SocialLink Trust Management System colludes on OSNs.

Picture a situation in SocialLink where a group of users, part of a collaboration network, conspire to deceitfully inflate each other's trust scores. They repeatedly endorse each other's content and actions, creating a deceptive facade of trustworthiness that doesn't align with their actual behavior or interactions within the network.

The aftermath of a colluding attack in a Trust Management System like SocialLink can be catastrophic. Such attacks can compromise the integrity and reliability of the system, distorting the trust scores and recommendations provided to other users. This can lead to severe consequences for the overall trustworthiness of the system, as genuine users may be misled by the artificially inflated trust scores of the colluding users. The potential for such catastrophic consequences underscores the urgent need for preventive measures.

Colluding attacks in Trust Management Systems like SocialLink can happen in various fields and industries where reputation and trustworthiness play a crucial role. Some specific fields where colluding attacks may occur include:

Social media platforms: Users may collude to artificially boost their followers' likes, shares, and comments, creating and influencing.

Online marketplaces: Sellers may collude to give fake positive reviews of each other's products or services, misleading buyers and manipulating the reputation system.

Crowdsourcing platforms: Participants may conspire to provide biased ratings or feedback on each other's work to gain unfair advantages in competitions or project evaluations.

Online gaming communities: Players may collude to manipulate rankings and leaderboards, cheat in multiplayer games, or artificially inflate their in-game achievements.

Academic research networks: Researchers may collude to cite each other's work excessively or provide fake peer reviews to boost their publication records and academic reputation.

As professionals and stakeholders in Trust Management Systems, you are pivotal in preventing and mitigating colluding attacks. By implementing robust authentication mechanisms, regular monitoring and analysis of user interactions, and appropriate penalties for fraudulent behavior, you can contribute to maintaining the integrity of the network. Equally important is your responsibility to maintain transparency and accountability in the trust evaluation process. These principles are not just crucial, but they are the key to reducing the likelihood of colluding attacks and ensuring the system's trustworthiness. Your commitment to these principles will make the system secure and reliable.

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SocialTrust Colluding Attack on E-commerce Platforms

To detect colluding users in OSNs for a system implementing reputation system. SocialTrust modifies the weight of ratings based on the social distance and interest relationship between peers, which increases the ability of the reputation system to fight against colluding. They also claim that their mechanism can be used in any reputation system for P2P networks.

This attack type involves a group of individuals creating fake accounts and artificially boosting each other's ratings or reviews to manipulate the trustworthiness of their profiles. By artificially inflating their ratings through conspiracy, they can deceive other users into trusting them more than they deserve, potentially leading to fraudulent transactions or other malicious activities. For a clear understanding, three individuals, A, B, and C, conspire to carry out a SocialTrust attack. They create multiple fake accounts on the platform and give each other high ratings and positive reviews, falsely inflating their trustworthiness scores.

Individual A lists a product for sale on the platform and receives high ratings and reviews from individuals B and C, making their profile appear trustworthy. Buyers on the platform see these positive ratings and reviews and are more likely to trust individual A and make a purchase.

However, once the buyer purchases, Individual A never delivers the product and disappears with the buyer's money. The buyers trusted Individual A based on the fake reviews and ratings provided by Individuals B and C, who colluded with Individual A to deceive other users on the platform. This can lead to significant financial losses for the unsuspecting buyers.

This colluding attack on SocialTrust undermines the platform's integrity and deceives users into trusting malicious actors, leading to potential financial losses and significant harm to the community. It calls for collective action to prevent such incidents.

SocialTrust colluding attacks on e-commerce platforms may not be publicly colluding attacks, or similar fraudulent activities have been reported on various platforms, prompting the platforms to take measures to prevent and combat such behavior. This highlights the importance of proactive steps to prevent such attacks.

Some well-known e-commerce platforms where colluding attacks or fraudulent activities have occurred in the past include:

1.eBay: There have been instances of sellers colluding to boost their ratings and artificially deceive buyers on the platform.

2. Amazon: There have been reports of fake reviews and ratings on the platform that were used to manipulate product rankings and deceive customers.

EBay and Amazon have implemented measures to detect and prevent such fraudulent activities, including algorithms to identify suspicious behavior, strict guidelines for reviews and ratings, and mechanisms for users to report suspicious or fraudulent activities. 

