1,653 research outputs found
Analyzing the Social Structure and Dynamics of E-mail and Spam in Massive Backbone Internet Traffic
E-mail is probably the most popular application on the Internet, with
everyday business and personal communications dependent on it. Spam or
unsolicited e-mail has been estimated to cost businesses significant amounts of
money. However, our understanding of the network-level behavior of legitimate
e-mail traffic and how it differs from spam traffic is limited. In this study,
we have passively captured SMTP packets from a 10 Gbit/s Internet backbone link
to construct a social network of e-mail users based on their exchanged e-mails.
The focus of this paper is on the graph metrics indicating various structural
properties of e-mail networks and how they evolve over time. This study also
looks into the differences in the structural and temporal characteristics of
spam and non-spam networks. Our analysis on the collected data allows us to
show several differences between the behavior of spam and legitimate e-mail
traffic, which can help us to understand the behavior of spammers and give us
the knowledge to statistically model spam traffic on the network-level in order
to complement current spam detection techniques.Comment: 15 pages, 20 figures, technical repor
Analyzing Social and Stylometric Features to Identify Spear phishing Emails
Spear phishing is a complex targeted attack in which, an attacker harvests
information about the victim prior to the attack. This information is then used
to create sophisticated, genuine-looking attack vectors, drawing the victim to
compromise confidential information. What makes spear phishing different, and
more powerful than normal phishing, is this contextual information about the
victim. Online social media services can be one such source for gathering vital
information about an individual. In this paper, we characterize and examine a
true positive dataset of spear phishing, spam, and normal phishing emails from
Symantec's enterprise email scanning service. We then present a model to detect
spear phishing emails sent to employees of 14 international organizations, by
using social features extracted from LinkedIn. Our dataset consists of 4,742
targeted attack emails sent to 2,434 victims, and 9,353 non targeted attack
emails sent to 5,912 non victims; and publicly available information from their
LinkedIn profiles. We applied various machine learning algorithms to this
labeled data, and achieved an overall maximum accuracy of 97.76% in identifying
spear phishing emails. We used a combination of social features from LinkedIn
profiles, and stylometric features extracted from email subjects, bodies, and
attachments. However, we achieved a slightly better accuracy of 98.28% without
the social features. Our analysis revealed that social features extracted from
LinkedIn do not help in identifying spear phishing emails. To the best of our
knowledge, this is one of the first attempts to make use of a combination of
stylometric features extracted from emails, and social features extracted from
an online social network to detect targeted spear phishing emails.Comment: Detection of spear phishing using social media feature
An Improved Transformer-based Model for Detecting Phishing, Spam, and Ham: A Large Language Model Approach
Phishing and spam detection is long standing challenge that has been the
subject of much academic research. Large Language Models (LLM) have vast
potential to transform society and provide new and innovative approaches to
solve well-established challenges. Phishing and spam have caused financial
hardships and lost time and resources to email users all over the world and
frequently serve as an entry point for ransomware threat actors. While
detection approaches exist, especially heuristic-based approaches, LLMs offer
the potential to venture into a new unexplored area for understanding and
solving this challenge. LLMs have rapidly altered the landscape from business,
consumers, and throughout academia and demonstrate transformational potential
for the potential of society. Based on this, applying these new and innovative
approaches to email detection is a rational next step in academic research. In
this work, we present IPSDM, our model based on fine-tuning the BERT family of
models to specifically detect phishing and spam email. We demonstrate our
fine-tuned version, IPSDM, is able to better classify emails in both unbalanced
and balanced datasets. This work serves as an important first step towards
employing LLMs to improve the security of our information systems
E-mail Filtering System for Nigerian Spam
This project shows about the project details in developing the E-Mail Filtering
System specifically in filtering the Nigerian Spam. The main elements in this report
consist of introduction, literature review, methodology and result and discussion. The
project is developed by focusing on research activities, findings analysis and developing
product. This project is developed based onthe advancement ofInformation Technology
(IT) system today which is recently growing rapidly. Recent growth in the use of email
for communication andthe corresponding growth in the volume of email received have
made automatic processing of email desirable. Present day solutions to stop spam work
by analyzing headers and message text or classifying the mail based on history. This
report gives anintroduction to machine learning methods for spam filtering especially for
Nigerian Spam. Anoverview of this mail system will fall back on SPAM filters that use
"Naive Bayesian Filtering" which is a probabilistic approach to estimate the degree of
SPAM
Recommended from our members
Proliferation and detection of blog spam
The ease of posting comments and links in blogs has attracted spammers as an alternative venue to conventional email. An experimental study investigates the nature and prevalence of blog spam. Using Defensio logs, the authors collected and analyzed more than one million blog comments during the last two weeks of June 2009. They used a support vector machine (SVM) classifier combined with heuristics to identify spam posters' IP addresses, autonomous system numbers (ASN), and IP blocks. Experimental results show that more than 75 percent of blog comments during the reporting period are spam. In addition, the results show that blog spammers likely operate from a few colocation facilities. © 2006 IEEE
Kemahiran pemikiran komputasional pelajar melalui modul pembelajaran berasaskan teknologi internet pelbagai benda
kemahiran pemikiran komputasional pelajar, ke arah lebih kreatif dan kritis
melalui penggunaan Modul Pembelajaran Berasaskan Teknologi Internet
Pelbagai Benda (MP-IoT) yang telah dibangunkan oleh penyelidik.
Pembangunan MP-IoT mengikut Model ADDIE dan melibatkan Teknologi
Arduino yang diterapkan dalam 5 aktiviti pembelajaran secara amali. Kajian
berbentuk kuantitatif jenis kuasi-eksperimental ini telah dijalankan ke atas 52
orang pelajar Tingkatan 4 dari 2 buah sekolah di daerah Batu Pahat, Johor dan
Kuala Kangsar, Perak. Data pula telah dianalisis secara deskriptif dan inferensi.
Satu set ujian pencapaian pra dan pasca sebagai instrument telah dibangunkan.
Analisis Item Indeks Kesukaran (IK), Indeks Diskriminasi, serta Interprestasi
skor bagi nilai Alpha Cronbach telah digunakan bagi memastikan soalan ujian
pencapaian sesuai digunakan. Manakala dalam proses pembangunan modul
MP-IoT, seramai 6 orang guru dari mata pelajaran Sains Komputer dipilih
sebagai pakar untuk mengenal pasti kesesuaian dari segi format, kandungan dan
kebolehgunaan modul yang dibangunkan Skala Likert lima mata digunakan
dalam kajian ini. Secara keseluruhannya, dapatan kajian menggunakan ujian-T
sampel berpasangan, menunjukkan terdapat perbezaan yang signifikan terhadap
tahap pencapaian pelajar kumpulan kawalan yang didedahkan dengan kaedah
konvensional dengan kumpulan rawatan yang didedahkan dengan modul MPIoT,
dengan
nilai
p-value
adalah
.000 iaitu
kurang
dari
.05 (p<0.05).
Selain
itu,
tahap
kemahiran pemikiran komputasional pelajar juga meningkat setelah
didedahkan dengan modul MP-IoT
- …