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Suki, Norazah Mohd, T. Ramayah, Au Soo Nee, and Norbayah Mohd Suki. "Consumer Intention to Use Anti-Spyware Software." International Journal of Technology and Human Interaction 10, no. 3 (July 2014): 19–31. http://dx.doi.org/10.4018/ijthi.2014070102.
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Awareness of the importance to install anti-spyware software in one's computer has increased in this digital world. This research aims to investigate the relationship between relative advantage, moral compatibility, ease of use, subjective norms, image, computing capacity, perceived cost, and trialability, and the consumer intention to use the anti-spyware software. Data was coded and analyzed in the Statistical Package for Social Science (SPSS) computerized software version 21 and analyzed by means of Exploratory Factor Analysis (EFA). Next, Structural Equation Modeling (SEM) technique via Analysis of Moment Structure (AMOS) computer program version 21 was used for data analysis in order to achieve the research objective. Results validated the hypotheses and revealed that five vital factors: Ease of use, relative advantage, image, perceived cost, and moral compatibility influenced consumer intention to use the anti-spyware software. The ease of use dimension has the greatest impact on consumer intention to use the anti-spyware software. The developers of the anti-virus software or the software development companies should specifically target the right target groups, in terms of its market segmentation, targeting, and positioning, to boost up consumer awareness and deployment of the anti-spyware software. This research brings implication in terms of it does help the researchers and IT professionals to understand factors that influence consumer intention to use anti-spyware software.
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Klang, Mathias. "Spyware ? the ethics of covert software." Ethics and Information Technology 6, no. 3 (2004): 193–202. http://dx.doi.org/10.1007/s10676-004-3392-3.
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Kucera, Kyle, Michel Plaisent, Prosper Bernard, and Lassana Maguiraga. "An empirical investigation of the prevalence of spyware in internet shareware and freeware distributions." Journal of Enterprise Information Management 18, no. 6 (December 1, 2005): 697–708. http://dx.doi.org/10.1108/17410390510628391.
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PurposeSpyware is a controversial software technology that allows the surreptitious collection of personal information from computers linked to the internet. The purpose of this study was to determine the prevalence of spyware in internet shareware and freeware and to investigate the personal information collected.Design/methodology/approachThe methodology was a two‐phase, multi‐case study. The first phase determined that five of the 50 most popular pieces of Windows®‐based freeware and shareware available to internet users from CNETDownload.com were suspected of containing spyware: these were included as data sources for phase two. The purpose of phase two was to confirm the existence of spyware and to identify the types and frequencies of any personal data transmissions. For this phase, data were collected and analyzed utilizing a passive network monitor program to examine packets of data transmitted from a personal computer to external destinations on the internet.FindingsThe findings confirmed the existence and use of spyware in three of five suspected cases. However, the data indicated that there was a low occurrence of spyware and that these programs have the capability to collect numerous types of personal data. The main limitation is that these results are based on a snapshot of data obtained during five days.Practical implicationsThe study has practical implications for internet users, who should be aware that spyware exists and understand its potential threat. Spyware developers should provide the user with an effective removal tool. Finally, marketers are cautioned that spyware might alienate customers.Originality/valueThis paper confirms the potential for misuse of these programs.
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Klang, Mathias. "Spyware: paying for software with our privacy." International Review of Law, Computers & Technology 17, no. 3 (November 2003): 313–22. http://dx.doi.org/10.1080/1360086032000174405.
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Damanik, Annisa Rizky, Henki Bayu Seta, and Theresiawati Theresiawati. "ANALISIS TROJAN DAN SPYWARE MENGGUNAKAN METODE HYBRID ANALYSIS." Jurnal Ilmiah Matrik 25, no. 1 (May 23, 2023): 89–97. http://dx.doi.org/10.33557/jurnalmatrik.v25i1.2327.
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Malicious Software or malware is software created to damage a computer system. The increase in internet users is also in line with the increase in the use of software. However, there are still many users who still use pirated software because it is relatively free and easy to obtain. Pirated software is usually embedded with dangerous malware such as Trojans and spyware. All crimes of spreading this malware are always related to stealing credit card information, internet banking and other cybercrimes. To prove that the software installed and used on a computer is malicious software, digital forensics is required by analyzing the software. Hybrid analysis technique is a combination of static and dynamic analysis which is suitable for analyzing malware activity. Based on the results of the analysis that has been carried out, the ryuk.bin trojan has evolved and the malware forms new malware files when it is run and also changes and destroys the original files on the system.
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Ames, W. "Understanding spyware: risk and response." IT Professional 6, no. 5 (September 2004): 25–29. http://dx.doi.org/10.1109/mitp.2004.71.
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Goud, Ediga Komal, and Gadekari Rohith Reddy. "Keylogger Surveillance Software." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (March 31, 2023): 1406–9. http://dx.doi.org/10.22214/ijraset.2023.49683.
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Abstract: A Keylogger is a spyware tool used to record targets movements. Keystroke are forensic technology used to monitor and record every keystroke written on a computer .Keylogging often used as a hacking tool by black hat hackers to steal login details of an employee and confidential company information. Keylogging presents a special challenge to security managers In contrast to customary worms and infections, particular sorts of keyloggers are everything except difficult to identify. Programming keyloggers are exceptionally well known instrument which are in many cases used to reap secret data. One of the fundamental purposes behind this fast development of keyloggers is the opportunities for unprivileged programs running in client space to snoop and screen every one of the keystrokes composed by the clients of a framework. Execution and Dissemination of these sort of keyloggers are exceptionally simple due to the capacity to run in unprivileged mode. However, simultaneously, permits one to comprehend and demonstrate their conduct exhaustively.
