Spam detection using hybrid Artificial Neural Network and Genetic algorithm

Arram, Anas; Mousa, Hisham; Zainal, Anzida

doi:10.1109/isda.2013.6920760

Cited by 9 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The best reported recall for ANN to detect an intrusion is 98.94% [139], and spam is 94% [140]. ANN has obtained best precision while detecting the intrusion is 97.89% [139], malware is 88.89% [141], and spam is 95% [142]. A detailed performance comparison of ANN to various cyber threats on the frequently used dataset is presented in Table 7.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

et al. 2020

View full text Add to dashboard Cite

Cyberspace has become an indispensable factor for all areas of the modern world. The world is becoming more and more dependent on the internet for everyday living. The increasing dependency on the internet has also widened the risks of malicious threats. On account of growing cybersecurity risks, cybersecurity has become the most pivotal element in the cyber world to battle against all cyber threats, attacks, and frauds. The expanding cyberspace is highly exposed to the intensifying possibility of being attacked by interminable cyber threats. The objective of this survey is to bestow a brief review of different machine learning (ML) techniques to get to the bottom of all the developments made in detection methods for potential cybersecurity risks. These cybersecurity risk detection methods mainly comprise of fraud detection, intrusion detection, spam detection, and malware detection. In this review paper, we build upon the existing literature of applications of ML models in cybersecurity and provide a comprehensive review of ML techniques in cybersecurity. To the best of our knowledge, we have made the first attempt to give a comparison of the time complexity of commonly used ML models in cybersecurity. We have comprehensively compared each classifier’s performance based on frequently used datasets and sub-domains of cyber threats. This work also provides a brief introduction of machine learning models besides commonly used security datasets. Despite having all the primary precedence, cybersecurity has its constraints compromises, and challenges. This work also expounds on the enormous current challenges and limitations faced during the application of machine learning techniques in cybersecurity.

show abstract

Section: Artificial Neural Networkmentioning

confidence: 99%

Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

et al. 2020

View full text Add to dashboard Cite

show abstract

“…To overcome such difficulties, ANNs are applied along with Genetic Algorithm to enhance the performance of spam detection and classification [51]. The authors in [51] proposed a combination of both ANN and GA to come up with a new hybrid algorithm that beats the conventional ANN. According to the improvement on spam detection accuracy, the proposed hybrid algorithm can be implemented to detect spam messages on OSNs.…”

Section: Artificial Nueral Network Intrusion Detection and Spam Filtementioning

confidence: 99%

Security techniques for intelligent spam sensing and anomaly detection in online social platforms

Aldwairi

Tawalbeh

2020

IJECE

View full text Add to dashboard Cite

The recent advances in communication and mobile technologies made it easier to access and share information for most people worldwide. Among the most powerful information spreading platforms are the Online Social Networks (OSN)s that allow Internet-connected users to share different information such as instant messages, tweets, photos, and videos. Adding to that many governmental and private institutions use the OSNs such as Twitter for official announcements. Consequently, there is a tremendous need to provide the required level of security for OSN users. However, there are many challenges due to the different protocols and variety of mobile apps used to access OSNs. Therefore, traditional security techniques fail to provide the needed security and privacy, and more intelligence is required. Computational intelligence adds high-speed computation, fault tolerance, adaptability, and error resilience when used to ensure security in OSN apps. This research provides a comprehensive related work survey and investigates the application of artificial neural networks for intrusion detection systems and spam filtering for OSNs. In addition, we use the concept of social graphs and weighted cliques in the detection of suspicious behavior of certain online groups and to prevent further planned actions such as cyber/terrorist attacks before they happen.

show abstract

“…They used hybrid neural model of ANN, Adaptive Neuro-Fuzzy Inference System (ANFIS) and GA, which predicted better than BPA alone. Arram et al [18] proposed a neural model for spam detection in email. In order to optimize ANN, GA was used.…”

Section: Related Workmentioning

confidence: 99%

On the Use of Local Search in the Evolution of Neural Networks for the Diagnosis of Breast Cancer

et al. 2015

View full text Add to dashboard Cite

With the advancement in the field of Artificial Intelligence, there have been considerable efforts to develop technologies for pattern recognition related to medical diagnosis. Artificial Neural Networks (ANNs), a significant piece of Artificial Intelligence forms the base for most of the marvels in the former field. However, ANNs face the problem of premature convergence at a local minimum and inability to set hyper-parameters (like the number of neurons, learning rate, etc.) while using Back Propagation Algorithm (BPA). In this paper, we have used the Genetic Algorithm (GA) for the evolution of the ANN, which overcomes the limitations of the BPA. Since GA alone cannot fit for a high-dimensional, complex and multi-modal optimization landscape of the ANN, BPA is used as a local search algorithm to aid the evolution. The contributions of GA and BPA in the resultant approach are adjudged to determine the magnitude of local search necessary for optimization, striking a clear balance between exploration and exploitation in the evolution. The algorithm was applied to deal with the problem of Breast Cancer diagnosis. Results showed that under optimal settings, hybrid algorithm performs better than BPA or GA alone.

show abstract

Spam detection using hybrid Artificial Neural Network and Genetic algorithm

Cited by 9 publications

References 9 publications

Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity

Security techniques for intelligent spam sensing and anomaly detection in online social platforms

On the Use of Local Search in the Evolution of Neural Networks for the Diagnosis of Breast Cancer

Contact Info

Product

Resources

About