Статті в журналах: "Active Malware Analysis"

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Joshi, Ankit, Komesh Borkar, Rohit Dhote, Saurabh Raut, Swapnil Thomare, Raghavendra Kulkarni, and Sharda Chhabria. "A Machine Learning Technique to Detect Malware." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 188–93. http://dx.doi.org/10.22214/ijraset.2022.47841.

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Abstract: Organizations have been threatened by malware for a long time, but timely detection of the virus remains a challenge. Malware may quickly damage the system by doing pointless tasks that burden it and prevent it from operating efficiently. There are two ways to detect malware: the traditional method that relies on the malware's signature and the behavior-based approach. The malware's behavior is characterized by the action it conducts when active in the machine, such as executing the operating system functions and downloading infected files from the internet. Based on how it behaves, the suggested algorithm finds the virus. The suggested model in this study is a hybrid of Support Vector Machine and Principle Component Analysis. For real Malware, our suggested model obtained an accuracy of 92.70% during validation, with 96% precision, 96.32% recall, and an f1- score of .96

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Miraglia, Armando, and Matteo Casenove. "Fight fire with fire: the ultimate active defence." Information & Computer Security 24, no. 3 (July 11, 2016): 288–96. http://dx.doi.org/10.1108/ics-01-2015-0004.

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Purpose This paper proposes an approach to deal with malware and botnets, which in recent years have become one of the major threats in the cyber world. These malicious pieces of software can cause harm not only to the infected victims but also to actors at a much larger scale. For this reason, defenders, namely, security researchers and analysts, and law enforcement have fought back and contained the spreading infections. However, the fight is fundamentally asymmetric. Design/methodology/approach In this paper, the authors argue the need to equip defenders with more powerful active defence tools such as malware and botnets, called antidotes, which must be used as last resort to mitigate malware epidemics. Additionally, the authors argue the validity of this approach by considering the ethical and legal concerns of leveraging sane and compromised hosts to mitigate malware epidemics. Finally, the authors further provide evidence of the possible success of these practices by applying their approach to Hlux, Sality and Zeus malware families. Findings Although attackers have neither ethical nor legal constraints, defenders are required to follow much stricter rules and develop significantly more intricate tools. Additionally, attackers have been improving their malware to make them more resilient to takeovers. Originality/value By combining existing research, the authors provide an analysis and possible implication of a more intrusive yet effective solution for fighting the spreading of malware.

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Zhang, Hong, Shumin Yang, Guowen Wu, Shigen Shen, and Qiying Cao. "Steady-State Availability Evaluation for Heterogeneous Edge Computing-Enabled WSNs with Malware Infections." Mobile Information Systems 2022 (April 11, 2022): 1–16. http://dx.doi.org/10.1155/2022/4743605.

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To evaluate the steady-state availability of heterogeneous edge computing-enabled wireless sensor networks (HECWSNs) with malware infections, we first propose a Stackelberg attack-defence game to predict the optimal strategies of malware and intrusion detection systems (IDSs) deployed in heterogeneous sensor nodes (HSNs). Next, we present a new malware infection model—heterogeneous susceptible-threatened-active-recovered-dead (HSTARD) based on epidemic theory. Then, considering the heterogeneity of sink sensor nodes and common sensor nodes and the malware attack correlation, we derive the state transition probability matrix of an HSN based on a semi-Markov process (SMP), as well as the steady-state availability of an HSN. Furthermore, based on a data flow analysis of HSNs, we deduce the steady-state availability of HECWSNs with various topologies, including the star topology, cluster topology, and mesh topology. Finally, numerical analyses illustrate the influence of the IDS parameters on the optimal infection probability of malware and reveal the effect of multiple factors on the steady-state availability of HSNs, including the initial infection rate, the infection change rate, and the malware attack correlation. In addition, we present data analyses of the steady-state availability of HECWSNs with various topologies, including the star topology, cluster topology, and mesh topology, which provide a theoretical basis for the design, deployment, and maintenance of high-availability HECWSNs.

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Shatnawi, Ahmed S., Aya Jaradat, Tuqa Bani Yaseen, Eyad Taqieddin, Mahmoud Al-Ayyoub, and Dheya Mustafa. "An Android Malware Detection Leveraging Machine Learning." Wireless Communications and Mobile Computing 2022 (May 6, 2022): 1–12. http://dx.doi.org/10.1155/2022/1830201.