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Abdul Wahed, Ali Abdul Hussien. "Local and Public Traffic Concrete Barrier (TCB)." Journal of Kufa for Mathematics and Computer 1, no. 2 (October 30, 2010): 1–11. http://dx.doi.org/10.31642/jokmc/2018/010201.
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This research presents the Local and Public Traffic Concrete Barrier software (TCB) which work as a first defensive line to the computer from any remotely orlocally attackers or intruders. The keyfeature in the TCB software that it does not depend or use the built in windows firewall which is became week against the new attacking techniques. The TCB software can blocks all TCP process based on rules that can be specified and saved by the user such as base on Process Name, Remote IP or Remote/Local Port. In order to make the user rules unchangeable the TCB is password protected software with 3 different security levels and it can also Logs and save all the activity. Dependingon the user defined rules the TCB act like a Virus/Spyware/Adware basic protector that alerts the user when it s automatically scans any Processes, IP or Port that matches with the user preset rules. All theabove mentioned TCB capabilities and features helps to keep the computer more secure. As well it restricts information that comes to the computer from other computers,providing more control over the data on the computer and providing a line of defense against people or programs (including viruses and worms) that attempts to connect to the computer without invitation (unsolicitedrequest).
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Luo, Xin. "A Holistic Approach for Managing Spyware." EDPACS 33, no. 11 (May 2006): 1–9. http://dx.doi.org/10.1201/1079.07366981/45977.33.11.20060501/93007.1.
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Gordon, Sarah. "Fighting Spyware and Adware in the Enterprise." EDPACS 32, no. 12 (June 2005): 14–18. http://dx.doi.org/10.1201/1079/45242.32.12.20050601/88294.2.
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Rossetto, Anubis Graciela de Moraes, Darlan Noetzold, Luis Augusto Silva, and Valderi Reis Quietinho Leithardt. "Enhancing Monitoring Performance: A Microservices Approach to Monitoring with Spyware Techniques and Prediction Models." Sensors 24, no. 13 (June 28, 2024): 4212. http://dx.doi.org/10.3390/s24134212.
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In today’s digital landscape, organizations face significant challenges, including sensitive data leaks and the proliferation of hate speech, both of which can lead to severe consequences such as financial losses, reputational damage, and psychological impacts on employees. This work considers a comprehensive solution using a microservices architecture to monitor computer usage within organizations effectively. The approach incorporates spyware techniques to capture data from employee computers and a web application for alert management. The system detects data leaks, suspicious behaviors, and hate speech through efficient data capture and predictive modeling. Therefore, this paper presents a comparative performance analysis between Spring Boot and Quarkus, focusing on objective metrics and quantitative statistics. By utilizing recognized tools and benchmarks in the computer science community, the study provides an in-depth understanding of the performance differences between these two platforms. The implementation of Quarkus over Spring Boot demonstrated substantial improvements: memory usage was reduced by up to 80% and CPU usage by 95%, and system uptime decreased by 119%. This solution offers a robust framework for enhancing organizational security and mitigating potential threats through proactive monitoring and predictive analysis while also guiding developers and software architects in making informed technological choices.
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Reddy, Reddyvari Venkateswara, Gattu Laxmi Priya, K. Varshini, and Mahankali Varun. "Spyware Surveillance on Keyboard Through Electronic Messaging Protocol." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (April 30, 2024): 609–15. http://dx.doi.org/10.22214/ijraset.2024.59835.
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Abstract: The cause of the keylogger characteristic described on this paper is to reveal and file using the programs on the laptop system. The software makes use of the Python programming language and libraries such as pynput, smtplib, pyautogui, and OpenCV. The important features of this keylogger include taking pictures keystrokes, shooting timestamps, viewing energetic window titles, typing search queries, and appearing actions with right mouse clicks. The keylogger runs in the heritage and captures all keystrokes by means of the user, including letters, places, and input key presses. Timestamps are recorded to test the timing of each keystroke. In addition, the program retrieves the title of the active window to offer a description of the keystroke. It also lists the phrases or queries that the user searched for. In addition, the keylogger is programmed to take a screenshot of the display screen and locate a right mouse click on and take a photo of the consumer with a web browser and then e mail those screenshots and pictures to the designated recipient. The electronic mail provider is created the use of the smtplib library, which permits keyloggers to safely ship recorded statistics to an e-mail cope with. Overall, this keylogger function offers high stages of surveillance, allowing diffused tracking of person pastime and providing precious insights into their interactions with the computer machine
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Fakiha, Bandr. "The Role of Raspberry Pi in Forensic Computer Crimes." Journal of Internet Services and Information Security 13, no. 4 (December 2, 2023): 76–87. http://dx.doi.org/10.58346/jisis.2023.i4.005.
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This study examined the use of Raspberry Pi for forensic analysis and its potential for providing computer protection. It investigates its capability to identify suspicious patterns on USB drives and its compatibility with certain software. Furthermore, it examines the ability of the Raspberry Pi to generate reports and send email notifications upon detection of any malicious files such as worms, Trojans, and spyware. Additionally, it investigates Raspberry Pi's ability to move potentially malicious files to a secure trash folder for sanitization. The experiment was designed using an experimental method. The findings suggest that the Raspberry Pi can provide computer protection through automatic report generation and email notifications for malicious file detection. As a result, this study provides evidence that Raspberry Pi can effectively provide digital forensics services. Its quick file search capabilities make it a powerful tool that can help organizations gather crucial digital evidence that may otherwise remain undetected.
