David C. Wyld et al. (Eds): CSE, AI & FL, NLPTT - 2021
pp. 59-72, 2021. CS & IT - CSCP 2021 DOI: 10.5121/csit.2021.112405
SOCIAL MEDIA NETWORK ATTACKS AND
THEIR PREVENTIVE MECHANISMS: A REVIEW
Emmanuel Etuh, Francis S. Bakpo
and Eneh Agozie H
Department of Computer Science, Faculty of Physical Sciences,
University of Nigeria, Nsukka, Nigeria
ABSTRACT
We live in a virtual world where actual lifestyles are replicated. The growing reliance on the
use of social media networks worldwide has resulted in great concern for information security.
One of the factors popularizing the social media platforms is how they connect people
worldwide to interact, share content, and engage in mutual interactions of common interest that
cut across geographical boundaries. Behind all these incredible gains are digital crime
equivalence that threatens the physical socialization. Criminal minded elements and hackers
are exploiting Social Media Platforms (SMP) for many nefarious activities to harm others. As
detection tools are developed to control these crimes, hackers’ tactics and techniques are
constantly evolving. Hackers are constantly developing new attacking tools and hacking
strategies to gain malicious access to systems and attack social media network thereby making
it difficult for security administrators and organizations to develop and implement the proper
policies and procedures necessary to prevent the hackers’ attacks. The increase in cyber-attacks
on the social media platforms calls for urgent and more intelligent security measures to
enhance the effectiveness of social media platforms. This paper explores the mode and tactics of
hackers’ mode of attacks on social media and ways of preventing their activities against users to
ensure secure social cyberspace and enhance virtual socialization. Social media platforms are
briefly categorized, the various types of attacks are also highlighted with current state-of-the-art
preventive mechanisms to overcome the attacks as proposed in research works, finally, social
media intrusion detection mechanism is suggested as a second line of defence to combat
cybercrime on social media networks
KEYWORDS
Intrusion Detection System, Data Warehouse, Machine Learning, Hackers, Social Media
Platform, Online Social Network Intrusion.
1. INTRODUCTION
Social media network platforms provide mechanisms that enhance the effectiveness of virtual
socialization in the global village. It is a medium that enable families, friends, and associates to
interact and communicate seamlessly irrespective of their locations, distances, and platforms.
Online Social Networks (OSNs) are the connection and communication platform that promotes
the social interaction in the virtual space [1]. [2] identified 7 categories of social networks on the
Internet to include: electronic mail services like Gmail, Yahoo mail, Microsoft outlook, Hotmail,
etc; Instant messengers like WhatsApp, Twitter, Yahoo messenger, Instagram, Telegram,
Snapchat, etc; Blogs platforms like Blogger, Tomblr, Wix, Linda etc; Social networking sites like
Facebook, TikTok, Quora, LinkedIn, etc; Multimedia sharing systems like YouTube, Skype,
Flikr, etc; Auction platforms like Jumia, Alibaba, Konga, OLX, etc; and Social search engines
like Google, Yahoo, Safari, etc. All these platforms enable users to socialize and stay in touch
60 Computer Science & Information Technology (CS & IT)
with social reality in the virtual environment with varying functionalities. The pervasive nature of
Information and Communication Technology (ICT) has greatly influenced every aspect of human
activities; this has also influenced social media users to see the platform as a virtual home where
they save their sensitive information on the database of these platforms.
The growing reliance on the use of social media networks worldwide has resulted in great
concern for information security. One of the factors popularizing the social media platforms is
how they connect people worldwide to interact, share content and engage in discussions of
mutual interest that know no geographical boundaries. Behind all these incredible gains, most
traditional crimes now have digital equivalence enabling criminal minded elements and hackers
to exploit social media platforms for many nefarious activities to harm others. As security
administrators and policy makers develop detection tools to control these crimes, hackers’ tactics
and techniques are also constantly evolving. Hackers are cybercriminals that specializes in virtual
terrorism that endangers the legitimate users of Social Media Network Platform (SMNP) in
particular and the entire virtual community in general [3] through various kinds of cyber-attacks.
These cybercrimes have significant negative impact on the social media platforms and the users
in particular. Because of ease of accessibility, some of the social media users prefer to store their
sensitive data on the network and when the account is hacked, this information could be used to
swindle and defraud the user; also, the user’s social contacts on the platform are at high risk of
being defrauded by the hacker who could use their techniques by masquerading as the authorized
user. High profile users like public and political leaders with private information that could
tarnish their image if extracted can be used to threaten the user for ransom.