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Android applications have recently witnessed a pronounced progress, making them among the fastest growing technological fields to thrive and advance. However, such level of growth does not evolve without some cost. This particularly involves increased security threats that the underlying applications and their users usually fall prey to. As malware becomes increasingly more capable of penetrating these applications and exploiting them in suspicious actions, the need for active research endeavors to counter these malicious programs becomes imminent. Some of the studies are based on dynamic analysis, and others are based on static analysis, while some are completely dependent on both. In this paper, we studied static, dynamic, and hybrid analyses to identify malicious applications. We leverage machine learning classifiers to detect malware activities as we explain the effectiveness of these classifiers in the classification process. Our results prove the efficiency of permissions and the action repetition feature set and their influential roles in detecting malware in Android applications. Our results show empirically very close accuracy results when using static, dynamic, and hybrid analyses. Thus, we use static analyses due to their lower cost compared to dynamic and hybrid analyses. In other words, we found the best results in terms of accuracy and cost (the trade-off) make us select static analysis over other techniques.

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Londoño, Sebastián, Christian Urcuqui, Manuel Fuentes Amaya, Johan Gómez, and Andrés Navarro Cadavid. "SafeCandy: System for security, analysis and validation in Android." Sistemas y Telemática 13, no. 35 (December 3, 2015): 89–102. http://dx.doi.org/10.18046/syt.v13i35.2154.

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Android is an operating system which currently has over one billion active users for all their mobile devices, a market impact that is influencing an increase in the amount of information that can be obtained from different users, facts that have motivated the development of malicious software by cybercriminals. To solve the problems caused by malware, Android implements a different architecture and security controls, such as a unique user ID (UID) for each application, while an API permits its distribution platform, Google Play applications. It has been shown that there are ways to violate that protection, so the developer community has been developing alternatives aimed at improving the level of safety. This paper presents: the latest information on the various trends and security solutions for Android, and SafeCandy, an app proposed as a new system for analysis, validation and configuration of Android applications that implements static and dynamic analysis with improved ASEF. Finally, a study is included to evaluate the effectiveness in threat detection of different malware antivirus software for Android.

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Sartea, Riccardo, Alessandro Farinelli, and Matteo Murari. "SECUR-AMA: Active Malware Analysis Based on Monte Carlo Tree Search for Android Systems." Engineering Applications of Artificial Intelligence 87 (January 2020): 103303. http://dx.doi.org/10.1016/j.engappai.2019.103303.

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O'Callaghan, Derek, Martin Harrigan, Joe Carthy, and Pádraig Cunningham. "Network Analysis of Recurring YouTube Spam Campaigns." Proceedings of the International AAAI Conference on Web and Social Media 6, no. 1 (August 3, 2021): 531–34. http://dx.doi.org/10.1609/icwsm.v6i1.14288.

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As the popularity of content sharing websites has increased, they have become targets for spam, phishing and the distribution of malware. On YouTube, the facility for users to post comments can be used by spam campaigns to direct unsuspecting users to malicious third-party websites. In this paper, we demonstrate how such campaigns can be tracked over time using network motif profiling, i.e. by tracking counts of indicative network motifs. By considering all motifs of up to five nodes, we identify discriminating motifs that reveal two distinctly different spam campaign strategies, and present an evaluation that tracks two corresponding active campaigns.

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Duraisamy Soundrapandian, Pradeepkumar, and Geetha Subbiah. "MULBER: Effective Android Malware Clustering Using Evolutionary Feature Selection and Mahalanobis Distance Metric." Symmetry 14, no. 10 (October 21, 2022): 2221. http://dx.doi.org/10.3390/sym14102221.

