19th IEEE International Conference on Tools with Artificial Intelligence Deep Belief Networks for Spam Filtering

Citations in the corpus

• Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning (2017, background): “Therefore, deep learning systems have become prevalent in our lives, including security-sensitive applications such as face recognition [55], [62], fingerprint identification [66], spam filtering [64], [56], malware detection [21], [57], and autonomous vehicles [17].”
• A Survey of Deep Learning Methods for Cyber Security (2019, methodology): “RBM Tzortzis and Likas [165] 21 Spam Identification EnronSpam [73] SpamAssassin [74] LingSpam [75]”
• LEMNA: Explaining Deep Learning based Security Applications (2018, background): “There are many other security applications such as detecting the “function end” for binary code, pinpointing the function types and detecting vulnerable code [15, 24, 47, 52, 66].”
• A review of machine learning approaches to Spam filtering (2009, background): “Tzortzis and Likas (2007) considered the application of deep belief networks, neural networks with several hidden layers, for which an efficient training algorithm was recently developed.”
• A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, background): “In DBN, every layer of the network needs to learn complete input to generate output [252].”