1.1. The Origin of Cybercrimes and the Rise of Modern Botnets
- Approximately 77% of the world is still a relatively safe haven for cybercriminals and their activities; therefore,
- The treaty does not yet bear the force of the law in at least four (8%) of the countries that are signatories to it;
- At least 10 (amounting to 5%) countries of the world that are not members of the Council of Europe have ratified the provisions and specifications of the treaty into law, even though they are not signatories to the treaty; therefore,
- There is still no globally acceptable (by democratic standards of at least a majority of the countries of the world) legal standard through which cybercriminals could be unilaterally prosecuted.
1.2. Typology of Attackers & Botnet Owners
- Hackers/Skilled Individuals: This class of attackers are basically individuals that possess an extensive knowledge and skillset in the art of scripting and coding. They are often loners who understand the internal workings of information and communication technologies, systems, and networks well enough to be able to obfuscate and bypass routine processes and procedures, in order to gain access and privileges within secure environments where they are not authorised. This class of botnet owners tend to use their botnets primarily for financial crimes and identity thefts, and often wield a rather limited amount of resources. They usually cover their tracks very carefully for fear of being found out and prosecuted in line with their jurisdictional cyber laws.
- Hacker Groups: This class of attackers are organised groups of hackers that share a common vision, mission and/or ideology. They possess more resource strength when compared to skilled individual hackers, as they are able to pool skillset, influence and finances to orchestrate coordinated attacks with more organisation, precision and impact. In recent times, we have seen a rise in the numbers and popularity of such hacker groups, such as: Anonymous, Chaos Computer Club and Legion of Doom, amongst many others. Because the composition of these groups typically span across several nationalities and jurisdictions with variations in cybercrime legislation, the individual members often seem insulated from the consequences of the group’s activities due to the technical complexities associated with the trans-jurisdictional policing of cybercrimes.
- Government/Nation-state Actors: Until recently, it was not commonplace to see government and nation-state actors as active players in the activities of cyber offence. Today we have seen several governments come up to claim responsibility for the actions of botnets and similar other malware that have featured in the annals of cyberspace offensives. One popular example was the 2014 Denial of Service attack launched against Sony Pictures by the North Korean Government . Such offensives and attacks are usually of a high-impact nature, owing to the very vast and seemingly limitless resource base that governments and nation-states are usually able to access.
2. Synchronous & Asynchronous Botnet Attacks
Known Botnet Control Architectures
- Direct: This is shown in Figure 3a. In this architecture, botmasters exert direct control over their botnets and the individual bots that compose it. The botmasters are able to directly recruit and interact with the bots and disseminate commands directly, either towards achieving the same (coordinated) or different goals. In this method, only the botmasters are able to know all of the bots in the botnet, because the various bot machines in the botnet typically do not have any form of interaction with each other. Even though this architecture lost popularity as cybercriminal laws became more stringent and penal, because it was possible to trace bots and botnets directly to the botmaster, its popularity is now beginning to rise as more sophisticated machine identity obfuscation techniques are emerging.
- Centralised: In the centralised C&C architecture shown in Figure 3b, all the bots in the botnet rely on connection to a centralised C&C server in order to remain a part of the botnet, receive commands and updates, and also make status and operations reports. This method is gradually ceasing to be the preferred method by botmasters because of the ease of takeout. Once the centralised C&C server is located and taken out, the botnet is dislodged, and its operations can no longer cohere. However, botmasters have devised a way of making this method more sophisticated by distributing and deepening the hierarchy of the centralisation, primarily through layering. This sophistication is similar to what was discussed as the Hierarchical (variant) by Marupally and Paruchuri in .