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Symmetric and asymmetric patterns are fascinating phenomena that show a level of co-existence in mobile application behavior analyses. For example, static phenomena, such as information sharing through collaboration with known apps, is a good example of a symmetric model of communication, and app collusion, where apps collaborate dynamically with unknown malware apps, is an example of a serious threat with an asymmetric pattern. The symmetric nature of app collaboration can become vulnerable when a vulnerability called PendingIntent is exchanged during Inter-Component Communication (ICC). The PendingIntent (PI) vulnerability enables a flexible software model, where the PendingIntent creator app can temporarily share its own permissions and identity with the PendingIntent receiving app. The PendingIntent vulnerability does not require approval from the device user or Android OS to share the permissions and identity with other apps. This is called a PI leak, which can lead to malware attacks such as privilege escalation and component hijacking attacks. This vulnerability in the symmetric behavior of an application without validating an app’s privileges dynamically leads to the asymmetric phenomena that can damage the robustness of an entire system. In this paper, we propose MULBER, a lightweight machine learning method for the detection of Android malware communications that enables a cybersecurity system to analyze multiple patterns and learn from them to help prevent similar attacks and respond to changing behavior. MULBER can help cybersecurity teams to be more proactive in preventing dynamic PI-based communication threats and responding to active attacks in real time. MULBER performs a static binary analysis on the APK file and gathers approximately 10,755 features, reducing it to 42 key features by grouping the permissions under the above-mentioned four categories. Finally, MULBER learns from these multivariate features using evolutionary feature selection and the Mahalanobis distance metric and classifies them as either benign or malware apps. In an evaluation of 22,638 malware samples from recent Android APK malware databases such as Drebin and CICMalDroid-2020, MULBER outperformed others by clustering applications based on the Mahalanobis distance metric and detected 95.69% of malware with few false alarms and the explanations provided for each detection revealed the relevant properties of the detected malware.

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Nawaz, Umair, Muhammad Aleem, and Jerry Chun-Wei Lin. "On the evaluation of android malware detectors against code-obfuscation techniques." PeerJ Computer Science 8 (June 21, 2022): e1002. http://dx.doi.org/10.7717/peerj-cs.1002.

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The Android mobile platform is the most popular and dominates the cell phone market. With the increasing use of Android, malware developers have become active in circumventing security measures by using various obfuscation techniques. The obfuscation techniques are used to hide the malicious code in the Android applications to evade detection by anti-malware tools. Some attackers use the obfuscation techniques in isolation, while some attackers use a mixed approach (i.e., employing multiple obfuscation techniques simultaneously). Therefore, it is crucial to analyze the impact of the different obfuscation techniques, both when they are used in isolation and when they are combined as hybrid techniques. Several studies have suggested that the obfuscation techniques may be more effective when used in a mixed pattern. However, in most of the related works, the obfuscation techniques used for analysis are either based on individual or a combination of primitive obfuscation techniques. In this work, we provide a comprehensive evaluation of anti-malware tools to gauge the impact of complex hybrid code-obfuscations techniques on malware detection capabilities of the prominent anti-malware tools. The evaluation results show that the inter-category-wise hybridized code obfuscation results in more evasion as compared to the individual or simple hybridized code obfuscations (using multiple and similar code obfuscations) which most of the existing related work employed for the evaluation. Obfuscation techniques significantly impact the detection rate of any anti-malware tool. The remarkable result i.e., almost 100% best detection rate is observed for the seven out of 10 tools when analyzed using the individual obfuscation techniques, four out of 10 tools on category-wise obfuscation, and not a single anti-malware tool attained full detection (i.e., 100%) for inter-category obfuscations.

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Wu, Xiaojun, Qiying Cao, Juan Jin, Yuanjie Li, and Hong Zhang. "Nodes Availability Analysis of NB-IoT Based Heterogeneous Wireless Sensor Networks under Malware Infection." Wireless Communications and Mobile Computing 2019 (January 3, 2019): 1–9. http://dx.doi.org/10.1155/2019/4392839.

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The Narrowband Internet of Things (NB-IoT) is a main stream technology based on mobile communication system. The combination of NB-IoT and WSNs can active the application of WSNs. In order to evaluate the influence of node heterogeneity on malware propagation in NB-IoT based Heterogeneous Wireless Sensor Networks, we propose a node heterogeneity model based on node distribution and vulnerability differences, which can be used to analyze the availability of nodes. We then establish the node state transition model by epidemic theory and Markov chain. Further, we obtain the dynamic equations of the transition between nodes and the calculation formula of node availability. The simulation result is that when the degree of node is small and the node vulnerability function is a power function, the node availability is the highest; when the degree of node is large and the node vulnerability function satisfies the exponential function and the power function, the node availability is high. Therefore, when constructing a NBIOT-HWSNs network, node protection is implemented according to the degree of node, so that when the node vulnerability function satisfies the power function, all nodes can maintain high availability, thus making the entire network more stable.