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Kikelomo, Akinwole Agnes, Yekini Nureni Asafe, and Ogundele Israel Oludayo. "Malware Detection System Using Mathematics of Random Forest Classifier." International Journal of Advances in Scientific Research and Engineering 09, no. 03 (2023): 45–53. http://dx.doi.org/10.31695/ijasre.2023.9.3.6.
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Most cyberattacks including data breaches, identity theft, fraud, and other issues, are known to be caused by malware. Some of the malware attacks are categories as adware, spyware, virus, worm, trojan, rootkit, backdoor, ransomware and command and control (C&C) bot, based on its purpose and behaviour. Malware detectors still utilise signature-based approaches to detect malicious software, which can only detect known malware. Attacks by malware pose a serious threat to people's and organizations' cybersecurity globally. These attacks are occurring more frequently and more frequently lately. Over eight billion malware attacks occurred in 2020, up 4% over the previous year, according to a Symantec report. It is crucial that computer users safeguard their computers with a malware detector like an antivirus, anti-spyware, etc. When creating a machine learning model to differentiate between malicious and benign files, it might be challenging to use domain-level expertise to extract the necessary attributes. This research aims to create a malware detector that uses a trained random forest classifier model to find malware and stop zero-day assaults. A dataset (including both harmful and benign software PE header information) was obtained from virusshare.com and used to train the random forest classifier in order to create this malware detector. The Random Forest Classifier generate greater accuracy when compared with other machine learning classifiers, such as KNN (K-Nearest Neighbors), Decision Tree, Logistic Regression etc., the random forest classifier gives a better accuracy of 99.4%. The Classifier model used here will be a better option to use in order to efficiently and effectively detect malware, it shows that the methodology can be utilized as the basis for an operational system for detecting an unknown malicious executable.
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Waliulu, Raditya Faisal, and Teguh Hidayat Iskandar Alam. "Reverse Engineering Analysis Statis Forensic Malware Webc2-Div." Insect (Informatics and Security): Jurnal Teknik Informatika 4, no. 1 (August 23, 2019): 15. http://dx.doi.org/10.33506/insect.v4i1.223.
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At this paper focus on Malicious Software also known as Malware APT1 (Advance Persistent Threat) codename WEBC2-DIV the most variants malware has criteria consists of Virus, Worm, Trojan, Adware, Spyware, Backdoor either Rootkit. Although, malware could avoidance scanning antivirus but reverse engineering could be know how dangerous malware infect computer client. Lately, malware attack as a form espionage (cyberwar) one of the most topic on security internet, because of has massive impact. Forensic malware becomes indicator successful user to realized about malware infect. This research about reverse engineering. A few steps there are scanning, suspected packet in network and analysis of malware behavior and disassembler body malware.
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Lavesson, Niklas, Martin Boldt, Paul Davidsson, and Andreas Jacobsson. "Learning to detect spyware using end user license agreements." Knowledge and Information Systems 26, no. 2 (January 16, 2010): 285–307. http://dx.doi.org/10.1007/s10115-009-0278-z.
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English, Ed. "Why You Should Not Play the Numbers Game with Anti-Spyware Vendors." EDPACS 33, no. 8 (February 2006): 20–24. http://dx.doi.org/10.1201/1079.07366981/45802.33.8.20060201/91958.3.
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Labinsky, Alexander. "THE MONITORING OF OPERATING SYSTEM AS THE MEANS OF THE INFORMATION PROTECTION." NATURAL AND MAN-MADE RISKS (PHYSICO-MATHEMATICAL AND APPLIED ASPECTS) 2024, no. 1 (April 17, 2024): 16–23. http://dx.doi.org/10.61260/2307-7476-2024-1-16-23.
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The article considers one of the methods of information protection on the computer – monitoring of the operating system.
Malicious software for computers are numerous, they are constantly updated and new programs with new principles of malicious work appear. Therefore, the use of antivirus and anti-spyware is often ineffective, as they work on the principle of signature search (sample search) and therefore cannot detect new varieties of malware.
The solution to this problem is the use of various utilities (service programs) for monitoring (research) of the operating system installed on the computer.
The article describes in detail the utilities for monitoring the operating system installed on the PC. This is a monitoring utility of the operating system: File Monitor (monitoring file operations), which allows you to monitor all file operations in real time; Registry Monitor (monitoring registry operations); TCP View (monitoring network activity)a feature of which is the binding of the listening port or open connection to the process that uses it; the auto run application control utility Auto Runs, which analyzes dozens of different autorun methods, including classic autorun methods, extensions of different types of explorer, scheduler jobs, services and drivers, print library and providers; Process Explorer utility, which allows you to change the priority of the process, stop the process and all its threads, force the process to end.
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Al-Marghilani, A. "Comprehensive Analysis of IoT Malware Evasion Techniques." Engineering, Technology & Applied Science Research 11, no. 4 (August 21, 2021): 7495–500. http://dx.doi.org/10.48084/etasr.4296.
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Malware detection in Internet of Things (IoT) devices is a great challenge, as these devices lack certain characteristics such as homogeneity and security. Malware is malicious software that affects a system as it can steal sensitive information, slow its speed, cause frequent hangs, and disrupt operations. The most common malware types are adware, computer viruses, spyware, trojans, worms, rootkits, key loggers, botnets, and ransomware. Malware detection is critical for a system's security. Many security researchers have studied the IoT malware detection domain. Many studies proposed the static or dynamic analysis on IoT malware detection. This paper presents a survey of IoT malware evasion techniques, reviewing and discussing various researches. Malware uses a few common evasion techniques such as user interaction, environmental awareness, stegosploit, domain and IP identification, code obfuscation, code encryption, timing, and code compression. A comparative analysis was conducted pointing various advantages and disadvantages. This study provides guidelines on IoT malware evasion techniques.