Different approaches have been used to prevent hackers’ intrusion on the social media network
platforms. The prevalent one is authentication using credentials like username and password, or
PIN; biometric authentication like using face recognition technology, fingerprint, pattern
matching, or voice recognition are all various forms of authentication. Other methods are Role
Based Access Controls (RBAC), Extended RBAC, Temporal (RBAC), Risk-based access control
[4]. These security methods have a lot of drawbacks like weak password which can easily be
guessed by hackers using dictionary attack. In an attempt to enforce stronger password for
authentication, social media users are forced to write their authentication credentials on papers
which can also be stolen by hackers, these weaknesses have influenced many researchers to
propose several security mechanisms to curtail the activities of these hackers. Some of these
proposals include: biometric authentication, hybrid system for anomaly detection in social
networks [5],Network Intrusion Detection System[6], [7], [8]etc.
All these methods are not suitable for data warehouse security. The commonly used network
securities software like firewall and anti-viruses independently provide different services to
network security but they can be bypassed by hackers. Hackers improvises new techniques of
breaking into the social media platforms without being detected, the two close proposals on Data
Warehouse Database Intrusion Detection System by [9] and [4] does not discourage resilient
hackers. The limitations of all these proposed security approaches have been pointed out in Table
2.4 of the literature review. Hence, there is therefore a need for Intelligent Intrusion Detection
Model (IIDM) that is efficient to disarm the hackers from carrying out their cybercrime activities
against SMNP.
2. THEORETICAL BACKGROUND
Social media platforms have become an integral part of average network users in the virtual
community. Billions of connected devices to the Internet operate on one social media platform or
Computer Science & Information Technology (CS & IT) 61
the other. According to report in [10], over 500million IoT devices were implemented globally in
2003, 12.5 billion in 2010, and 50 billion in 2020.There are about 3.5 billion people on social
media with an estimated attacks that generate over $3 billion annually for cyber criminals [11].
Online social network platform like Facebook incorporate several functionalities like product and
services advertisement and sales that makes it relevant to almost all internet users either
cooperate or private. This has also increased cybercriminals’ activity on the platform. According
to a recent survey by Computer Emergency Response Team (CERT), the rate of cyber-attacks has
been doubling every year [6]. Online social network is faced with threatening security challenges
[2]. Facebook is the most popular social networking site. It was launched in February 2004
[12]With roughly 2.89 billion monthly active users as at the second quarter of 2021, Facebook is
the biggest social network worldwide.
The Covid19 pandemic has been instrumental to the geometric shift to virtual socialization. The
technological shift to cloud computing paradigm also has positively influenced the ubiquity of
social media. This shift seems to have given hacker an edge to securely carryout their nefarious
acts since humans are less involved. Cloud intrusion attacks are set of actions that attempt to
violate the integrity, confidentiality or availability of cloud resources on cloud SMNP. The rising
drop in processing and Internet accessibility cost is also increasing users’ vulnerability to a wide
variety of cyber threats and attacks.
Intrusion detection is meant to detect misuse or an unauthorized use of the computer systems by
internal and external elements [7]. IDS are an effective security technology, which can detect,
prevent and possibly react to the attack [13], [14] opined that artificial Intelligence plays a
driving role in security services like intrusion detection.
2.1. Social media network
Social media network is a platform that creates virtual environment for social interactions among
circle of friends and fans of like-minds. “Social media platforms are internet-based applications
focused on broadcasting user-generated Content”[15] It deals with the sharing of information and
multimedia content between users on similar platforms over electronic network especially the
internet and cyberspace [16]. This platform has geometrically grown to become not only an
effective communication tool for personal and social use, but also an essential channel for
businesses and official communication channels. There are thousands of social media platforms
being used today for different purposes, few of them that are most popular are highlighted below.
Facebook is an online social media platform that provides several services like social
networking of friends and fans, online advertising, voice calls, instant messaging, video calls,
video sharing and viewing, online market place, virtual gifts among both young and the old,
private and corporate bodies. It was launched on February 4, 2004, by Mark Zuckerberg. It
had over 1.18 billion monthly active users as of August 2015 [16] and 2.85 billion active
users in 20201 according to statistics by [17] with an engagement of over 4 billion views of
videos everyday on the network. About 2.14 billion people can be reached via advertising on
Facebook [17].
