Report: Preventing Network Data Leakage and Security Threats Analysis

This report addresses the critical issue of preventing network data leakage in the current technological landscape, where internet usage and online transactions are prevalent. It defines data leakage as the unauthorized disclosure of sensitive information, highlighting its severe consequences for organizations, including reputational damage and financial losses. The report explores common threats such as website hacking, malicious software, phishing, and social engineering, which exploit vulnerabilities in financial systems and user behavior. It then delves into various detection methods, including network and host-based intrusion detection systems, signature-based recognition, and anomaly-based detection, emphasizing their roles in identifying and mitigating security breaches. Finally, the report discusses intrusion prevention systems (IPSs) and reactive recognition methods, providing a comprehensive overview of data leakage prevention strategies. The references provide further insights into the topic.

APPLICATION: PREVENTING NETWORK DATA
LEAKAGE
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Key Diagnosis
It is assumed that in the current period of technocratic domain, the persons are motivated plus
encouraged to have engaged over the internet tools and the commercial transactions through the
“easy-going” configuration of the virtual media (Abeka, 2013). The breach of information
stands as the premeditated or automatic introduction of intimate data to unofficial parties.
“Information leakage” causes severe pressures to the establishments including substantial
reputational demolition as well as economic losses.
The gaps within data are occurring over and over again, identifying and preventing loss of
information. It has turned into the most persistent safety concerns for organizations. Despite a
superfluity of investigative efforts on protecting sensitive facts from being disclosed, it keeps on
a dynamic “research problem”. Data leakage may be produced by internal plus external openings
of evidence, either purposefully (e.g., data stealing by deceivers or interference by insider
assailants) or carelessly (e.g., fortuitous revelation of sensitive records by staffs and associates)
(Hill & Power, 2012). The resolution remains to focus on organizational exposures of data leak,
schematize clarifications for data leak recognition and anticipation, and indicate future research
prospects in this extent.
(Source: Network Design: Firewall, IDS/IPS, 2019)
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Common Threats
The set of malicious operations deals with the hacking of websites of the organization and
administering the key and dynamic statistics to get-out and play trickeries skillfully to puzzle
with the aim of acquiring the enforced information which are frequently desirable to retain it by a
trusted approach. The financial settings are weakening with the acknowledgment of vindictive
software that are eradicating and boosting the other intimidations for the online securities
(Huang et al. 2013). The invaders are presently chasing the ultimate users of the subjective
processors those possess the feeblest link in the finance set-up. It is achieved by secreted actions
of diverse methods like key-logger and phishing otherwise the outbreaks of “Trojan horse”. Lee
et al. (2014) confirmed that two categories of threats comprising presentation and system based
dangers.
In the presentation risk, the “fraudster” gives the impression of a manipulator of the operational
financial application to perform the illegitimate events. The safe plus confident system like
substitute servers or firewalls and network filters etc. may not safeguard the financial
transactions from the shakedowns fabricated on the solicitation. Contrariwise, Yu (2014)
identified that the network based dangers such as unexpected assaults plus, hacking of sites, the
outbreak of viruses and worms or else denial-of-service might cause the foremost attack of
system and the setup of the institute.
The intimidations in the financial transactions within the investment subdivision may derive
from the server of the unit; otherwise from the patrons. This can create prodigious destruction
and loss to the all-inclusive operating structure of software or communication slips triggered by
human interference. The extreme contemporary security extortions stand by the way of
Phishing, Spyware and Adware, Viruses, Trojans, Key loggers (Singh & Srivastava, 2014).
The “Social engineering” like, Phishing remains a kind of fraud where the impostor’s effort to
fish is intended for getting the particular facts by crushing unreliable and hoax correspondences
demanding to be maintained by the viable establishments. This procedure of distressing the
community lingers to be taken up by the reinforcement of online raids (Huang et al. 2013).