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Kamarudin, Nur Khairani, Nur Nazifa Md Hasani, Rafiza Ruslan, Rashidah Ramle, Nurul Hidayah Ahmad Zukri, and Iman Hazwam Abd Halim. "Performance Analysis of Malware Attack." Journal of Computing Research and Innovation 3, no. 4 (November 18, 2018): 19–24. http://dx.doi.org/10.24191/jcrinn.v3i4.72.
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People in this new era of modernization nowadays take Internet as one of the vital thing for daily activities. Internet is not only for adults, it is also a needs for people of all ages. However, network vulnerabilities exist in all network that are connec ted to the Internet. The network mostly are exposed to the malicious software or mostly known as malware. In fact, this malware is growing rapidly and giving a bad impact to the human intervention. The number of attack are increasing rapidly and it comes i n various way just to exploit the victims. There are various type of malware attack. For instance, viruses, worms, spyware, rootkits, Trojan horse and botnet are considered as noteworthy threat for the computer network. Some people giving full confidence on the security of data transmission to the network. However, other can access the personal information without them realizing it. The objective of this paper is to detect malware attack using honeypot Dionaea. Malicious file launched was detected by the honeypot and the file was analyzed by using the sandbox tool, Virus Total. This paper found that honeypot Dionaea is helpful in detecting various types of malware attack.
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K, Valarmathi, Hemalatha S, Perumal P, Puthilibai D, and Abirami M. "Shoulder Surfing Attack Trusted Verification – A Survey." ECS Transactions 107, no. 1 (April 24, 2022): 16041–47. http://dx.doi.org/10.1149/10701.16041ecst.
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Individual performance such as select a mistaken code word or inflowing a code word in an unsure of yourself manner result in the easiest linkage in authentication. An invader is able to harm the hardware, software, or information by exploiting the weakest link. As a result, our goal is to give a elegant method for users to validate their bank accounts. In computer or IT security, authentication-based passwords are used. As an alternative of using alphanumeric as a code word, the user is able to choose to use an image as a password. The customer is capable of depicting the shoulder surfing attack using the mobile application. Passwords can be observed by attackers using spyware or shoulder surfing. To solve the problem, the Pass Matrix authentication system is proposed, which is based on graphical passwords and can withstand a shoulder surfing attack. Pass Matrix does not provide any suggestion or stature, still when conducting camera-based attacks, through a just the once suitable login display and navigation button cover the whole range of pass-image. As a result, the future system outperforms the competition in provisos of shoulder surfing resistance.
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Faisal, Mohammad, and Sa’ed Abed. "Cluster-Based Antiphishing (CAP) Model for Smart Phones." Scientific Programming 2021 (July 7, 2021): 1–9. http://dx.doi.org/10.1155/2021/9957323.
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Different types of connectivity are available on smartphones such as WiFi, infrared, Bluetooth, GPRS, GPS, and GSM. The ubiquitous computing features of smartphones make them a vital part of our lives. The boom in smartphone technology has unfortunately attracted hackers and crackers as well. Smartphones have become the ideal hub for malware, gray ware, and spyware writers to exploit smartphone vulnerabilities and insecure communication channels. For every security service introduced, there is simultaneously a counterattack to breach the security and vice versa. Until a new mechanism is discovered, the diverse classifications of technology mean that one security contrivance cannot be a remedy for phishing attacks in all circumstances. Therefore, a novel architecture for antiphishing is mandatory that can compensate web page protection and authentication from falsified web pages on smartphones. In this paper, we developed a cluster-based antiphishing (CAP) model, which is a lightweight scheme specifically for smartphones to save energy in portable devices. The model is significant in identifying, clustering, and preventing phishing attacks on smartphone platforms. Our CAP model detects and prevents illegal access to smartphones based on clustering data to legitimate/normal and illegitimate/abnormal. First, we evaluated our scheme with mathematical and algorithmic methods. Next, we conducted a real test bed to identify and counter phishing attacks on smartphones which provided 90% accuracy in the detection system as true positives and less than 9% of the results as true negative.
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Kauser.Sk, Heena, and Maria Anu.V. "A Hybrid Model for Android Malware Detection using Decision Tree and KNN." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 1s (December 15, 2022): 321–28. http://dx.doi.org/10.17762/ijritcc.v10i1s.5899.
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Malwares are becoming a major problem nowadays all around the world in android operating systems. The malware is a piece of software developed for harming or exploiting certain other hardware as well as software. The term Malware is also known as malicious software which is utilized to define Trojans, viruses, as well as other kinds of spyware. There have been developed many kinds of techniques for protecting the android operating systems from malware during the last decade. However, the existing techniques have numerous drawbacks such as accuracy to detect the type of malware in real-time in a quick manner for protecting the android operating systems. In this article, the authors developed a hybrid model for android malware detection using a decision tree and KNN (k-nearest neighbours) technique. First, Dalvik opcode, as well as real opcode, was pulled out by using the reverse procedure of the android software. Secondly, eigenvectors of sampling were produced by utilizing the n-gram model. Our suggested hybrid model efficiently combines KNN along with the decision tree for effective detection of the android malware in real-time. The outcome of the proposed scheme illustrates that the proposed hybrid model is better in terms of the accurate detection of any kind of malware from the Android operating system in a fast and accurate manner. In this experiment, 815 sample size was selected for the normal samples and the 3268-sample size was selected for the malicious samples. Our proposed hybrid model provides pragmatic values of the parameters namely precision, ACC along with the Recall, and F1 such as 0.93, 0.98, 0.96, and 0.99 along with 0.94, 0.99, 0.93, and 0.99 respectively. In the future, there are vital possibilities to carry out more research in this field to develop new methods for Android malware detection.