WhatsApp is a cross-platform internet-based instant messaging application that allows smart
phone users to exchange text, image, video and audio messages for free provided the device
has Internet access. It was developed in 2009 by Brian Acton and Jan Koum. WhatsApp
became the most popular messaging app with about 900 million active users as at September,
2015 [16].
MySpace is a social networking website offering an interactive, user-submitted network of
friends, personal profiles, blogs, groups, photos, music and videos. It was the biggest social
62 Computer Science & Information Technology (CS & IT)
media platform up till 2008 when it was overtaken by Facebook. It was cofounded by Chris
DeWolfe and Tom Anderson
Twitter is a social network platform that enables users to write and read short character
messages called tweets. It revolves around the principle of followers who are equally users,
who choose to follow another Twitter user and can thus view tweets sent by that user.
Whereas unregistered users can read tweets, one must be registered to send tweets. It was
founded in March, 2006 by Jack Dorsey [16].
Instagram allows users to upload media that can be edited with filters and organized
by hashtags and geographical tagging. Posts can be shared publicly or with pre-approved
followers. Users can browse other users' content by tags and locations and view trending
content. Users can like photos and follow other users to add their content to a personal feed.
Instagram has 1.38 billion active users with 500 million daily active users of Instagram
stories, 1.16 billion people can be reached through adverts on Instagram [17]
YouTube is a video sharing service that allows users to watch videos posted by other users
and upload videos of their own. With the ubiquitous use of smart phones this platform has
become the first choice in personal broadcasting and video sharing. It was cofounded by
Chad Hurley, Steve Chen, and Jawed Karim in February 2005. In November 2006, it was
bought by Google and now operated by Google
LinkedIn is a social media platform for professional networking. It is a social networking
tool available to job seekers and professionals where users can invite other users and even
non-users to connect. Inviters who get several rejections from invitees risk having their
accounts restricted or closed. On this platform, users can get introduced to networks of
contacts, new job and business opportunities, display products and services in their company
profile pages, list job vacancies and search for potential candidates
Skype is an IP telephony service provider that can be used to make free voice and video calls
over the Internet to any Skype subscriber or to any other non-user at low calling rates. It is
relatively simple to download and install the software, which works on most computers and
phones. A dedicated Skype phone can be used or desktop computers, notebooks, tablets,
mobile phones and other mobile devices fitted with a headset, speakers, microphones or USB
phone. Skype also enables file transfers, texting, video chat and videoconferencing.
Viber is a mobile application that allows phone calls and text messages to all other users,
whether mobile or landline, for free. It is available over WiFi or 3G with sound quality much
better than a regular call with mobile carrier charges applicable when used over a 3G
network. Once the application is installed, calls can also be made to numbers that do not have
Viber at low rates using ViberOut. Viber works on most android, iphone, blackberry,
windows, mac, nokia and bada devices.
Tumblr is a microblogging and social networking platform it’s service allows users to
post multimedia and other content to a short-form blog. Users can follow other users' blogs.
Bloggers can also make their blogs private. For bloggers many of the website's features are
accessed from a "dashboard" interface. It was founded by David Karp in 2007.
WeChat is a Chinese multi-purpose instant messaging, social media and mobile payment app
developed by Tencent. First released in 2011, it became the world's largest standalone mobile
app in 2018, with over 1 billion monthly active users. It has been described as China's "app
for everything" and a "super app" because of its wide range of functions. It provides text
messaging, hold-to-talk voice messaging, broadcast (one-to-many) messaging, video
conferencing, video games, sharing of photographs and videos and location sharing.
Reddit This social media platform enables you to submit content and later vote for the
content. The voting determines whether the content moves up or down, which is ultimately
organized based on the areas of interest (known as subreddits). Number of active users per
month: 100 million approximately.
Computer Science & Information Technology (CS & IT) 63
Taringa is one of the largest social networking platforms in Latin America and allows users
to share their experiences, content and more. Number of active users: 75 million
approximately.
Renren is the largest social networking site in China and is literally a platform for everyone.
It has been highly popular with the youth due to its similarity to Facebook, as it allows users
to easily connect with others, quickly share thoughts and posts, and even update their moods.
Number of active users per month: More than 30 million approximately
All the social media network platforms including the ones highlighted above can be categorized
based on their support for the types of data they exchange or based on their aspect of support for
social interactions.