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McDonald, Grant, Pavlos Papadopoulos, Nikolaos Pitropakis, Jawad Ahmad, and William J. Buchanan. "Ransomware: Analysing the Impact on Windows Active Directory Domain Services." Sensors 22, no. 3 (January 26, 2022): 953. http://dx.doi.org/10.3390/s22030953.

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Ransomware has become an increasingly popular type of malware across the past decade and continues to rise in popularity due to its high profitability. Organisations and enterprises have become prime targets for ransomware as they are more likely to succumb to ransom demands as part of operating expenses to counter the cost incurred from downtime. Despite the prevalence of ransomware as a threat towards organisations, there is very little information outlining how ransomware affects Windows Server environments, and particularly its proprietary domain services such as Active Directory. Hence, we aim to increase the cyber situational awareness of organisations and corporations that utilise these environments. Dynamic analysis was performed using three ransomware variants to uncover how crypto-ransomware affects Windows Server-specific services and processes. Our work outlines the practical investigation undertaken as WannaCry, TeslaCrypt, and Jigsaw were acquired and tested against several domain services. The findings showed that none of the three variants stopped the processes and decidedly left all domain services untouched. However, although the services remained operational, they became uniquely dysfunctional as ransomware encrypted the files pertaining to those services.

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Oksiiuk, Oleksandr, Andriy Zerko, and Andriy Fesenko. "ANALYSIS OF THE PRIMARY TRENDS IN CYBERSECURITY." Information systems and technologies security, no. 1 (2) (2020): 3–8. http://dx.doi.org/10.17721/ists.2020.1.3-8.

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Open and free cyberspace increases the freedom of people and social communications, in such conditions it becomes especially important to search for new possibilities of ensuring the state security in view of the formation of a new confrontation field - cyberspace. It is important to analyze the actual problems of information security, actions of the world governments and world organizations for identifying the current state of modern trends in the cybersecurity field. Cybersecurity incidents affect the lives of consumers of informational and many other services, and cyberattacks targeting various objects of electronic communications infrastructure or process management. This article covers in detail the factors that influence the state of cybersecurity in the country, its cyberspace and the protection of information objects. The rapid development of malicious software in the world and the lines of action by famous hacker groups are analyzed. The tendencies of active legislative updates in the cybersecurity field of the world’s leading countries, such as creating new structural groups, increasing the number of existing ones and increasing their funding, are identified. The reasons for attackers concentrating their efforts on the search for assets vulnerabilities and the development of a unique multifunctional malware and technologies for unauthorized assets are considered. Structured information about the status of modern trends in the field of cybersecurity and information protection is presented in this article. The situation that has evolved to date with cybercrime requires continuous improvement of cybercrime fighting methods, development of information systems and methods aimed at ensuring the country's cybersecurity. Therefore, the issue of cyberspace security, cybercrime fighting is relevant internationally as well as at the national level and therefore needs further consideration.

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Bux, Khuda, Muhammad Yousaf, Akhtar Hussain Jalbani, and Komal Batool. "Detection of Malicious Servers for Preventing Client-Side Attacks." January 2021 40, no. 1 (January 1, 2021): 230–40. http://dx.doi.org/10.22581/muet1982.2101.20.

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The number of client-side attacks is increasing day-by-day. These attacks are launched by using various methods like phishing, drive-by downloads, click-frauds, social engineering, scareware, and ransomware. To get more advantage with less exertion and time, the attackers are focus on the clients, rather than servers which are more secured as compared to the clients. This makes clients as an easy target for the attackers on the Internet. A number of systems/tools have been created by the security community with various functions for detection of client-side attacks. The discovery of malicious servers that launch the client side attacks can be characterized in two types. First to detect malicious servers with passive detection which is often signature based. Second to detect the malicious servers with active detection often with dynamic malware analysis. Current systems or tools have more focus on identifying malicious servers rather than preventing the clients from those malicious servers. In this paper, we have proposed a solution for the detection and prevention of malicious servers that use the Bro Intrusion Detection System (IDS) and VirusTotal API 2.0. The detected malicious link is then blocked at the gateway.