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Bauri, Mr Chandra Kant, Mr Chetan Indulkar, Mr Shantanu Jadhav,, and Prof Anjali S. Khandagale. "Windows Post Exploitation [MSF] Keylogger for Security." International Journal for Research in Applied Science and Engineering Technology 10, no. 4 (April 30, 2022): 1329–33. http://dx.doi.org/10.22214/ijraset.2022.41509.
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Abstract: Keyloggers or keystroke loggers are software programs or hardware devices that track the activities (keys pressed) of a keyboard. Keyloggers are a form of spyware where users are unaware their actions are being tracked. Keyloggers can be used for a variety of purposes; hackers may use them to maliciously gain access to your private information, while employers might use them to monitor employee activities. Some keyloggers can also capture your screen at random intervals; these are known as screen recorders. Keylogger software typically stores your keystrokes in a small file, which is either accessed later or automatically emailed to the person monitoring your actions. You'll find the use of keyloggers in everything from Microsoft products to your own employer's computers and servers. In some cases, your spouse may have put a keylogger on your phone or laptop to confirm their suspicions of infidelity. Worse cases have shown criminals to implant legitimate websites, apps, and even USB drives with keylogger malware. Whether for malicious intent or legitimate uses, you should be aware of how keyloggers are affecting you. First, we'll further define keystroke logging before diving into how keyloggers work. Keywords: Keylogger, Reverse Shell, Post-Exploitation, Metasploit, Netcat, Intranet, privilege escalation.
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Abbas, Umar Farouk, and Abdulrahman Abdulkarim. "PERFORMANCE ANALYSIS OF CAPTCHA BASED BLOCKING AND RESPONSE OF AN INTRUSION DETECTION MODEL USING SIGNATURE." FUDMA JOURNAL OF SCIENCES 6, no. 3 (July 6, 2022): 248–53. http://dx.doi.org/10.33003/fjs-2022-0603-998.
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Intrusion Detection System is the process of intelligently monitoring the events occurring in a computer system or network, analyzing them for signs of violations of a security policy. Its primary aim is to protect the availability, confidentiality and integrity of critical networked information systems. This paper considered and reviewed a CAPTCHA based intrusion detection model. A method of incorporating signature was used along with the CAPTCHA in the intrusion detection model to clear the controversy identified in the existing model. The signature provides a means of identifying intruders that are able to by-pass the system as legitimate users. The model was implemented using a website hosted online. Dataset obtained from the site was analyzed based on Naïve Bayes classification model using confusion matrices. Implementation of the data analysis was carried out using RStudio software package. Analyzed results shows a better Detection Rate (DR), Accuracy (CR) and False Positive Rate (FPR). This shows that the developed system has significant capability of identifying intelligent spywares targeted at breaking CAPTCHA
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Bauri, Chandra Kant, Chetan Indulkar, Shantanu Jadhav, and Prof Anjali S. Khandagale. "A Survey on Windows Post Exploitation [MSF] Keylogger for Security." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 721–26. http://dx.doi.org/10.22214/ijraset.2022.40684.
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Abstract: Keyloggers or keystroke loggers are software programs or hardware devices that track the activities (keys pressed) of a keyboard. Keyloggers are a form of spyware where users are unaware their actions are being tracked. Keyloggers can be used for a variety of purposes; hackers may use them to maliciously gain access to your private information, while employers might use them to monitor employee activities. Some keyloggers can also capture your screen at random intervals; these are known as screen recorders. Keylogger software typically stores your keystrokes in a small file, which is either accessed later or automatically emailed to the person monitoring your actions. You’ll find use of keyloggers in everything from Microsoft products to your own employer’s computers and servers. In some cases, your spouse may have put a keylogger on your phone or laptop to confirm their suspicions of infidelity. Worse cases have shown criminals to implant legitimate websites, apps, and even USB drives with keylogger malware. Whether for malicious intent or for legitimate uses, you should be aware how keyloggers are affecting you. First, we’ll further define keystroke logging before diving into how keyloggers work. Then you’ll be able to better understand how to secure yourself from unwanted eyes. You might find legal keyloggers are in your daily life more than you realized. Fortunately, the power to control your data is often in your hands if the monitoring party has asked for access. Outside of employment, you can simply decline permission to the keyloggers if you so choose. Keywords: Keylogger, Reverse Shell, Post-Exploitation, Metasploit, Netcat, Intranet, privilege escalation.
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Lima Carvajal, Roberth Gabriel, and Marco Inlago. "Spyware." INNOVATION & DEVELOPMENT IN ENGINEERING AND APPLIED SCIENCES 3, no. 2 (February 3, 2022). http://dx.doi.org/10.53358/ideas.v3i2.630.
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Computer security is a topic that has been gaining strength in the last decade, this because it is worrying the possibility that the government or companies outside the law to monitor citizens. Spyware, a software that does nothing more than violate the privacy of people who unfortunately are infected with one or more and this is done in a hidden and quite silent way. So it is of utmost importance to know, recognize and even determine how to fight against these tiny cyber threats. This small software leaks, searches and steals information abruptly, which could cause financial catastrophes in a family, or in a company, selling information or taking advantage of all the stolen content to wreak havoc. Although some Spyware developers sell their product as a way to control employees, it does not detract from the fact that their program handles the same operation as if to do evil.