Based on their support for the types of data they exchange, social media network platforms can
be categorized into four main types:
i) Textual-based platform: used for text-related social communication for sending/receiving
messages. A good examples are the messenger platforms.
ii) Visual-based platforms: used to for picture-related social interactions like sending and
receiving images. A good example is flikr platform
iii) Audio-visual based platforms: used to for video-related social interactions like
sending/receiving video data).A typical example is the YouTube,
iv) Hybrid platforms: this platform combines the functionalities of more than one of the textual,
visual, and audio-visual platforms. A typical example is the Facebook
Social media platforms can also be categorized based on their aspect of support for social
interactions. These categories are summarized in Table 2.1 below.
Table 2.1. Summary of Categories of Social Media Platform
Category
Usage
Examples
Electronic Mail
Service Platforms
The very first social media platform that gave birth
to the Internet. Used to send and receive electronic
mails from friends and associates
Gmail, Yahoo mail,
Microsoft outlook,
hotmail, etc
Social networking
websites
Mostly used to connect friends, family, brands, and
to reach out to target audience.
Facebook,Twitter,
Whatsapp, Instagram,
Discussion
Platforms
Mostly designed for research, and focused discussion
with people with common interest
Reddit, Quora,
Nairaland, etc
Blogging platforms
Mostly used for news, writing of articles, and
personal messages to targeted audience
Tumblr, Medium,
Blogger, Wix, Linda
Instant Messenger
Platforms
Used for real-time textual conversation with instant
sending and receiving functionalities
Whatsapp, Twitter,
Yahoo messenger,
Instagram, Snapchat,
Multimedia
Platforms
For sharing of videos with both subscribed and
visitor social media user of this platform
YouTube, Skype Flikr,
TikTok, etc
Auction Systems
For sales of goods and services
Jumia, Alibaba, Konga,
OLX,
Cooperate Social
Platforms
Most companies are now incorporating social flavor
like blog to their virtual platform to enable them to
reach their target customers with their products and
services
Microsoft news, Yahoo
news, BBC news, Safari
etc
Educational
/Professional
Platforms
For sharing knowledge through, chart, uploading of
resources, live meetings, and connecting with
professionals in the chosen profession of the social
Elsevier, Academia,
Slack. DOAJ, Google
Meet, Zoom, LinkedIn
64 Computer Science & Information Technology (CS & IT)
media user
etc
Gaming platforms
Hosts gamming applications where social media
users play games either for entertainment, fun, or
betting
Nairabet, Naijabet,
Kingsbet, etc
Search Engines
It is the major tool that enhances virtual
socialization. It enable social media user to easily
locate the object of search
Google, Yahoo,
Microsoft edge, Firefox,
etc
2.2. Social media network platforms attacks
There are several attacks launched against social media network platforms. It is important to
know them because a more thorough understanding of these types of attacks equips social media
user an armament of defensive measures and knowledge to lessen the likelihood of being
exploited [3]. In August 6, 2009, Twitter, Facebook, LiveJournal, Google’s Blogger, and
YouTube were attacked by a distributed denial-of-service (DDoS) attack, in October, 2021,
similar service disruption was encountered. [3] identified seven deadliest attacks on social media
network platforms. These attacks are highlighted as follows
1. Social networking infrastructure attacks: here the attacker launches the attack on the
platform that provides the social service with the view to disconnecting users from accessing the
services provided by the platform. The major attack used against social networking infrastructure
that directly affects the users is DDoS.
2. Malware attacks: in this type of attack, the hacker develop a malicious software with
the intention of gaining control and utilizing the user’s device to perform some malicious
activities like launching DoS attack, keystrokes logging, theft of credential, credit-card number or
bank details, etc. The mode of infecting user’s device on social media is usually through links or
images sent to the user’s inbox knowing that the user will likely open since it comes from a
connected social contact [18]. Once a user is infected, the hacker uses the compromised social
media account to spread the worm by delivering a message to other users who are friends with
the infected user containing a luring link to a third-party Web site, where they are then prompted
to perform an action like “register to view full image”, “update you Adobe Flash player to have a
better view”, etc. Once the action is performed, the worm will automatically infect the devices of
all the connected friends that followed the link to the third party site. Common Malware
Categories are Crimeware, Spyware, Adware, Browser Hijackers, Downloader, Toolbars, and
Dialers [18]. Hackers leverage on the openness of social networking sites where users generate
their contents; the large number of users; and the trust that is implied where users assume all
friends are to be trusted to launch attacks to connected billions of users. The most effective
method is by using Cross-Site Scripting XSS to implement their malicious codes on social
networking site.