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Patel, Himanshu, Deep Patel, Jaspreet Ahluwalia, Vaishali Kapoor, Karthik Narasimhan, Harmanpreet Singh, Harmanjot Kaur, Gadi Harshitha Reddy, Sai Sushma Peruboina, and Sergey Butakov. "Evaluation of Survivability of the Automatically Obfuscated Android Malware." Applied Sciences 12, no. 10 (May 14, 2022): 4969. http://dx.doi.org/10.3390/app12104969.

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Malware is a growing threat to all mobile platforms and hundreds of new malicious applications are being detected every day. At the same time, the development of automated software obfuscation techniques allows for the easy production of new malware variants even by attackers with entry-level programming skills. Such obfuscation techniques can evade the signature-based mechanism implemented in current antimalware technology. This paper presents the results of a study that examined how automated obfuscation techniques affect malicious and benign applications by two widely used malware detection approaches, namely static and dynamic analyses. The research explored 5000 samples of malware and benign programs and evaluated the impact of automated obfuscation on Android applications. The experimental results indicated that (1) up to 73% of the reviewed applications “survived” the automated obfuscation; (2) automated obfuscation reduced the detection ratio to 65–85% depending on the obfuscation method used. These findings call for a more active use of advanced malware detection methods in commonly used antivirus platforms.

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Biberaj, Aleksander, Enida Sheme, Alban Rakipi, Sonila Xhaferllari, Renalda Kushe, and Mirjeta Alinci. "Cyber Attack Against E-Albania and Its Social, Economic and Strategic Effects." Journal of Corporate Governance, Insurance, and Risk Management 9, no. 2 (December 31, 2022): 341–47. http://dx.doi.org/10.56578/jcgirm090204.

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Purpose: During last years, even because of pandemic situation caused by covid-19 virus, in Albania most of governmental public services for citizens, businesses and other customers were offered in an electronic way by creating a national database (e-Albania), offering more than 2200 services. As this electronic system was newly implemented, time after time it was attacked from hackers in different sectors of services, causing the interruption of service for hours, downloading all the confidential information and publishing them. After several partial attacks, in July 2022 came the general attack of the whole system, which black out the system and services for several days. Cyber actors - identifying as “HomeLand Justice” - launched a destructive cyber-attack against e-Albania which rendered websites and services unavailable. An investigation indicates cyber actors acquired initial access to the victim’s network approximately 14 months before launching the destructive cyber-attack, which included a ransomware-style file encryptor and disk wiping malware. The actors maintained continuous network access for approximately a year, periodically accessing and exfiltrating e-mail content. From late July to mid-August 2022, social media accounts associated with HomeLand Justice demonstrated a repeated pattern of advertising Albanian Government information for release, posting a poll asking respondents to select the government information to be released by HomeLand Justice, and then releasing that information - either in a .zip file or a video of a screen recording with the documents shown. This cyber-attack creates social problems, economical loss and influenced negatively in the reputation of e-Albania and damage as well strategically the country and development of this sector in the future. Methodology: We have monitored the system and the attack, and we continue to do this. We analyze and synthesis the data collected, to come to conclusions and recommendations needed for the future. All the data which we have used are open for public, and mostly are primary data. The research method combines both quantitative and qualitative methods, but it is closer with qualitative method, as far as there in not enough data for using e pure quantitative analysis. We have used mostly the descriptive method. Results/Findings: Improving essentially the cyber infrastructure to avoid in the future such attacks with high social, economic and strategical cost. Conclusions: In the institution there was not a team for Cyber Security Monitoring the system, so called SOC (Security Operation Center), who controls in the real time all the logins. It was missing as well so called “Identifying Behavior”. There was not e separation of active directory, in physic machines and virtual machines, they were altogether. As the administrator had Full Right Privilege, the hacker doesn’t need to create a Privilege Escalation Vertical, so he easily took all the right of Admin. Originality and Practical Implications: The paper is original; it has not been previously published and it is not under consideration by any other publisher. The originality of the method stands in the fact that it is the first case in the world in information age, that a country (a whole electronic system, e-Albania), face a such complex, well organized and hard cyber-attack, which collapse the system for several days. All the data are authentic ones.