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"Put spyware on the security." ITNOW 47, no. 1 (December 16, 2004): 18–19. http://dx.doi.org/10.1093/combul/bwi006.
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"Prevention of Cyber Crime in Bangladesh." International Journal for Empirical Education and Research, April 30, 2019, 33–47. http://dx.doi.org/10.35935/edr/34.4733.
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Cybercrime covers such a broad scope of criminal activity; the examples above are only a few of the thousands of crimes that are considered cybercrimes. While computers and the Internet have made our lives easier in many ways, it is unfortunate that people also use these technologies to take advantage of others. Therefore, it is smart to protect yourself by using antivirus and spyware blocking software and being careful where you enter your personal information. So from above discussion we can say that, Cyber means committing any crime by using computer or any act which is forbidden by law, by using information technology.
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Naser, Muawya, Hussein Albazar, and Hussein Abdel-Jaber. "Mobile Spyware Identification and Categorization: A Systematic Review." Informatica 47, no. 8 (September 28, 2023). http://dx.doi.org/10.31449/inf.v47i8.4881.
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R., Manasa, and A. Jayanthila Devi. "Cyber Security Attacks Detecting Thread in the Virtual World of Corporate Sectors." International Journal of Case Studies in Business, IT, and Education, April 12, 2023, 90–105. http://dx.doi.org/10.47992/ijcsbe.2581.6942.0261.
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Purpose: Attempting to get access to a computer, computer network, or computing system without authorization is known as a cyber-attack. To modify, impede, erase, manipulate or steal data from computer systems is the purpose of a cyber-attack. These attacks may be carried out in a number of ways. This placeholder information is used to identify a single instance of the use of a prgramme that may support numerous users at once. A thread is information that a programme requires to serve a single user or a single service request. Cybercriminals make use of technology to do malicious actions on digital systems or networks in order to make a profit. These crimes include hacking computer systems and stealing confidential information from businesses and individuals. A thorough study on the algorithms to detect threats in the virtual world of corporate sectors. Finding/Result: Researchers are using a wide array of deep learning algorithms to achieve this goal, and the results have been rather impressive. A system like this may provide substandard results because to its limited ability to describe the problem area and the complexity of its modeling of hazardous behaviours. Supervised learning systems often deliver a high level of accuracy because of the large amount of data made available by manually labelled samples. Originality/Value: Antivirus software is an absolute need for any and all computers. The vast majority of antivirus software is able to identify malicious software such as malware, spyware, ransomware, and harmful email attachments. Paper Type: Literature Review.
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Waliulu, Raditya Faisal. "Reverse Engineering Reverse Engineering Analysis Forensic Malware WEBC2-Div." Journal of Informatics, Information System, Software Engineering and Applications (INISTA) 1, no. 1 (September 26, 2018). http://dx.doi.org/10.20895/inista.v1i1.10.
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At this paper focus on Malicious Software also known as Malware APT1 (Advance Persistent Threat) codename WEBC2-DIV the most variants malware has criteria consists of Virus, Worm, Trojan, Adware, Spyware, Backdoor either Rootkit. Although, malware could avoidance scanning antivirus but reverse engineering could be know how dangerous malware infect computer client. Lately, malware attack as a form espionage (cyberwar) one of the most topic on security internet, because of has massive impact. Forensic malware becomes indicator successful user to realized about malware infect. This research about reverse engineering. A few steps there are scanning, suspected packet in network and analysis of malware behavior and disassembler body malware.
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Asir, D. "Enhancing Malware Detection through Machine Learning: A Comparative Analysis of Random Forest and Naive Bayes Classification Systems." International Journal of Advanced Research in Science and Technology, 2024, 1288–92. http://dx.doi.org/10.62226/ijarst20241332.
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Malware, a type of malicious software encompassing viruses, worms, Trojans, backdoors, and spyware, poses a grave threat to the confidentiality, integrity, and functionality of computer systems, given their integral role in everyday life. To combat the escalating sophistication of malware attacks, deep-learning-based Malware Detection Systems (MDSs) have emerged as indispensable components of both economic and national security. Utilizing a dataset sourced from a repository, our research focuses on classifying observations into benign and malicious software for Android devices, employing machine learning algorithms such as Random Forest and Naïve Bayes. The dataset comprises 100,000 observations with 35 features, and our evaluation metrics encompass accuracy, precision, recall, and F1-score. This study underscores the significance of MDSs in safeguarding against evolving cyber threats, utilizing cutting-edge machine learning techniques to bolster defense mechanisms.
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Waliulu, Raditya Faisal, and Teguh Hidayat Iskandar Alam. "REVERSE ENGINEERING ANALYSIS FORENSIC MALWARE WEBC2-DIV." KOMIK (Konferensi Nasional Teknologi Informasi dan Komputer) 2, no. 1 (October 5, 2018). http://dx.doi.org/10.30865/komik.v2i1.902.