3. Phishing attacks: as the name imply is a hacking technique where the hacker lure a user
using “bate” that is most appealing to the user with the intention of trapping the user. In most
cases, the user is coarse into divulging sensitive information that will in turn be used to attack the
user.
4. Evil twin attacks: in this type of attack, the hacker uses the target’s profile to create
account to mimic the authentic user. This attack can also be called cyber-impersonation. The new
account is then used to send friend’s request to the contacts on the social media platform just to
enable the attacker to enjoy the privileges of friends and gain access to the users on the platform.
5. Identity theft: in this type of attack, the user’s credential is stolen and used to securely
gain access to the user’s social media platform. Once the attacker successfully gains access, they
launch their pre-conceived attacks while impersonating the authentic user.
Computer Science & Information Technology (CS & IT) 65
6. Cyber-bullying: a way of threatening or intimidating a social media user either through
messages or by posting objectionable content on the social media network to harass or intimidate
the targeted user.
7. Physical threats: in this attack, the hacker launches physical attack against selected user.
It could be in form of bypassing physical security of the platform through threatening the user to
remove the device security.
All these threats can use any of the following attacking mode to carryout the threats.
1. Denial of Service (DoS) where an attacker tries to prevent legitimate users from using a
service
2. Probe attack where an attacker tries to find information about the target host through ways
such as scanning victims to get information about available services and the operating system
3. User to Root (U2R) where unauthorized access to local super-user privileges are being
granted
4. Remote to Local (R2L) where unauthorized access from a remote machine through
approaches such as guessing password to obtain a local account on the victim host
5. Advanced Persistent Threat (APT) is a targeted attack against a high-value asset or physical
system where attackers often leverage stolen user credentials or zero-day exploits to avoid
triggering alerts
2.3. Security measures for social media network
The “juicy prospect” of social media network platforms has made hackers to constantly device
techniques to intrude and usurp users. They have two fold targets which are the social media
users and the SMNP which they break into and control for their selfish gain [19]. On the users
end, the hackers’ activities make them susceptible to threats which include identity theft, evil
twin, password resetting, sim cloning, brute force, fake links, phishing, information leakage,
celebrity spoofing, fake account, impersonation, etc [20] [21] [22]. They also use code injection
through malicious SQL script to disrupt the network. The existing security mechanism for DW
include Role Based Access Controls (RBAC), Extended RBAC, Temporal RBAC (TRBAC),
Risk-based access control[4] which all has to do with authentication using username and
password. [9] were the first to propose Database Intrusion Detection Systems (DIDS) for DW. [4]
improved it by incorporating second level authentication, instant messenger like Whatsapp also
uses two-steps verification where the user is asked to supply a PIN at intervals to prevent hackers
from the network any time there is a detected anomaly, but hackers still use social engineering to
trick the users thereby compromising the account which go undetected by role-based access
controlled systems.
To hack into a social media network, there are basic steps taken by attackers to perform their
operations. These steps are:
i. Target selection: the attacker determine the victim of the social media users to attack
ii. Attack selection: The attacker determine the type of attack to launch against the target,
e.g infrastructure, malware, phishing, evil-twin, identity theft, cyber-bullying, physical
threat, celebrity spoofing etc.
iii. Strategy formation: the strategy is dependent on the type of attack to use. If the selected
attack is to launch DDoS attack, then the attacker will have to recruit accomplices
(botnet) to use in launching the attack either through Calls, E-mail, Post to user groups,
or Creation of a Web page where users are redirected to for infection.
iv. Army training: Furnish the accomplices with package containing the attack, time, date,
and instructions on how to perform the attack.
66 Computer Science & Information Technology (CS & IT)
v. Launching attack: here the attacker will launch the attack, wait and watch the execution
of the attack.
To overcome the various attacks highlighted on the social media network platform, the user
should:
i. Ensure an up-to-date antivirus software on the device as a primary line of defence
ii. Not open e-mails from people you don’t know.
iii. Not click on unknown links
iv. Not visit unfamiliar sites
v. Disable JavaScript.
vi. Maintain and ensure regular update on software patches.
vii. Implement browser security policies, such as blocking pop-ups and limiting the number
of connections.
viii. Implement platform privacy security policies, such as “who can see my personal info”,
“who can post on my wall”, status, etc
ix. Implement IDS/IPS as second line of defence against attackers.
