ransomware as well as preventing the attack and ways to recover from it.The program aimed at providing cyber security knowledge to 15 employees in a Sudanese trading and investment company.According to their cyber behaviour before the program,the participants showed a low level cyber security awareness that with 72%they are likely of being attacked by a ransomware from a phishing email,which is well known for spreading ransomware attacks.The results revealed that the cyber security awareness program greatly diminished the probability of being attacked by a ransomware with an average of 28%.This study can be used as a real-life ransomware attack rescue plan.