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At this paper focus on Malicous Software also known as Malware APT1 (Advance Persistent Threat) codename WEBC2-DIV the most variants malware has criteria consists of Virus, Worm, Trojan, Adware, Spyware, Backdoor either Rootkit. Although, malware could avoidance scanning antivirus but reverse engineering could be know how dangerous malware infect computer client. Lately, malware attack as a form espionage (cyberwar) one of the most topic on security internet, because of has massive impact. Forensic malware becomes indicator successfull user to realized about malware infect. This research about reverse engineering. A few steps there are scanning, suspected packet in network and analysis of malware behavior and dissambler body malware.Keyword : forensic malware, Analysis, Advance Presistent Threat, Cyberwar, dissambler
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Downs, Daniel M., Ilir Ademaj, and Amie M. Schuck. "Internet security: Who is leaving the ‘virtual door’ open and why?" First Monday, December 26, 2008. http://dx.doi.org/10.5210/fm.v14i1.2251.
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The purpose of the present paper is to study Chicago residents’ knowledge about Internet security and their utilization of prevention and detection tools. The results from hierarchical linear models suggest that there are significant gender, race, age, and community differences in knowledge about firewalls, spyware, phishing and data encryption, as well as the utilization of prevention and detection tools such as anti-virus programs, pop-up blockers and parental control software. Further, diffusion of innovation theory and utopian and dystopian perspectives toward technology help to explain some, but not all, of the variation in peoples’ knowledge about Internet threats and their use of security measures. These findings should help experts identify those people that may be more susceptible to cyber victimization, and highlight the importance of users’ behavior in the realm of Internet security.
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"Spyware Infiltration Rises in Corporate Networks, But Webroot Survey Finds Companies Still Neglect Threat." EDPACS 32, no. 7 (January 2005): 24. http://dx.doi.org/10.1201/1079/44911.32.7.20050101/85732.3.
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"Detection of Malicious Uniform Resource Locator." International Journal of Recent Technology and Engineering 8, no. 2 (July 30, 2019): 41–47. http://dx.doi.org/10.35940/ijrte.a1265.078219.
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With the growing use of internet across the world ,the threats posed by it are numerous. The information you get and share across the internet is accessible, can be tracked and modified. Malicious websites play a pivotal role in effecting your system. These websites reach users through emails, text messages, pop ups or devious advertisements. The outcome of these websites or Uniform Resource Locators (URLs) would often be a downloaded malware, spyware, ransomware and compromised accounts. A malicious website or URL requires action on the users side, however in the case of drive by only downloads, the website will attempt to install software on the computer without asking users permission first. We put forward a model to forecast a URL is malicious or benign, based on the application layer and network characteristics. Machine learning algorithms for classification are used to develop a classifier using the targeted dataset. The targeted dataset is divided into training and validation sets. These sets are used to train and validate the classifier model. The hyper parameters are tuned to refine the model and generate better results
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Xiao, Peng. "Network Malware Detection Using Deep Learning Network Analysis." Journal of Cyber Security and Mobility, December 11, 2023. http://dx.doi.org/10.13052/jcsm2245-1439.1312.
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Malware, short for malicious software, is designed for harmful purposes and threatens network security because it can propagate without human interaction by exploiting user’s vulnerabilities and carelessness. Having your system regularly scanned for malicious software is essential for keeping hackers at bay and avoiding the disclosure of sensitive data. The major drawbacks are the rapid creation of new malware variants, and it may become difficult to detect existing threats. With the ever-increasing volume of Android malware, the sophistication with which it can hide, and the potentially enormous value of data assets stored on Android devices, detecting or classifying Android malware is a big data problem. Security researchers have developed various malware detection and prevention programs for servers, gateways, user workstations, and mobile devices. Some offer centralized monitoring for malware detection software deployed on many systems or computers. The purpose of this essay is to critically examine the research that has been done specifically on malware detection. This paper proposes the Anti-Virus Software Detection for Malware with Deep Learning Network (AVSD-MDLN) framework to explore the possible threats. The two methods help in finding the threats. Dynamic Analysis for the Detection of Spyware (DA-DS) framework is framed to detect malicious malware, while the other is for classifying Android malware which is helped out through the Category in an Ensemble (CE) method. Prior malware detection methods are compared with the results of the proposed method. According to the research findings, the proposed approach achieves a higher projected time (0.5 sec) and detection accuracy (97.47%) than the existing situation machine learning and deep learning methodologies. Performance, correlation coefficient, and recall rate all improved in the suggested framework. Likewise, the negative rate (MPR) and the positive rate (PPR) also improved.
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Almoqbil, Abdullah Hamad N. "Anomaly detection for early ransomware and spyware warning in nuclear power plant systems based on FusionGuard." International Journal of Information Security, April 13, 2024. http://dx.doi.org/10.1007/s10207-024-00841-z.
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Hossain, Md Alamgir, and Md Saiful Islam. "Enhanced detection of obfuscated malware in memory dumps: a machine learning approach for advanced cybersecurity." Cybersecurity 7, no. 1 (January 25, 2024). http://dx.doi.org/10.1186/s42400-024-00205-z.
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AbstractIn the realm of cybersecurity, the detection and analysis of obfuscated malware remain a critical challenge, especially in the context of memory dumps. This research paper presents a novel machine learning-based framework designed to enhance the detection and analytical capabilities against such elusive threats for binary and multi type’s malware. Our approach leverages a comprehensive dataset comprising benign and malicious memory dumps, encompassing a wide array of obfuscated malware types including Spyware, Ransomware, and Trojan Horses with their sub-categories. We begin by employing rigorous data preprocessing methods, including the normalization of memory dumps and encoding of categorical data. To tackle the issue of class imbalance, a Synthetic Minority Over-sampling Technique is utilized, ensuring a balanced representation of various malware types. Feature selection is meticulously conducted through Chi-Square tests, mutual information, and correlation analyses, refining the model’s focus on the most indicative attributes of obfuscated malware. The heart of our framework lies in the deployment of an Ensemble-based Classifier, chosen for its robustness and effectiveness in handling complex data structures. The model’s performance is rigorously evaluated using a suite of metrics, including accuracy, precision, recall, F1-score, and the area under the ROC curve (AUC) with other evaluation metrics to assess the model’s efficiency. The proposed model demonstrates a detection accuracy exceeding 99% across all cases, surpassing the performance of all existing models in the realm of malware detection.