2.4. Intrusion detection mechanisms
Intrusion detection system (IDS) is a device or software application that monitors a system,
network or application for malicious activity or policy violations with the aim of detect them.
Two major IDS emphasized in literatures are Host-based and Network-based. These IDSs are not
suitable for intrusion detection in application related intrusion attacks. This gave rise to the
development of application specific IDS which is application based. Fig 2.1 shows the three types
of intrusion detection systems presently available for information security.
Fig 2.1. Types of Intrusion Detection System
Researchers have proposed various methods to detect abnormal operations in a system.
Generally, IDS comprises of four main components: Traffic Collector, Analysis Engine,
Signature Database, Management and Reporting Interface [7]. Network-based, Host-based, or
Application-based intrusion detection systems use either signature mechanism to detect intrusion
or anomaly approach. The signature approach uses rules for decision making on classifying
intrusion based known profile of intrusion. Anomaly on the other hand classifies an operation as
intrusion based on a deviation from the known normal operation of a given system.
Computer Science & Information Technology (CS & IT) 67
Fig 2.2. Mechanisms for intrusion detection
The work done in can be consulted for further reading [23] as it compared the various approaches
using features, advantages, and disadvantages of each approach.
2.5. Review of Related Literatures
[5] proposed “an efficient hybrid system for anomaly detection in social networks”. The model
cascaded several machine learning algorithms that included decision tree, Support Vector
Machine (SVM) and Naïve Bayesian classifier (NBC) for classifying normal and abnormal users
on social networks. the anomaly detection engine uses SVM algorithm to classify social media
network user as happy or disappointed, NBC algorithm is used based on a defined dictionary to
classify social media users with social tendency. Unique features derived from users’ profile and
contents were extracted and used for training and testing of the model, performance evaluation
conducted by experiment on the model using synthetic and real datasets from social network
shows 98% accuracy.
(Mansoori et al. 2020) proposed “Suspicious Activity Detection of Twitter and Facebook using
Sentimental Analysis”. The model was designed using Natural
[24] proposed “Social Media Cyberbullying Detection using Machine Learning” the model
leverage on the machine learning capability to detect the language patterns of bullies on social
media network which will be used to generate a model that can automatically detect
cyberbullying actions on the network. The model was evaluated using two classifier algorithms:
SVM and Neural Network, TFIDF and sentiment analysis algorithms were used for features ex-
traction. Different n-gram language models were used to evaluate the model and was found to
achieved 92.8% accuracy using Neural Network with 3-grams and 90.3% accuracy using SVM
with 4- grams while using both TFIDF and sentiment analysis together. NN performed better than
the SVM classifier with average f-score of 91.9% while that of SVM achieved average f-score
89.8%.
(Shah, Sharma, and Bandgar 2021) proposed “Cybercrime Prevention on Social Media” in which
a Facebook-like social network platform was designed as a proof-of-concept to detect
cyberbullying and anormaly detection. Each user’s post will be passed through “Post Classifier”
to divide illegal posts from adult image post, the illegal post detection algorithm was used to
check for violence-based objects. Similarly, sentiment analysis was used to check the intention of
68 Computer Science & Information Technology (CS & IT)
the message to detect any harrasment words in the content of the post. A threashold was set for
any of the negative words and images posted by a user, if any illegal post by a user exceed a
threashold, the user will be warned, and if there is persistence in the post, the user will be banned
from making a post on the platform. Convolution Neural Network (CNN) machine learning
algorithm was used in the implementation
[4] “Intrusion Detection System for Data Warehouse with Second Level Authentication”. This
proposal was premised on the ground that earlier security mechanisms for data warehouse like
“Role Based Access Controls (RBAC), Extended RBAC, Temporal RBAC (TRBAC), Risk-
based access control, Intrusion Detection System (IDS) and some other customized security
solutions for DWs does not detect a hacker that gain rightful access to the DW through credential
theft. Therefore, a second level authentication mechanism within the IDS was proposed where a
minute deviation from the user’s past behavior will be detected based on providing answer to
secrete question that was provided at the account setup phase.
In their research work, [12] envisioned the most important criteria of employing social network in
higher education, they observed broad view of possibilities for using social networks in higher
education. The conceptual framework for academic social network should have the following
four main objectives: (1) To provide academic service support and academic information
dissemination; (2) To enable student support and communication; (3) To exalt social and
cooperate learning; (4) To provide achievement representationability.
