Cyber crime refers to a broad range of criminal offences related to computer systems that lead to loss or compromise of intellectual property and vital data.Cyberattacks have been the fastest growing crimes and statistics depict that close to $6trillion shillings worth of investment is lost to cybercriminals. Unprecedented attacks have been a major hindrance to investment and innovation on a global scale. Prediction of the cyber-attacks is a phenomenon being adopted in addition to prevention and is attributed to the sophisticated nature of the crimes and associated impacts. The essay intends to evaluate the prediction models adopted and the effectiveness in countering cybercrimes.
There are various types of cybercrimes and their understanding is critical in the prediction of likelihood of occurrence and eventual adversity. The cybercrimes include data espionage, data and system interference (Vacca, 2017).Identity theft, phishing, cyber laundering are among other complex methods adopted by malicious individuals to compromise computer systems and their functionality.
Data espionage refers to the illegal access of information through the internet thus leading to the loss of sensitive data such as company data, trade secrets and classified information. The perpetrators use different techniques to circumvent the deployed prevention measures. These include the human aided intrusion to the sophisticated phishing attacks targeting the individual users, who are the weakest link in data protection.
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Data and system interference is another phenomenon affecting the private users and businesses that depend on the integrity of data. Limited access to data results in financial damage or compromise of data. Virus attacks are common and have become sophisticated due to different methods of distribution in the deployed computer systems. Web based scams such as computer worms and denial of service attacks have become prevalent (Vacca, 2017).
Computer worms are malware that are self-replicating programs with the potential to initiate multiple data transfers leading to huge losses. They are also, able to impact the functionality of system resources and crush websites due to network traffic overload.DDoS attacks are characterized by overload of servers with invalid requests or queries overwhelming the system resources. This is the oldest form of attack leading to crushing of competitive websites and programs.
Identity theft is another cyber threat characterized by the fraudulent acquisition of resources using another person’s identity. This is carried out using internet infrastructure and technical support. Globalization of network based services has led to the use of identity confirmation systems such as payment cards and other ecommerce integrated processes. The targeted data include social security, phone numbers, passwords, and passport numbers among other vital data.
Phishing involves techniques used to acquire personal data utilizing spyware and other implementations leading to false obtaining of personal or secret information. There are different types of phishing including email phishing, spear phishing, angler phishing among others. User behavior is the greatest motivation of phishing attacks that can lead to unprecedented loss of personal and organization information (Vacca, 2017).
Effective cyber threats forecasting is critical in the mitigation of the adverse effects. Different methods involving big data and other disruptive technologies are used in predictive modelling.Deep packet inspection is one of the effective predictive tools. The technology can be integrated in the architecture of the software or other critical integrations. It is able to facilitate network filtration and issue alerts in the event that there is non-compliance to set protocols, viruses, malware (Hill et al, 2018).The organization is able to get real time data on inconsistencies for example lateral movements that signify impending attacks.
Deep packet inspection leverages on the ability to evaluate the contents of a packet based on a set of guidelines provided by the internet service provider. It can gather data on the origin and thereafter redirect the network traffic. It serves several purposes such as the forecasting and detection of intrusion. The various techniques deployed by the technology include pattern or signature matching, IPS solutions and protocol anomaly that focus on the identification of source of threat thus providing room for mitigating expected attacks.
A real situation of abated cyber-attack is where malware is directed towards the database, web applications or remote access functionalities by employing hacking tools. The DPI software is able to detect traffic utilizing established protocols. Lack of visibility in the different domains is the reason for close to 30% of attacks attributed to lateral movements in IT infrastrsucture.This is according to a report by SANS institute that depicted that 68% of respondents were exposed to malware attacks without prior knowledge on the breaches (Xing et al, 2019).
Cybercrime predictive analysis can also, be done through extracting intelligence from social media data. Advanced methods such as the natural language processing and machine learning methodologies are used to connect individual opinions to the real time cyber-attack events (Hill et al, 2018). This is because of the realization that most of the security attacks are initiated by hackers in response to social events. Tracking the data provides critical insights on the tools used and motivations.
Distinct topics on the social media platforms highlight polarized opinions obtained from corporations, governments and individual users on daily life activities. The valuable information is depicted as a social sentiment that might be used to predict likelihood of cyber-attacks. It entails analysis of human mobility, spam and bot detection as well as disaster reaction. Large data collected over time provides critical insights on the social motivations behind cyber-attacks (Hernandez et al, 2018). The natural language processing has depicted that there are negative aspects associated with information security attacks by hacktivists.Classification of data becomes vital in segmenting attacks considering the involved actors sentiments and consequent probability of attacks.