- Peer-to-Peer (P2P) or Decentralised: In the P2P C&C architecture, each infected host possesses the capability to serve both as a bot and as a C&C server to at least one other computer connected to it. Using the P2P method, the botmaster has no need to maintain forward communication with all of the bots and the botnet, especially at the infiltration level, because the bot code is engineered to be a self-sufficient unit; once released onto the network, the bot code is both able to recruit new bots to join the botnet, and also infuse each bot with the C&C capability, such that only reverse communication (which may then be redirected through several external servers for added layers of anonymity) to the botmaster is necessary to make status and operations reports. It is the lack of a single point of the failure of the botnet modelled in this architecture that makes them more resilient to most modern take down measures [6,39,40]. This architecture is shown in Figure 3c.
- Hybrid: As shown in Figure 3d, in the hybrid approach, the strengths of the Direct, P2P and Centralised methods may be combined to create the most resilient deployment of a botnet C&C server. Most modern botnets that have threatened the Internet in recent years (such as Conficker) have been discovered to feature a C&C mechanism  that exhibits some form of hybridisation, which has made them quite difficult to exterminate. Marupally and Paruchuri discuss the Multi-Server P2P Model , which illustrates the working principles of the hybrid botnet C&C architecture.
3. Lifecycle of Botnets
- Infection/Doping: This is the first stage of the botnet lifecycle. The botmaster releases a carefully engineered and structured bot code into the network. This code then seeks to exploit certain vulnerabilities in software or network configurations that may already be known to the botmaster (following proper reconnaissance). Once machines are located on the network that feature (these) vulnerabilities, they are infused with the bot code; turning them into zombies, whereby control of these machines are remotely ceded to the botmaster. These machines have now been doped.Infection/Doping of vulnerable machines could employ either active procedures (such as scanning, flooding, war driving and injection, or physical trans-loading/infusion, amongst others) or passive procedures (such as drive-by downloads, trans-loading from various removable media, or social engineering, emails, ads, cloned URLs, games, bugged/pirated Software, amongst others) [1,8,32].
- Recruitment & Rallying: This is the second stage of the botnet lifecycle, and one which the botmaster arguably may consider as the most important stage. This is because the strength of a botnet has been discovered to be directly proportional to its bot-army strength . At this stage of the botnet lifecycle, newer targets with similar vulnerabilities are acquired and enumerated as members of the botnet [6,8].Rallying mechanisms used in recent botnets include: Hard coded or generated Domain Name Services (DNS) commands; or hardcoded IP Addresses .
- Synchronisation & Reporting: This is the third stage of the botnet lifecycle. At this stage, the enumerated members of the botnet would be synchronised with the C&C centre, from whence they would henceforth receive commands and directives for action, and also report their status and results of their operations [6,8]. Attackers could decide to either use existing protocols or neoteric protocols for C&C . Following this stage, the bots need to maintain synchronisation with the C&C system at all times in order to receive new commands, infiltration parameters and takeover specifications, which they readily execute. Next, backdoors are installed on the zombies, unused ports are opened up and/or hijacked, such that even after firewalls upgrades and security patch updates, these would still remain difficult to shut off . These guarantee future access to the bot by the C&C server and the botmaster when the need arises.
4. Typology of Existing Botnets
- Spam Botnets: This class of botnets are involved in sending and disseminating large amounts of spamware daily, and seeking to exploit naïve users typically by emails. Popular examples of botnets that belong to the class are the Necrus and Gamut botnets of June 2016 and around early 2013 respectively, which were reported by the McAfee Labs March 2018 Threat Report to comprise a combined 97% of the global spam botnet traffic . Others include Bagle of early 2004, the Storm botnet of early 2007, and the Marina botnet, amongst preponderant others. Xie, et al.  discovered that this class of botnets feature a lot of similarities in bot IP address distribution, email sending patterns and behaviours, email properties and sending time.