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16

Biran, Yahav, George Collins, Borky John M, and Joel Dubow. "Volume 2, Issue 3, Special issue on Recent Advances in Engineering Systems (Published Papers) Articles Transmit / Received Beamforming for Frequency Diverse Array with Symmetrical frequency offsets Shaddrack Yaw Nusenu Adv. Sci. Technol. Eng. Syst. J. 2(3), 1-6 (2017); View Description Detailed Analysis of Amplitude and Slope Diffraction Coefficients for knife-edge structure in S-UTD-CH Model Eray Arik, Mehmet Baris Tabakcioglu Adv. Sci. Technol. Eng. Syst. J. 2(3), 7-11 (2017); View Description Applications of Case Based Organizational Memory Supported by the PAbMM Architecture Martín, María de los Ángeles, Diván, Mario José Adv. Sci. Technol. Eng. Syst. J. 2(3), 12-23 (2017); View Description Low Probability of Interception Beampattern Using Frequency Diverse Array Antenna Shaddrack Yaw Nusenu Adv. Sci. Technol. Eng. Syst. 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17

Khan, Mossaraf. "Malware Analysis Using Volatility." Digital Forensics (4n6) Journal, May 1, 2020, 8–14. http://dx.doi.org/10.46293/4n6/2020.02.02.01.

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Volatile Data of a computer is a temporary and they are created when a computer system is running aka in operational condition. They are removed immediately when the system powered off. It is stored on the Random Access Memory (RAM) and other temporary storage units such as Registars of the Computer and not in the main storage partitions of Hard Drives. It could be emails related information, chats or browser history, running processes related information, unsaved data, clipboard contents etc. The analysis of volatile memory for extracting forensic artifacts is called Memory Forensic. Volatile Memory contains the most valuable information about running programs and instructions including running system processes, kernel drivers, loaded modules, executed commands, executable paths, active Network Connections, etc.

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18

Dambra, Savino, Leyla Bilge, and Davide Balzarotti. "A Comparison of Systemic and Systematic Risks of Malware Encounters in Consumer and Enterprise Environments." ACM Transactions on Privacy and Security, October 3, 2022. http://dx.doi.org/10.1145/3565362.

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Malware is still a widespread problem and it is used by malicious actors to routinely compromise the security of computer systems. Consumers typically rely on a single AV product to detect and block possible malware infections, while corporations often install multiple security products, activate several layers of defenses, and establish security policies among employees. However, if a better security posture should lower the risk of malware infections, the actual extent to which this happens is still under debate by risk analysis experts. Moreover, the difference in risks encountered by consumers and enterprises has never been empirically studied by using real-world data. In fact, the mere use of third-party software, network services, and the interconnected nature of our society necessarily exposes both classes of users to undiversifiable risks: independently from how careful users are and how well they manage their cyber hygiene, a portion of that risk would simply exist because of the fact of using a computer, sharing the same networks, and running the same software. In this work, we shed light on both systemic (i.e., diversifiable and dependent on the security posture) and systematic (i.e., undiversifiable and independent of the cyber hygiene) risk classes. Leveraging the telemetry data of a popular security company, we compare, in the first part of our study, the effects that different security measures have on malware encounter risks in consumer and enterprise environments. In the second part, we conduct exploratory research on systematic risk, investigate the quality of nine different indicators we were able to extract from our telemetry, and provide, for the first time, quantitative indicators of their predictive power. Our results show that even if consumers have a slightly lower encounter rate than enterprises (9.8% vs 12.0%), the latter do considerably better when selecting machines with an increasingly higher uptime (89% vs 53%). The two segments also diverge when we separately consider the presence of Adware and Potentially Unwanted Applications (PUA), and the generic samples detected through behavioral signatures: while consumers have an encounter rate for Adware and PUA that is 6 times higher than enterprise machines, those on average match behavioral signatures two times more frequently than the counterpart. We find, instead, similar trends when analyzing the age of encountered signatures, and the prevalence of different classes of traditional malware (such as Ransomware and Cryptominers). Finally, our findings show that the amount of time a host is active, the volume of files generated on the machine, the number and reputation of vendors of the installed applications, the host geographical location and its recurrent infected state carry useful information as indicators of systematic risk of malware encounters. Activity days and hours have a higher influence in the risk of consumers, increasing the odds of encountering malware of 4.51 and 2.65 times. In addition, we measure that the volume of files generated on the host represents a reliable indicator, especially when considering Adware. We further report that the likelihood of encountering Worms and Adware is much higher (on average 8 times in consumers and enterprises) for those machines that already reported this kind of signatures in the past.

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