[13] developed an “Intrusion Detection System for College (ERP) Enterprise Resource Planning
System” The system used a layered approach combined with a decision tree based architecture to
detect attacks, the design and simulation was carried out using Netbeans integrated development
environment with MYSQL database.
[8]proposed a model for building the network intrusion detection system using a machine
learning algorithm called decision tree to detect anomaly based intrusion. The system used
Recursive-Feature-Elimination (RFE) to select the best features from Change Control Intrusion
Detection (CCIDS) 2017dataset. The dataset was split into training and test dataset, the training
dataset was fit to the classification model developed to classify the test data as malicious or
benign. The precision of the proposed system indicated True-Positive-Rate (TPR) of 99.9% and
the False-Positive-Rate (FPR) of 0.1%
[14]proposed a dynamic Intelligent Intrusion Detection Model based on specific AI approach for
intrusion detection. It is a hybrid system that combines anomaly, misuse and host based detection.
SNORT packet sniffer was used for new data collection which will be passed through the
inference engine to classify the operation as host-based or anomaly. The implementation
algorithm used was neural networks and fuzzy logic with network profiling. Simple data mining
techniques was used to process the network data to predict anomaly detection.
[7] proposed “Network Intrusion Detection System”. The system like any other intrusion
detection system comprises of four major components which are: Traffic Collector for gathering
activity and event data for analysis; Analysis Engine that analyzes the data that the traffic
collector gathered; Signature Database which is an amalgamation of signatures known to be
associated with suspicious and malicious activities; Management and Reporting Interface used by
system administrators to manage the system and receive alerts when intrusions are detected. The
system was implemented using Java programming language, used to detect specific attacks which
are Man-in-the- Middle, DOS and ping of death.
Computer Science & Information Technology (CS & IT) 69
[6] proposed “Data warehousing and data mining techniques for intrusion detection systems” the
aim was to improve the performance and usability of Intrusion Detection Systems (IDS). The
system model network traffic and alerts using a multi-dimensional data model and star schemas
to perform network security analysis and to detect denial of service attacks. A prototype of the
system was successfully tested at Army Research Labs.
[9] proposed “DBMS Application Layer Intrusion Detection for Data Warehouses (DW)”. They
argued that the current DIDS lack capacity to detect heterogeneous oriented DW. In the research
work, specific requirements for data warehouse based IDS was defined which lead to the
proposal of a conceptual approach for a real-time DIDS for DWs at the SQL command level that
works transparently as an extension of the Database Management System (DBMS) between the
user applications and the database server itself.
In their review, [25] looked into four different approaches to intrusion detection in a network
environment. The approaches are: Artificial Neural Network (ANN) “is comprised of a collection
of processing elements that are highly interconnected, and convert a set of inputs to a set of
desired outputs”; Self Organizing Map (SOM), Fuzzy Logic and Support Vector Machine
(SVM).
Table 1. shows the summary of related literatures
Methodology/
Tools
Contribution
Research Gap
R language and
Python machine
learning
packages.
DT-SVMNB that
classifies users as
depressed one or
suicidal one in the
social network
Focus was on
predicting vulnerable
users on the social
media not hackers
Sentiment
analysis, NLP
based Part-Of-
Speech (POS)
tagging
Developed a model
that can analyze the
opinions posted on the
internet to classify
them as good, bad, or
neutral.
Does not cover the
hackers’ intrusion
detection on the social
media platforms
SVM, Neural
Network, TFIDF
and sentiment
analysis
algorithms
Achieved average f-
score of 91.9% and
89.8% for NN and
SVM respectively on
the detection.
Handled only one
aspect of social media
attacks –
cyberbullying. Does
not cover anomaly
intrusion
Django, NLP,
NN, CNN
Developed a
customized platform
and implemented
anomaly post detection
The intrusion
detection can only
detect cyberbullying
attack
Oracle 11g
DBMS, TPC-H
benchmark
Proposed DBMS
Application Layer
Intrusion Detection for
Data Warehouses
Hackers with
persistent attack can
evade the security
mechanism
Qualitative
highlighted four
different approaches to
intrusion detection in a
network environment
There was no
proposed design
MariaDB,
MYSQL, TPC-H
Developed IDS for
data warehouse with
Resilient hacker can
use their hacking
70 Computer Science & Information Technology (CS & IT)
second level
authentication to
reinforce access
control security
techniques to
overcome the second
level authentication
JDK 1.7,
MYSQL,
NETBEANS
Developed IDS for
college ERP system
The IDS only detect
inconsistent data
entry to the ERP
system
Python libraries,
CCIDS, RFE
Achieved TPR
of99.9% and FPR of
0.1% on CCIDS
Decision trees have a
tendency to over-fit
and can create
biasness
Data mining
MySQL,
SNORT, Fuzzy
Logic
Used data mining
techniques to process
the network data used
to predict anomaly
detection.