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Detection of Colluding in Online Social Networks

Online Social Networks (OSNs) are public social platforms offering users services such as connecting with remote individuals, sharing activities and experiences, and, most notably, sharing personal information. However, these platforms have their risks. OSNs have become a fundamental component in people's daily lives, not just as a platform to establish contacts but also as a means of soliciting contributions for personal data. This personal data, such as relationship status, phone numbers, address, current location, and sharing information regarding their everyday lives, opinions, political affiliations, etc., can be susceptible and should be shared cautiously. The information provided helps ingrain users in the network, driving traffic and creating treasure troves of information for hackers. This vulnerability makes OSNs a prime target for malicious users who harvest personal data to conduct attacks in the future. Users must be aware of these risks and take steps to protect their data.

OSNs have caused an exploitation of online human interaction and connectivity. Each minute, 49,380 posts are created on Instagram, and 473,400 tweets are shared on Twitter. Moreover, Facebook is one of the largest and most used OSNs, with 1.5 billion daily active users. This exchange information includes Health Conditions, User status shared on their personal account's wall such as an address, Public Individual's thoughts sharings regarding Social Establishment, educational, Personal thoughts and opinions on various aspects of social engagements, and off-course Workplace information. The user shared this information with their inner trust circle.  

Detection of Collaboration Malicious Users in Crowdsourcing: Crowdsourcing marketplaces or Internet crowdsourcing systems, such as Amazon Mechanical Turk (AMT), offer monetary rewards for completing specific tasks. As a result, they have become valuable sources of feedback for new products and services. Usually, these tasks are complex for computers to complete sentiment analysis of text, classify website content, create a 3-D photo tour, etc. This reward has begun to attract malicious users to present that behaviour; colluders are concerned with avoiding the effort usually required to produce a genuine review rather than with its quality.

The Sybil attack is a chilling example of how easy it is to exploit the vulnerabilities in OSNs. In 2012, Facebook conducted an experiment to test its network's vulnerability to fake accounts. They created numerous counterfeit accounts that appeared to be independent users and used them to manipulate the network's algorithms, spread misinformation, and influence public opinion. This demonstrated how individuals or organizations can create multiple fake accounts and use them to deceive others or gain an unfair advantage. The attackers used several techniques to exploit these vulnerabilities effectively, highlighting the potential dangers of Sybil attacks in social networks.

Mass Account Creation: The Sybil attackers created many fake accounts, making it difficult for the platform to detect and prevent their activities. This allowed them to have a significant presence on the network and amplify their reach.

Faking Relationships and Amplifying Content: The attackers established connections with legitimate users by sending friend requests, commenting on posts, and conversing. By appearing as active and engaged users, they gained the trust of others on the platform.

The attackers spread false information, rumours, and misleading content through fake accounts. This misleading content could include phoney news articles, doctored images, or deceptive advertisements. By using the network's viral nature to amplify their messages, the attackers were able to influence many users. This highlights the potential impact of misinformation and the need for user vigilance when consuming content on social networks.

In summary, the Sybil attackers exploited the vulnerabilities in Facebook's network and significantly impacted the platform's ecosystem. This example highlights the potential dangers of Sybil attacks in social networks and the importance of implementing robust security measures to prevent such malicious activities.

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Android Vulnerabilities and Anti-Malware Techniques

Android has become the most commonly used OS for the last half of decades, with 87% of Android users. According to researchers, Android Apps APK has become the most significant target of attacks; on average, more than 600,000 malware applications are distributed monthly. According to the AVTest reports, the Android OS has been facing a tremendous increase in attacks yearly. Those attacks vary in the specialization level and target, which creates a pressing need for in-depth analysis of their techniques to develop effective detection and classification tools. To detect and deter new malware definitions, traditional anti-malware, primarily based on signature-based anti-virus (AV), is insufficient as the malware keeps changing the signature pattern of its attack. Such attacks require the use of more sophisticated approaches to detect them and contain them. Signature-based AV uses a fixed set of malware characteristics to identify and categorize malware; however, malware can easily surmount it using obfuscation or encryption.

In 2013, the Obad malware was the first to outsmart dynamic detection by identifying emulators and refraining from executing malicious activities. In two thousand nineteen, the most damaging attacks exploited hardware architecture-based vulnerabilities to read memory content containing sensitive information. Traditional anti-malware, based on signature-based antivirus, cannot keep up with these attacks' constantly changing signature patterns. This underscores the need for more sophisticated approaches to detect and contain these evolving threats.

There are many types of malware tactics and approaches. Among them, one of the most widespread malware known to users is those annoying apps that keep popups and disturb device usage. Some malware takes control of the user device and starts bombarding them with advertisements, changing their default search engine, and more, such as Plankton. Over 93% of malware uses the infected device for the bot, such as Beanbot, which attacks the devices by stealing information, such as IMEI number, phone number, etc., and sending it to a remote server. It can also send high-priced SMS from the device to give remote control to the hacker via this code. The malware hidden in the app prompts users to install an update, and in this disguise, the remote-control program is downloaded and installed on the victim’s machines.

