3,187 research outputs found
A systematic literature review on spam content detection and classification
The presence of spam content in social media is tremendously increasing, and therefore the detection of spam has become vital. The spam contents increase as people extensively use social media, i.e ., Facebook, Twitter, YouTube, and E-mail. The time spent by people using social media is overgrowing, especially in the time of the pandemic. Users get a lot of text messages through social media, and they cannot recognize the spam content in these messages. Spam messages contain malicious links, apps, fake accounts, fake news, reviews, rumors, etc. To improve social media security, the detection and control of spam text are essential. This paper presents a detailed survey on the latest developments in spam text detection and classification in social media. The various techniques involved in spam detection and classification involving Machine Learning, Deep Learning, and text-based approaches are discussed in this paper. We also present the challenges encountered in the identification of spam with its control mechanisms and datasets used in existing works involving spam detection
Artificial intelligence in the cyber domain: Offense and defense
Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.Web of Science123art. no. 41
A new semantic attribute deep learning with a linguistic attribute hierarchy for spam detection
The massive increase of spam is posing a very
serious threat to email and SMS, which have become an important
means of communication. Not only do spams annoy users, but
they also become a security threat. Machine learning techniques
have been widely used for spam detection. In this paper, we
propose another form of deep learning, a linguistic attribute
hierarchy, embedded with linguistic decision trees, for spam
detection, and examine the effect of semantic attributes on the
spam detection, represented by the linguistic attribute hierarchy.
A case study on the SMS message database from the UCI machine
learning repository has shown that a linguistic attribute hierarchy
embedded with linguistic decision trees provides a transparent
approach to in-depth analysing attribute impact on spam
detection. This approach can not only efficiently tackle ‘curse
of dimensionality’ in spam detection with massive attributes,
but also improve the performance of spam detection when the
semantic attributes are constructed to a proper hierarchy
SPANet: Frequency-balancing Token Mixer using Spectral Pooling Aggregation Modulation
Recent studies show that self-attentions behave like low-pass filters (as
opposed to convolutions) and enhancing their high-pass filtering capability
improves model performance. Contrary to this idea, we investigate existing
convolution-based models with spectral analysis and observe that improving the
low-pass filtering in convolution operations also leads to performance
improvement. To account for this observation, we hypothesize that utilizing
optimal token mixers that capture balanced representations of both high- and
low-frequency components can enhance the performance of models. We verify this
by decomposing visual features into the frequency domain and combining them in
a balanced manner. To handle this, we replace the balancing problem with a mask
filtering problem in the frequency domain. Then, we introduce a novel
token-mixer named SPAM and leverage it to derive a MetaFormer model termed as
SPANet. Experimental results show that the proposed method provides a way to
achieve this balance, and the balanced representations of both high- and
low-frequency components can improve the performance of models on multiple
computer vision tasks. Our code is available at
.Comment: Accepted paper at ICCV 202
Heckerthoughts
This manuscript is technical memoir about my work at Stanford and Microsoft
Research. Included are fundamental concepts central to machine learning and
artificial intelligence, applications of these concepts, and stories behind
their creation
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