Overcoming Business Security Challenges With Machine Learning Development

vipasha.singh | 26th February 2020

In a data-driven market, having analytical and predictive capabilities that effortlessly operate on ginormous data sets is essential to deal with security threats. The volume and frequency of breach attempts through malware, unauthorized entry, and intrusions into organizational data is multifarious. It is a tedious task to discover their existence on a network alone. Machine learning development services, however, expose and deal with security risks at a lightning speed.

Machine learning is one of the most effective technologies that unlocks new possibilities for developing futuristic web and mobile applications. Being a part of Artificial Intelligence (AI) services, it enables businesses to automate their inbound/outbound processes and deliver personalized customer experiences. Machine learning techniques incorporate several complex algorithms to identify patterns in data and predict future tendencies of a prototype.

At Oodles, we train machine learning (ML) algorithms to deploy predictive engines across organizations and media channels to inspect records, log analytics, and geospatial data. Thus, our tools locate anomalies explicitly by recognizing trends and observing patterns in the data.
Let us discover how employing machine learning development enables an enterprise to eliminate potential security threats.

 

Eliminating Security Threats with ML Solutions

1) IoT

An era where machine to machine interactions are greater than human to human or human to machine interaction is well on its way. The Internet of Things uses technologies like RFID for auto-identification in various industries. Protecting supply chain transaction information is vital to all businesses.

    a) Cyber Reconnaissance

Intruders in cyberspace often engineer tracking, analyzing, and engaging with a targeted system to identify it’s vulnerable nodes. They use this information to access secret information and manipulate it for their gains.
With ML, companies can significantly benefit from cyber reconnaissance. First, it increases data volume. Then, it applies advanced analytics to identify potential weaknesses, openings, and nodes exhibiting anomalous behavior. The information thus gathered facilitates resolution and remediation through preemptive actions.

    b) Brute Force Attack

Intruders target your online store’s admin panel in an attempt to figure out your password with automated software. It uses all possible permutations and combinations to get access to the desired network.
ML-aided systems automatically learn from user patterns and identify behavioral anomalies in such cases.

    c) Tracking

Plausibly, tangible or intangible factors may jeopardize the UID of an IoT device.
On such occasions, two-factor authentication methods employed by smart systems protect user data from violating.

Also read- How Cloud-based Machine Learning Solutions Amplify Business Growth

 

2) E-Commerce

Commercial transactions online are just as susceptible to transgressions as any other activity on the internet.

    a) Disclosure/demolition/mutation of data

Delinquent elements attempt to gain direct access to a company’s database through direct access or backdoor attacks that bypass the normal authentication mechanisms. Since they don’t have physical access to systems, hackers use blackhat techniques like worms and viruses to break into the network.
An AI-based machine learning antivirus software can spot unusual software behavior to identify signs of potential virus threats. Microsoft Windows Defender is a typical example.

    b) Denial of services

In a denial-of-service attack (DoS attack), the attacker takes actions that prevent permissible users from accessing network resources. They temporarily disrupt the services of a host connected to the internet by flooding the servers with multiple requests until the website crashes.
DoS attacks can be monitored using security plugins and adaptive software for firewalls.

    c) Financial Fraud

Theft of credit card information from payment gateways occurs in real-time when attackers gain access through successful phishing or brute force attack. Some attacks also happen through third-party compromises. Such breaches affect user loyalty and damage the company’s overall reputation.
SaaS security applications, as from Oodles, ensure layered security that prevents fraud, secures information standards, and builds compliant frameworks. The icing on the cake is that the SaaS provider manages updates and maintenance.

    d) Repudiation

A repudiation attack occurs when a transaction is denied by the end-user including refusal to acknowledge any communication. It can be handled by employing communication modules that require compulsory acknowledgment.

Also read- How eCommerce can harness Deep Learning for Recommendation Systems

 

3) Big Data

As of today, every organization works with extremely large data sets. They are frequently analyzed to reveal patterns, trends, and associations computationally. Therefore, it is essential to protect such a sizable amount of data from being compromised for the smooth functioning of an organization.

    a) Access to sensitive and personal information

Big Data analytics combines various data sources including machine data and analytics data clustered in such a way that routine security audits are unfeasible. With multiple vulnerable nodes and servers across the system, sensitive information is jeopardized.

Multiple factor authentication with bio-metric authentication and encryption tools that work on all types of data can mitigate such risks. It is part of granular access control, used in machine language development systems.

    b) Compromise of data rights and ownership

Different stakeholders may attempt to claim ownership of data they generate, compile, structure, add-to or obtain a license on. No single stakeholder, however, has exclusive rights. At the same time, a data breach is the liability of the data owner in a cloud environment, even if the data holder is at fault.
Such a situation is preventable. Organizations need to follow security practices like centralized key management, the secure untrusted data repository (SUNDR) and strengthen hardware and software configurations.

    c) Vulnerability to fake data generation

One of the main features of big data is its size. When not secure, fake data inputs into the system become a sizable problem. In turn, analytics processes go to waste.
Machine learning development services enable encryption for NoSQL databases and secure and protect data in real-time.

   d) Internal Data Security

Big data administrators often secure points of entry and exit without making internal security as robust.
To ensure internal data security, ML solutions use attributes based encryption as well as efficient intrusion detection and prevention systems.

Also read- Mobilizing Big Data for Cloud-based Predictive Analytics

 

Closing Remarks

Observing various threats to corporations’ databases, it can be inferred that ML-driven AI technologies are the foundation of a resilient security system. A dynamic, pro-active security solution must inevitably comprise firewalls, antivirus programs, security plugins, and encryptions supported by ML.

 

Why Choose Oodles AI For Machine Learning Development?

At Oodles, our expertise in supervised, unsupervised, semi-supervised, and reinforcement ML algorithms makes provision for the most fitting security solutions in the market. Get in touch with our AI-ML team for faster response to threats and to secure asset management.

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vipasha.singh


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