- Information Gathering/Reconnaissance Botnets: This class of botnets are used to mine information over the Internet in large quantities on a daily basis. They also feature in the espionage operations of coordinated cybercrime syndicates. A popular example of a botnet that belongs in this class is the Mirai botnet that was discovered in August 2016 to have been scanning the Internet for the IP addresses of vulnerable devices that are part of the Internet of Things (IoT) , and then goes on to infect them to be enlisted as part of the botnet; which was later discovered to have been behind the October 2016 Dyn cyber-attack . Kolias, et al.  and Kambourakis, et al.  present a detailed analysis of the Mirai botnet, covering its internal structure, system of operations, variants of the Mirai botnet and the realities that Mirai and related botnets portend for the future of the IoT. The Satori botnet is a more dreaded variant of the Mirai botnet that was discovered in May 2018 to feature operations similar to its parent form, but was instead focused on mining information pertaining to vulnerable cryptocurrency remote management infrastructures for the purpose of later infiltrating user wallets to steal cryptocurrencies . Another example was the Asprox botnet that hit the Internet around 2008.
- Identity Theft Botnets: This class of botnets are involved in stealing large amounts of private user identity information, such as social security and credit card details, health record information, login usernames and passwords, among other forms of sensitive information, typically for fraudulent purposes. Popular examples of botnets that belong in this class include the Zeus botnet that “compromised over 74,000 FTP accounts on websites of such companies as the Bank of America, NASA, Monster.com, ABC, Oracle, Play.com, Cisco, Amazon, and BusinessWeek”, stealing sensitive banking information through web browser keystroke logging and form grabbing , and also the Bredolab botnet, which was developed in 2009 by 27-year-old Russian Hacker Georgy Avanesov to siphon bank account passwords and other confidential information from infected computers . Others include the Torpig botnet of since 2005, the Alureon botnet of around 2010, and the Mariposa botnet of December 2008, amongst others.
- Click-Fraud Botnets: This class of botnets attempt to mimic legitimate human click-ad behaviour in a bid to con Internet advertisers into believing that their online adverts have been engaged with by a legitimate, actual human audience, thereby accumulating financial revenue for the botherders as the botnet click-advertising web traffic continues to mount. One popular example of a botnet belonging to this class is the Chameleon botnet of February 2013, which was reported to have amassed a monthly revenue of over $6 million USD for the botnet owners following an infection of over 120,000 Windows® machines .
- Crypto Botnets: This class of botnets are used by criminals in mining crypto currencies and resources for financial gain. Examples include the high-profile Smominru and ADB.Miner cryptomining botnets.
5. Botnet Countermeasures
- Prevention [P]: This goal is aimed at increasing the chances/possibility of averting the occurrence of a botnet attack in a network. Techniques that seek to achieve this goal are most often implemented around the edge (entry and exit) points of the network environment/infrastructure, so as to make sure that traffic and data coming into the network are legitimate and non-malicious. Examples of such proposed techniques include that proposed by Luo et al. , amongst others.
- Detection [D]: The goal here is to spot the presence of a botnet within a production network. Techniques that are tailored towards this goal typically focus on analysing packets, traffic and communications that take place within the network, so as to identify those of malicious compositions, operations and intent, and to alert the administrators accordingly. These techniques may sometimes do little to prevent the botnets from attacking the network, but they basically aim to identify the presence of botnets in the network. However, in some deployment cases, these techniques are used alongside another technique(s) that focuses on one of the other goals, so as to make it more potent and sophisticated. For example, Böck et al.  propose a novel approach to detecting botnets that are fully-distributed and asynchronous in their operations, using a novel mechanism known as Trust Based Botnet Monitoring Countermeasure (TrustBotMC), and then proposing a follow-up mitigation strategy. Khattak et al.  offer further insight into the dimensions for botnet detection that have been proposed in literature.The task of detecting botnets in actual implementations is often made further difficult by a phenomenon known as flash crowds. Flash Crowds occur when a large crowd of legitimate users repeatedly try to gain access to a server resource or service at the same time, often around a time that can be considered as the peak period(s) of such service(s) (known as a flash event), and may wrongly be flagged off as a persistent threat/attack situation. Indeed, Flash Crowds can also cause DoS to occur, and in fact go a long way to further complicate the task of detecting and controlling DoS attacks. This is so because flash crowd traffic and DoS attack traffic have certain characteristics in common, and distinguishing them under the rush and load of DoS traffic can be a really difficult task. Peng et al.  and Alsaleem et al.  proposed a rule-based mechanism by which HTTP denial of service (DoS) attacks could be detected and isolated during flash events, while in the same vein, Saad et al.  proposed a rule-based technique for the detection of anomalous ICMPv6 behaviours; all for the purpose of reducing the rates of false positives and negatives in threat situations. Also, Jazi et al.  proposed a technique for detecting HTTP-based DoS attacks at the application layers of web servers using sampling techniques, while Behal et al.  reviews existing strategies and methods for characterising and isolating Distributed Denial of Service (DDoS) attacks, even in the midst of flash events. Lonea  proposed a quantitative method for detecting DDoS attacks in cloud environments by analysing intrusion detection system alerts, while D’Cruze  proposed an efficient and flexible Software-Defined Networking (SDN) solution to mitigate DDoS attacks on Internet Service Provider (ISP) networks.
