14 Ways To Evade Botnet Malware Attacks On Your Computers
Usually, bots are used in large numbers to create a botnet, which is a network of bots used to launch broad remotely-controlled floods of attacks, such as DDoS attacks. Botnets can become quite expansive. For example, the Mirai IoT botnet ranged from 800,000 to 2.5M computers.
14 Ways to Evade Botnet Malware Attacks On Your Computers
Unfortunately, this has spawned an exponentially increasing number of malicious attempts to take advantage of smartphone vulnerabilities. From adware, Trojans, spyware, worms, and ransomware, malware can find its way onto your phone in a number of ways. Clicking on a dodgy link or downloading an unreliable app are some obvious culprits, but you can also get infected through emails, texts, and even your Bluetooth connection. Moreover, malware such as worms can spread from one infected phone to another without any interaction from the user.
A hacked microphone and camera can record everything you see and say. A hacked GPS can broadcast your every move. Even worse, mobile malware can be used to evade the multi-factor authentication (MFA) many apps use to keep our data secure.
The defense strategies against malware differ according to the type of malware but most can be thwarted by installing antivirus software, firewalls, applying regular patches to reduce zero-day attacks, securing networks from intrusion, having regular backups and isolating infected systems. Malware is now being designed to evade antivirus software detection algorithms.
Since the rise of widespread broadband Internet access, malicious software has more frequently been designed for profit. Since 2003, the majority of widespread viruses and worms have been designed to take control of users' computers for illicit purposes. Infected "zombie computers" can be used to send email spam, to host contraband data such as child pornography, or to engage in distributed denial-of-service attacks as a form of extortion. Malware is used broadly against government or corporate websites to gather sensitive information, or to disrupt their operation in general. Further, malware can be used against individuals to gain information such as personal identification numbers or details, bank or credit card numbers, and passwords.
The most common anti-detection mechanism is to encrypt the malware payload so that antivirus software does not recognize the signature. More advanced malware is capable of changing its form into variants so they the signatures differ enough to make detection unlikely. Other common techniques used to evade detection include from common to uncommon: (1) evasion of analysis and detection by fingerprinting the environment when executed; (2) confusing automated tools' detection methods. This allows malware to avoid detection by technologies such as signature-based antivirus software by changing the server used by the malware; (3) timing-based evasion. This is when malware runs at certain times or following certain actions taken by the user, so it executes during certain vulnerable periods, such as during the boot process, while remaining dormant the rest of the time; (4) obfuscating internal data so that automated tools do not detect the malware; (v) information hiding techniques, namely stegomalware; and (5) fileless malware which runs within memory instead of using files and utilizes existing system tools to carry out malicious acts. This reduces the amount of forensic artifacts available to analyze. Recently these types of attacks have become more frequent with a 432% increase in 2017 and makeup 35% of the attacks in 2018. Such attacks are not easy to perform but are becoming more prevalent with the help of exploit-kits.
With the growth of Internet of Things (IoT), more devices than ever are joining the internet, increasing the attack vector possibilities. Even the seemingly harmless wireless CCTV cameras that watch your porch or backyard can be compromised to open an entry point for botnet malware to enter the network. The fact that such new IoT devices can come with poorly configured security settings only worsens the problem.
Prevention of botnet attacks requires good techniques to detect them ahead of time. Using advanced analytics to monitor and manage traffic flows, user access, and data leaks is another measure you can take. The Mirai botnet was one such instance where the attackers exploited insecure connected devices by turning them into zombie computers.
Sometimes, even your best prevention measures can be overcome by botnet attacks. It becomes too late by the time you detect them in your network, and as a result, the functionality of your network is compromised. In such scenarios, your best bet is to mitigate the impact of such attacks. This means reducing the damage that will be caused.
Hackers use botnets to attack large numbers of computers at once. A botnet is a network of compromised computers that are controlled remotely by a single attacker. These machines are infected with malware, such as viruses, worms, Trojans, spyware, adware, and rootkits.
Most bots are created to send spam emails, but many others are used to steal personal information, launch denial-of-service attacks, or distribute malware. Some botnets are built around zombie PCs, which are already infected with malware.
A denial of service (DoS) attack is a type of malicious activity that disrupts or prevents access to a website by flooding it with too many requests. A botnet is a network of computers controlled by hackers that are used to perform these attacks. A botnet may consist of thousands of computers spread around the world, but the goal of the hacker behind the operation is to control the computers and use them to launch DoS attacks against another computer system.
The Trickbot banking Trojan is now targeting U.S. banks in new spam campaigns fueled by the prolific Necurs botnet. The malware has grown more potent with the introduction of a customized redirection method as part of its attacks.
Recently, you most likely watched widespread news coverage of a new cyber attack called WannaCry. It infected over 200,000 computers worldwide and locked numerous organizations out of their data, including hospitals in the United Kingdom. There are several reasons this attack gained so much attention. First, it spread rapidly from computer to computer by attacking a known weakness in Windows computers. Second, the attack was a type of malware called Ransomware, which meant that once it infected your computer it encrypted all your files, locking you out of your data. The only way you could recover your data was from backups or by paying the attacker a $300 ransom to decrypt all of your data. The third and most important reason this attack gained so much attention was because it never should have happened. The weakness that WannaCry attacked in Windows computers was well known by Microsoft, which had released a fix months earlier. But many organizations failed to install the fix, or were still using operating systems that are no longer supported by Microsoft.
Once the botnet is in place, the hacker can command all the malware-infected zombie computers to perform certain functions simultaneously, such as sending out millions of spam emails, secretly mining crypto (cryptojacking) or coordinating a significant DDoS attack.
Today, forensics experts and anti-malware solutions face a multitude of challenges when attempting to extract information from malicious files; dynamic analysis (sandboxing) is a popular method of identifying behavior associated with running or opening a given file, and provides the ability to examine the actions which that file is responsible for. Dynamic analysis technology is gaining popularity for use in detecting targeted threats and zero-day attacks, because this approach need not rely on detecting the malicious code. Instead, it can leverage the ability to identify generic "suspicious behaviors" to assess the risk inherent in running a given sample, and provide intelligence about the protocols and infrastructure attackers can use to control malicious samples. Of course, many of the attackers have a vested interest in making it much more difficult to extract intelligence from their backdoors or implants. New techniques to evade or complicate analysis of samples are growing in popularity and diversity. With malware authors constantly evolving new techniques to hamper automated analysis, what is a researcher to do? In the first part of our presentation, Christopher Kruegel, Co-Founder and Chief Scientist at Lastline, will talk about designing dynamic analysis systems, how one might go about building such a system, and what information one should seek to extract with a dynamic analysis platform. He will explain the advantages and limitations of externally instrumented full-system emulation, and demonstrate its value in comparison with other approaches such as OS emulation or traditional virtualization solutions which instrument from inside the analysis environment. In the second part, Christopher will discuss and provide recent examples of several classes of evasion techniques observed in the wild, including environment triggers, stalling code, and detection of human interaction, and demonstrate the evolution of techniques over time. In the third part, he will present a number of solutions to these challenges, each enabled by full system emulation. He will discuss how to extend a sandbox to detect environment-dependent branching, identifying or circumventing environment detection attempts, and forcing execution along each possible path, covering as much of the executable code as possible. Christopher will also present approaches to identify and mitigate stalling code blocks, dramatically reducing the overhead of analysis when this approach is sufficient, or forcing the execution to exit the costly blocks when it is not. The session will also cover methods for identifying attempts to detect human behaviors, and recipes for bypassing these detection attempts.