Besides Static and Dynamic anti-malware analysis, Hybrid Analysis tools like WikiLeaks use hybrid techniques that combine static and dynamic analysis. They detect permission requests and collect data using these permissions. They use static analysis to categorize the permissions attained by the app, and then dynamic analysis is used to identify the uses of these permissions for data collection and leaks afterward.

Anti-Analysis Techniques: As malware spreads, several anti-malware techniques are used to detect it. However, malware developers are also evolving and updating their ways of bypassing these techniques. The following are some of the most common anti-analysis techniques malware developers use to evade anti-malware detections.

Repackaging is a prevent malware technique, and more than 85% of malware employs techniques such as DroidDream and DroidKungFu. Also, developers nowadays embed the malicious payload as a source in the form of an app/jar file and then ask the user to install some critical App updates, which will get the user malicious payload from a remote server. Other malware uses polymorphism to change its code every time it gets updated without changing its functionality, such as Opfake. Polymorphism’s main advantage to the malicious code is exploiting the same methods but doing different behaviors via code overriding using inheritance. The interfacing is exploited to override the actual behavior of changing the code to include malicious behavior.

In summary, cyber crimes are spreading daily in the fast-growing digitalization world. New techniques and tools cover the spaces between data security application breaches and platforms. With the right action and approach, breaches can be detected.

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Quick JavaScript Base64 Encoding and Decoding Tips

Today’s online businesses increasingly recognize the pivotal role of sophisticated websites and web application platforms. These platforms, spanning business, education, personal blogs, sports, and health services, are not just a passing trend but a necessity. A sophisticated website attracts more viewers and users and sets you apart in the competitive online marketplace. To keep these platforms running, reliable hosting is essential, with WordPress hosting emerging as a popular choice.

Base64, a binary-to-text encoding scheme, is a critical player in web technology. It converts binary data into a sequence of printable characters, simplifying data transmission over systems that may not support binary data. This widely-used encoding scheme is a cornerstone of web applications and digital systems, including the World Wide Web, where it embeds image files or other binary assets within textual assets like HTML and CSS files. Base64 is often employed to encode binary data (images or files) into a text format, which can be safely transmitted over supporting text protocols like email or HTTP. For instance, when you attach an image to an email, it’s often base64-encoded. Some web applications use Base64 to encode data in URLs, ensuring that special characters are included for tracking or authentication purposes. This is particularly useful when making GET requests with query parameters, providing the server correctly interprets special URL characters.

Base64 encoding is commonly used when binary data needs to be transmitted over media that do not correctly handle binary data. It is designed to hold only textual data from the 7-bit US-ASCII charset. When you send an email containing an image to your friend, your email software encodes the image and inserts the equivalent text into the message like a big chunk of hexadecimal text code. The friend’s email software will base64-decode the encoded textual data to restore the original binary image. Likewise, Base64-encoded malicious scripts on a website involve several steps, such as decoding the Base64 String, starting with observing the decoded content for recognizable patterns, keywords, or suspicious commands and analyzing the script content by looking for the functionality, such as what actions does the script perform? Does it manipulate the DOM, make network requests, or interact with data? Checks for URLs within the scripts. Identifying dynamic values or parameters used. Contextual analysis, like understanding where the scripts are injected. Is it part of HTML, JavaScript, or CSS? Considering the website’s purpose and functionality.

In summary, The best practice for handling user input in web applications is to implement client-side validation to validate user inputs before sending them to the server and validating against expected data types, lengths, and formats to ensure data integrity and reduce unnecessary server requests. Always perform server-side validation to prevent malicious or incorrect data from being processed. Also, sanitizing input to avoid code injection attacks like SQL injection and Cross-Site Scripting to ensure data consistency, validate input against expected patterns, help users understand what went wrong, and guide them toward correct input.

 

GitHub Malicious Compliance for Base64 Benefits

In Beyond the Surface, a timely GitHub malicious repository research paper, it was revealed that repositories are intentionally malicious and publicly uploaded by the repository owner. The researcher studied 47,313 repositories containing malicious files and downloaded GitHub repositories; 4893 were malicious. In some, the attackers were trying to plant malware on users’ machines, while in others, they tried to open backdoors using CobalStrick. This research is particularly relevant in the current cybersecurity landscape.