- Offensive [O]: Here, the goal is to launch a form of counter-attack against the botnet/intrusion element, with the ultimate motive of taking it down (where possible), or forcing the individual bots to go against the commands they are receiving from the C&C server; effectively obfuscating and dislodging the botnet. One way by which this is often achieved is through sinkholing. Usually, countermeasures that are built towards this goal are designed to take advantage of freshly-discovered/already-known vulnerabilities in the botnet design architecture, by engaging with active research findings and discoveries that relate to the botnet under investigation; hence, offensive countermeasures are often not generic, but specific to certain botnet types/examples. Offensives could be direct (when they engage with the botnet directly, and are targeted towards dislodging/incapacitating specific botnet components or the botnet itself) or indirect (when they are they just targeted towards obscuring or redirecting particular botnet components, often through surrogate points in the network) .
- Reconnaissance [R]: Though considered to be one of the most passive of countermeasure goals, this goal is actually what should be the foundation of any countermeasure that seeks to effectively take down any modern engineered botnet. The goal here is to passively monitor a known botnet that has been detected on a network, and gather as much information as possible relating to its mode of operations, bot members/strength, C&C architecture, malicious capabilities and obfuscation techniques, amongst others. Most offensive countermeasures that actually produce any result in real botnet scenarios rely largely on detailed and extensive ab initio/pre-engagement reconnaissance.
- Mitigation [M]: This goal is aimed at controlling and curtailing the extent of the damage to the network and hosts, whose environment has already been breached by a rampaging botnet; it is concerned with damage control. Countermeasures focused towards this goal typically involve disinfecting bots in real-time, stopping compromised services, reinforcing firewall defences, closing up unused ports on hosts, amongst others.
5.1. Categories & Limitations of Existing Botnet Countermeasures
5.1.3. Exploit/Take Down
6. Botnets in Mobile and Cloud Environments
7. The Botnets of the Future
9. Future Research Directions
Conflicts of Interest
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|1||Setup||Easiest||Easy||Fairly Difficult||Difficult (difficulty increases with hybridisation)|
|4||Ease of takeout||Easiest||Easy||Moderately Difficult||Difficult|
|5||Ease and Accuracy of Traceback||Easiest||Easy||Difficult||Very Difficult|
|6||Command Dissemination Latency (the time it would take a command issued by the botmaster to travel through to the very last bot in the botnet)||Instant||Fast||Moderately Slow||Slow (speed of dissemination decreases further with depth and level of hybridisation)|
|7||Possibility of Botnet Failure||Instant||Easier||Easy||Difficult|
|8||Botnet Enumeration||Near Impossible||Easier||Easy||Difficult|
|9||Botnet Franchisement||Difficult||Easiest (and more structured)||Easier||Easy|
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