Detecting and Preventing Brute Force Attacks on Mobile Devices Using Advanced Encryption Techniques
Brute pressure attacks are a not unusual approach utilized by malicious actors to gain unauthorized rights of entry to touchy records on mobile devices. Those assaults involve trying out multiple combinations of usernames and passwords until the precise one is located. This poses a huge risk to mobile security, as those devices often incorporate a tremendous quantity of personal and private information. The proposed solution is to detect and save brute force attacks on cellular gadgets by enforcing superior encryption techniques. Those techniques contain complex algorithms and methods to protect touchy records from being accessed by way of unauthorized events. This may make it easier for attackers to decipher the encrypted documents, even though they're capable of getting entry to them. The device will constantly display the get entry to attempts and locate any unusual styles or suspicious pastimes. If a brute pressure assault is detected, the device will robotically block further entry attempts and cause an alert to the consumer. It'll additionally encrypt the information in the tool to save it from being compromised. This answer will appreciably increase the security of cell gadgets because it will make it almost impossible for hackers to benefit from unauthorized admission to sensitive data. It's going to supply user's peace of thoughts, understanding that their statistics are covered, and offer an extra layer of security to cell gadgets.
Authors
- Savinder Kaur (ORCID: https://orcid.org/0000-0002-9715-4924)
- K. Gopalakrishna
- Binita Kumari (ORCID: https://orcid.org/0000-0003-0986-262X)
- Ramya G Franklin
- Mohit Gupta (ORCID: https://orcid.org/0009-0003-3449-0968)
- Trapty Agarwal Maharishi
Institutions
- Jain University (IN)
- Siksha O Anusandhan University (IN)
- Maharishi University of Management and Technology (IN)
- Sathyabama Institute of Science and Technology (IN)
- Chitkara University (IN)
Publication Details
- Published
- 2025-03-04
- DOI
- https://doi.org/10.1109/autocom64127.2025.10957378
- Primary Topic
- Advanced Malware Detection Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00