The researchers have meticulously collected a detailed dataset to analyze all PoCs on GitHub made for specific CVEs by using the GitHub API, which provides keyword-based search capabilities for repositories, code, and commits. They also painstakingly examined the contents, source code, and associated documentation of such repositories to verify whether they contained PoCs for CVE exploits.

In the 21st century, there are many tactics for exploiting vulnerabilities in web applications, OS systems, Network security, etc. Whether a small website or a bigger online platform, all data are connected through programming languages. Cyber attackers exploit up-to-date vulnerabilities to inject malware or attack scripts and shell code. GitHub is not exceptional. They use many repository code templates for programming, website development, etc. The motive of this blog is to write how the researcher analyzes their collected dataset using Base64 analysis.

A table of programming languages used in the repositories shows that Python has emerged as the dominant programming language among hackers and has exploited developers over the past five years.

Base64 analysis: Base64 encoding is another prevalent method for detecting malicious payloads; the researcher extracted base64 values using regular expressions, analyzed them, and decoded hidden scripts to detect any connections with IPs within the encoded payloads. During the analysis, they initially conducted an automated search for base64 payloads using the following regular expression in the PoCs:

([A-Za-z0-9+/]{4}*([A-Za-z0-9+/]{4} |[A-Za-z0-9+/]{3}=|[A-Za-z0-9+/]{2}==))

The Compliance Insider Risk Management Forensics Evidence

Compliance insider risk is a technical process that the user must follow within a specified system. Several approaches and steps make the system perfectly user-friendly for accomplishing their work.

1. Feature capabilities:

i. Visual Capturing allows organizations to capture clips of key security-related user activities, allowing for more secure or compliance visibility and meeting organizational needs.

ii. Configure a recording policy that focuses on the applications and websites that present the most risk by including or excluding desktop applications and/or Windows. This preserves storage space and user privacy. For example, Exclude personal Email and Social Media accounts.

iii. Enhances phishing protection (Preview) allows organizations to capture clips related to enhanced phishing protection in Microsoft Defender Smart Screen. For example, you can capture when users enter the Microsoft Password they used to sign into their Windows OS device on a phishing site or application connecting to a phishing site.

iv. Protected user privacy through multiple levels of approval for activating the capturing feature.

v. Customizable triggers and capturing options mean that security teams can set forensics evidence to meet their needs, whether based on incidents (for example, Capture 5 minutes before and 10 minutes after a user has downloaded Secreatresearch.docx) or on continuous capturing needs.

vi. User-centric policy targets mean that security and compliance teams can focus on user activity, not device activity, for better contextual insights.

vii. Strong role-based access controls (RBAC) mean that the ability to set and review forensics clips is tightly controlled and only available to individuals in organizations with the correct permissions.

viii. Trial capacity (Up to 20 GB) for captured clips, with quick access to capacity utilization and the ability to purchase additional capacity.

2. Capturing Options:

There are two options for capturing information.

Triggering insider risk management policies, including forensics evidence policies. Triggering events are user actions that determine if users are brought into scope for evaluation in insider risk management policies.

1. i.     Specific Activities: This policy option captures activity only when a triggering event has brought an approved user into scope for the forensics evidence policy and when the user detects the conditions for a policy indicator. For example, a user-approved system for forensic evidence capturing is brought into scope to services or portable storage devices. Capture is scoped only for the configured time frame when the user copies the data to the personal cloud storage service or portable storage device. This option's capture will be available for review on the forensic evidence tab on the alerts dashboard.

1. ii.   activities: This policy option captures any user activity. For example, your organization must capture activities for an approved user actively involved in potentially risky activities that may lead to a security incident. Policy incidents may not have reached the threshold for an alert to be generated by the policy, and the potentially risk activity may not be documented. Continuous capturing helps prevent potentially risky activity from being missed or going undetected. Computers for this option will be reviewed on the Forensics evidence tab on the User Activity Reports (Preview) dashboard.

3. Workflow:

i. Users subject to capturing must have explicit capturing requests and approvals: This is an extra process not included in configuring other insider risk management policies. Users assigned to the Insider Risk Management or Insider Risk Management Admin role groups must submit a request to those assigned to the Insider Risk Management approved role group before any user in your organization is eligible for clip-capturing options. For example, This requirement helps support organizational scenarios where your insider risk management admins must replicate approval from your designed legal or human resources personnel before enabling capturing for any user.

ii. Devices must be onboarded and installed with the Microsoft Purview client: Before forensics evidence can collect and store clips captured for eligible users, their devices must be onboarded to the Microsoft Purview compliance portal. Additionally, each device must have the Microsoft Purview Client installed.

These prerequisites enable support for both online and offline device capturing.