Rule-based models
are limited by the
knowledge of the
expert that developed
sit
Java, Traffic
Sniffer
used to detect specific
attacks which are Man-
in-the- Middle, DOS
and ping of death
Focused on NID for
Man-in-the- Middle,
DOS and ping of
death, detection
algorithm not
specified
STAR schemas,
OLAP
improved the
performance and
usability of Intrusion
Detection Systems
(IDS with the star
schema
The improvement was
for network intrusion,
not DW intrusion
detection
conceptual model
Identified 4 main
functions of social
network usage in
higher education:
academic service
support; student
support; social and
cooperate
learning; and
achievement
representation
Social vulnerability of
the network was not
explored. This will
affect efficient
learning
WEKA
Justified that attribute
selection in will
improve Intrusion
Detection System
IDS was not
developed
2.6. Research/Knowledge Gap
There is presently no developed intrusion detection system for social media platform to curtail
the activities of hackers that have turned their attention to the platform. Most of the literatures
reviewed do not have the intelligence to detect anomaly usage of social media account.
Role Based Access Control (RBAC), Extended RBAC, Temporal RBAC (TRBAC), Risk-based
access control, etc does not have the ability to detect an attacker who obtains access to the system
using some compromised credentials. Intrusion Detection System (IDS) and some other
Computer Science & Information Technology (CS & IT) 71
customized security solutions for DWs have also been proposed including second level
authentication. But the same mechanism used to evade the first level authentication can still be
employed to overcome the second level authentication security approach. Hence fooling the
attacker with fake response will provide a better reinforcement to DW intrusion
detection/prevention mechanism.
Most of the previous proposals used KDD-CUP-99and DARPA 98/99 dataset for training but
these datasets have become outdated with limitations over updating of new attacks [8]. Those
earlier models might not work well owing to the fact that attackers changes their signatures
regularly to evade detection.
3. CONCLUSIONS
Social media network has become the nerve centre of the virtual community that connects
billions of heterogeneous users for mutual interaction. Because of its dynamic nature where users
can share contents freely between friends and followers, hackers are seriously exploiting this rich
platform for malicious intention. Various strategies for attacking social media users have been
highlighted in this paper with various preventive approaches proposed by researchers. Despite all
these preventive approaches, hackers activities on the platform is on the rise hence social media
intrusion detection system will be highly recommended as second line of defence against the
attacks of hackers on the social media network platforms.
ACKNOWLEDGEMENTS
The authors would like to thank my lecturers for their constructive criticism and impact!
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AUTHORS
Emmanuel Etuh is currently pursuing a PhD in Computer Science at the University of
Nigeria, Nsukka, He obtained his first degree certificate in Computer Science from
Kogi State University, Anyigba in 2009 and an MSc degree in Computer Science from
Ahmadu Bello University, Zaria in 2014, His research interest include Artificial
Intelligence, CyberSecurity, and Software Engineering.
Professor Bakpo, Francis Sunday received his M Eng. degree in Computer Science and Engineering from
Kazakh National Technical University, Almaty (formerly USSR) in 1994 and PhD
degree in Computer Engineering in 2008 from Enugu State University of Science and
Technology (ESUT), Agbani. He joined the Department of Computer Science at
University of Nigeria Nsukka as lecturer II in June 1996 and further progressed from
the rank of lecturer II to Professor in 2010. His current research interest includes
Computer architectures, computer communications networking, artificial neural network applications, and
Petri net theory. He is dully registered professionally as member Nigeria Computer Society, MNCS and
Computer professional of Nigeria, MCPN, respectively and has also published a number of excellent
journal papers, books and conference proceeding papers in his field.
Eneh, Agozie H is a Senior Lecturer in the Department of Computer Science at the
University of Nigeria, Nsukka. His Area of Specialization include: authentication
protocols analysis, network security, optimisation theories, medical informatics, and
assistive technologies for educating children and adolescents with learning difficulties.
© 2021 By AIRCC Publishing Corporation. This article is published under the Creative
Commons Attribution (CC BY) license.