Detection Methods of threats and remedies
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Network incursion detection apparatuses and structures are currently indispensable
for system security. Providentially, these arrangements are precisely easy to employ and
maximum of the preeminent detection systems on the marketplace are open to relate.
“Host-based detection” for intrusion inspects actions on a processor on the network
reasonably than the transportation that badges about the system. The hot based detection aims to
support the config files to enable the settings to restore specifying a malicious virus which
concerns the safety of configuration by altering the setup of the workstation. One more key
constituent needs to guard alongside stands as the core access on platforms such as UNIX or
archive modifications on WINDOWS (Lee et al. 2014). It won’t be able to block these changes,
but it should be able to alert you if any such access occurs.
A “Hot-based detection” may not stand to block the abnormalities; nevertheless it ought to be
capable to alert if some access of virus take place. But, it is further usual to set up the “Hot-
based detection” on each device on the system to log in and receive the response (Meiners et al.
2012). A circulated pattern of “Host-based detection” structure requires including an integrated
device that encodes communications among host mediators and the principal monitor.
The detection of Network-based interference would inspect the flow of data and comprise a
packaged sniffer to meet network traffic aimed at investigation. The guidelines of driving
examination correspondingly create discriminatory detention of data. A Network-based detection
needs a sensor unit to collect traffic, hence one might be capable to transport it to a LAN
analyzer, or decide on to share a computer to run the assignment. On the other hand, the tools
need to ensure sufficient clock speed to run the set-up (Singh & Srivastava, 2014).
Signature-based recognition systems can be useful by Host-based as well as network
based exposure. It may consist of a record of packet of signatures that is identified to be the
foundations of spiteful actions. Hackers employ automated measures provided by eminent
implements to produce the identical traffic signatures at each spell since computer drivers recap
the similar instructions frequently rather than leading indiscriminate variations (Network Design:
Firewall, IDS/IPS, 2019).
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Anomaly-based exposure that finds unanticipated or uncommon designs of activities might
be executed by Network and Host founded intrusions. A wide-ranging anomaly instrument traces
on the practices of AI. Nevertheless, signature-based approaches put down the assessment of
values. Indeed, in “Host-based detection”, harmonizing of patterns with file forms may remain a
precisely direct job that everyone could accomplish with command line functions using
systematic terms (Lee et al. 2014).
“Intrusion prevention systems” mechanically device defensive approach on the discovery
of an attentive disorder. A direct observing and informing scheme refers to “passive” detection.
The method of acknowledging and remediating impairment as well as blocking supplementary
activities of interruption from a perceived source may be termed as reactive recognitions (Hill &
Power, 2012). “Reactive systems or else IPSs” typically interrelate with firewalls plus
applications through modification of settings instead of direct execution of resolutions.
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References
Abeka, S. (2013). Corporate customer’s acceptance of internet banking. Hamburg: Anchor
Academic Pub.
Hill, B. & Power, D. (2012). The pocket small business owner's guide to business plans. York,
NY: Allworth Press.
Huang, D., Bevilacqua, V., Figueroa, J. & Premaratne, P. (2013). Intelligent computing theories.
Berlin: Springer.
Lee, J., Bauer, L. & Mazurek, M. (2014). Studying the Effectiveness of Security Images in
Internet Banking. IEEE Internet Comput. 1-1.
Meiners, R., Ringleb, A. & Edwards, F. (2012). The legal environment of business. Mason, OH:
South-Western Cengage Learning.
Network Design: Firewall, IDS/IPS. (2019). Retrieved from
https://resources.infosecinstitute.com/network-design-firewall-idsips/#gref
Singh, S. & Srivastava, R. (2014). Factors Influencing the Adoption of Mobile Banking in India.
International Journal of E-Services and Mobile Applications, 6(4) 1-15.
Yu, C. (2014). CONSUMER SWITCHING BEHAVIOR FROM ONLINE BANKING TO
MOBILE BANKING. International journal of cyber society and education, 7(1) 1-28.
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