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Baloi, Aurel, Bogdan Belean, Flaviu Turcu, and Daniel Peptenatu. "GPU-based similarity metrics computation and machine learning approaches for string similarity evaluation in large datasets." Soft Computing, June 14, 2023. http://dx.doi.org/10.1007/s00500-023-08687-8.
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AbstractThe digital era brings up on one hand massive amounts of available data and on the other hand the need of parallel computing architectures for efficient data processing. String similarity evaluation is a processing task applied on large data volumes, commonly performed by various applications such as search engines, biomedical data analysis and even software tools for defending against viruses, spyware, or spam. String similarities are also used in musical industry for matching playlist records with repertory records composed of song titles, performer artists and producers names, aiming to assure copyright protection of mass-media broadcast materials. The present paper proposes a novel GPU-based approach for parallel implementation of the Jaro–Winkler string similarity metric computation, broadly used for matching strings over large datasets. The proposed implementation is applied in musical industry for matching playlist with over 100k records with a given repertory which includes a collection of over 1 million right owner records. The global GPU RAM memory is used to store multiple string lines representing repertory records, whereas single playlist string comparisons with the raw data are performed using the maximum number of available GPU threads and the stride operations. Further on, the accuracy of the Jaro–Winkler approach for the string matching procedure is increased using both an adaptive neural network approach guided by a novelty detection classifier (aNN) and a multiple-features neural network implementation (MF-NN). Thus, the aNN approach yielded an accuracy of 92% while the MF-NN approach achieved an accuracy of 99% at the cost of increased computational complexity. Timing considerations and the computational complexity are detailed for the proposed approaches compared with both the general-purpose processor (CPU) implementation and the state-of-the-art GPU approaches. A speed-up factor of 21.6 was obtained for the GPU-based Jaro–Winkler implementation compared with the CPU one, whereas a factor of 3.72 was obtained compared with the existing GPU implementation of string matching procedure based on Levenstein distance metrics.
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"Keystroke Logging: Integrating Natural Language Processing Technique to Analyze Log Data." International Journal of Innovative Technology and Exploring Engineering 9, no. 3 (January 10, 2020): 2028–33. http://dx.doi.org/10.35940/ijitee.c8817.019320.
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Cyberwarfare is observed very frequently as always some or the other country is targeting to ruin its enemy country by hacking confidential data from vital computer systems. This has led to dangerous international conflicts. Hence, to avoid illicit entry of other than military person or a government official several tools are being used today as spyware. Keyloggers are one of the prominent tools which are used in today’s world to obtain secret or confidential data of a legitimate and contradictory a malicious user too. These keyloggers are advantageous and taken up positively for monitoring employee productivity, for law enforcement and the search for evidence of the crime. While it’s negative illegitimate use includes data theft and passwords. The keylogger is today witnessed as a malicious attack and is looked upon as a security threat. But every coin has two sides. Keylogger actually helps in avoiding several security breaches and also aids in detecting several crimes across the net world followed by other fellow countries. This fact has motivated to write this paper and as a consequence, an experimental analysis too was carried out in order to conclude that keyloggers’ log file helps identify the person by analyzing proper pattern of the words entered in the file. This paper focuses majorly on the aspect of natural language processing, where a log file obtained thru keylogger software is thoroughly processed via the algorithm as described in the paper. The results yielded a fair understanding of the results obtained as one can easily identify the words used and on the basis of that can also know the type of person on the other end with his ideas, malicious one or of a legal kind.
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Himanshu, Sachin Kaushik, Pankaj Kumar, and Vimmi Malhotra. "Keystroke Logging for Activity-Monitoring using Python." International Journal of Advanced Research in Science, Communication and Technology, April 26, 2024, 684–90. http://dx.doi.org/10.48175/ijarsct-17695.
Full textAbstract:
Cyberwarfare is observed very frequently as always some or the other country is targeting to ruin its enemy country by hacking confidential data from vital computer systems. This has led to dangerous international conflicts. Hence, to avoid illicit entry of other than military person or a government official several tools are being used today as spyware. Keyloggers are one of the prominent tools which are used in today’s world to obtain secret or confidential data of a legitimate and contradictory a malicious user too. These keyloggers are advantageous and taken up positively for monitoring employee productivity, for law enforcement and the search for evidence of the crime. While it’s negative illegitimate use includes data theft and passwords. The keylogger is today witnessed as a malicious attack and is looked upon as a security threat. But every coin has two sides. Keylogger actually helps in avoiding several security breaches and also aids in detecting several crimes across the net world followed by other fellow countries. This fact has motivated to write this paper and as a consequence, an experimental analysis too was carried out in order to conclude that keyloggers’ log file helps identify the person by analyzing proper pattern of the words entered in the file. This paper focuses majorly on the aspect of natural language processing, where a log file obtained thru keylogger software is thoroughly processed via the algorithm as described in the paper. The results yielded a fair understanding of the results obtained as one can easily identify the words used and on the basis of that can also know the type of person on the other end with his ideas, malicious one or of a legal kind