The area of invention for this study is in the intersection of artificial intelligence,
cybersecurity, and protection of privacy. It is the innovation of Al-based systems to
identify and prevent spyware and stalkerware, two types of ever-growing malicious
software that invade user privacy and security. Through the utilization of sophisticated
machine learning methods, behavioral analysis, and network traffic monitoring, this
invention seeks to establish an intelligent detection system with the ability to
accurately detect potentially malicious apps. Not only does this innovation expand
upon current cybersecurity technologies, but it also impacts the larger landscape of
personal data protection in the digital world through the provision of a proactive
response to privacy endangerment by covert surveillance apps. Unlike other detection
solutions based on signature-based approaches, which can detect only known threats,
this system utilizes machine learning to learn new and emerging spyware and
stalkerware. The feature of learning from new data makes the detection system
efficient against new threats. The solution emphasizes protecting user privacy by
detecting applications that monitor or record user activity secretly without consent. By
detecting suspicious software at an early stage, users can be warned of possible
invasions of privacy, avoiding long-term exposure and abuse of personal information.
BACKGROUND OF INVENTION
In the hyper-connected digital age of today, smartphone and mobile app usage has
become a part of daily life. But this heightened connectivity has also brought with it
new and emerging threats. Among the most troubling of these threats are stalkerware
and ·Spyware-malicious software that is intended to track, monitor. and harvest
personal data without the knowledge or permission of the user. These applicatious are
able to retrieve sensitive information like call history, messages, location, microphone,
camera, and so on, usually without the victim's awareness. Historically, security and antivirus software usc signature-based detection, which detects known malware based
on previously established threat definitions. This method is not effective against
zero-day attacks, custom spyware, or newer versions that hide their presence through
obfuscation. Specifically, stalkerware tends to impersonate legitimate applications,
making it even harder to detect using conventional means. With the increasing cases
of cyberstalking and digital privacy invasion, there is a pressing need for intelligent,
adaptive, and proactive security measures.
SUMMARY OF INVENTION
The present invention pertains to a mobile security app intended for iOS platforms
that identifies stalkerware, spyware, malware, and rogue system activity based on a
hybrid of AI, ML, and heuristic analysis. The invention otTers a multi-module
framework with static and behavioral application analysis, live anomaly detection,
firewall potency scanning, and virus signature checking via SHA-256 hashing and
rule-based detection. The system gathers information from application behavior,
permission access, CPU usage, microphone and camera usage, and network traffic to
detect unusual patterns that signal malicious activity. The ML module uses supervised
and unsupervised models such as Random Forest, SVM, CNNs, and RNNs to detect
known and zero threats. A threat visualizer is integrated to otTer graphical views of
detection results for user understanding. Further, the invention also incorporates
real-time tracking of unauthorized logins, background access, and suspect API calls,
detecting potential threats at an early stage. The user interface is created in Flutter to
support cross-platform functionality with interactive charts and real-time notification.
The application adds digital security through proactive detection of threats while
maintaining user privacy, providing a new, light-weight, and intelligent solution for
protection of personal devices DETAILED DESCRIPTION OF OUTPUT SCREENSHOTS
This invention oilers a complete spyware and stalkerware detection solution with an
- - --- - -- - - --
integrated dashboard visualizing threats, system activity, and firewall health. It
observes real-lime network and file-level anomalies, and identifies concealed spyware
or stalkcrwarc. A graphical interface features live activity timelines, threat level
graphs, and malware metadata analysis for simplicity. The application proviues a
one-click scan facility with in-depth information and recommended actions to increase
device security.
We claim that,
I. The Al-powered defence identifies spyware and stalker-ware on smartphones by
observing app behavior, such as permission use, background processes, and data
access patterns.
2. The Al-powered defence uses a machine learning-based model trained on labeled
datasets of safe and unsafe applications to identify potential threats and analyse it
by behavioral features.
3. The Al-powered defence includes heuristic scanning to recognize unknown or
obfuscated threats using pre-defined rules and anomaly patterns and visualises it.
4. The Al-powered defence provides users with real-time notifications upon
identifying suspicious activity and offers actionable mitigation actions like
penn ission revocation or app removal.
5. The Al-powered defence contains a proof generation module that assembles threat
evidence -such as logs and activity traces for user examination ·or legal record.