Data is obtained from the querying of social networking engines to ascertain the actors and the chronology of events. Social media is now regarded as a platform for the spread of malware because of different avenues that can be exploited and thus the continuing opportunity for exploitation. The FBI report documented 58 reports between 2015 and 2017 highlighting how social media had become an effective platform for cyber-attacks. The increase in fake accounts, understanding the motivation of attacks and the skill levels is critical to predict the impending threats (Hernandez et al, 2018).
Deployment of big data analytics is another critical aspect in predicting cyber-crimes. The analysis tool needs to be in a position to make predictions on patterns of crime. The adoption of hotspot groups is desirable in the modelling of a predictive algorithm. This provides insight on the resource usage and statistics on the location of the cyber criminals. Tools that are of the essence include Quick tool, R tool, Orange, qradar among others (Abdullah et al, 2019).
Data mining becomes a vital resource in the evaluation of the structured and semi structured data. The criminal data obtained is manipulated using the quick tool that focuses on k clustering of the data for the different groups. The clusters are then used to ascertain the likelihood of crime. Crime mapping especially on a geographical context utilizes structured data that is geographically distributed and algorithms can be used in pattern prediction as well as providing a perspective for mitigation.
MIT researchers have been able to tie the machine learning aspects to predictive modelling.The created machine learning infrastructure is able to detect close to 85% of impending attacks by evaluating web scale platform that is capable of creating millions of logs. The implementation is able to first detect suspicious events which are then availed to a human interface for confirmation of attacks. Feedback then informs the virtual analyst model. The ability of the implementation to generate more models impacts on the effectiveness of the detection and leverages on the unsupervised machine learning model (Ahmet et al, 2018).
Attack graphs area also, deployed in the cyber threat prediction to ascertain the likelihood of a hacker exploiting the vulnerabilities that exist in a computer system. This then provides sufficient information to the developers on the need to initiate defensive mechanisms. It is one of the most used security metrics by organizations adopted to assess how the vulnerabilities are interlinked and leveraged to impact on the entire system. Stochastic models of evaluation are desirable in the analysis of attack graphs deployment in a multi-platform arrangement.
Attack graphs evaluations using probabilistic analysis is of the essence in measuring the security of network. The model provides a perspective of security aspects that need to be initiated to deal with impending attacks. Structural metrics, time-based metrics and probability metrics provide an aggregate situation of the network. Focus on dynamic attributes is essential. The two main tasks mainly handled by the attack graphs include the prediction of malefactor actions, evaluation and detection of past actions as well as linking to the present state of the systems (Xing et al, 2019).
An example is a scenario where an attacker intends to hack a website that has multiple validation vulnerabilities.SQL injection attack by a malicious individual with the intention to gain access to information contained in a database can be detected. The SQL injection is in most cases multi staged and the capabilities of attack graphs is critical to address the safety situation. This is because the attacks have specific goals. The topology of the network with defined nodes grows at a high rate and the attack is initiated by known vulnerabilities thus intrusion can be mitigated.
The dynamic bayesian network (DBN) is a complementary statistical model that can be adopted to predict the likelihood of a cyber-attack. It works within the probability framework. The detect functionality provides mechanism for alert system, threat classification and signalizing of threats among other aspects. The strength of a system is attributed to being able to autonomously assess the security level and provide a mechanism of threats (Xing et al, 2019).
Both human and cultural aspects can be manipulated in the bayesian network model to develop the cyber security risk model. The risk assessment taxonomy considers several aspects. Data accessibility is the main risk assessment level. Identification of individual steps in the pathway to be modelled is critical.Assesment targets such as accepting connection requests to the existing database while minimizing risks is desirable. Evaluation of risk parameters such as ports, attack skill, connection, defense, user permission among others become vital in the statistical analysis.
Recommendation systems like those deployed in movies are also, vital in the evaluation of the cyber threats. They leverage on the behavior of the various harmful sources and internet protocols that are the unique numerical labels provided to the devices connected in a network (Pirc et al, 2016). They have the potential to mitigate against large scale and coordinated attacks on multiple fronts. This includes the provision of the chronology of events, prediction and the clustering process. However, behavioral considerations of the cyber attackers needs to be taken into consideration.
AI will play a significant role in cyber-crime predictive analysis. This is attributed to the deployment of sophisticated tools and equipment that can detect malicious events before they happen. This is through the deployment of less human intensive platforms to aid in monitoring and cost reduction of cyber security implementations. Real time threat tagging is an innovative concept that is derived from the ability of the system to provide historical cyber threat activity. This makes it possible to ascertain adverse events, flag them and provide logs for the various integrated devices.
Proactive mitigation is another critical aspect focusing on the automated interventions that are deployed before risk exposure. Ensuring that systems are more intelligent with decreased manipulation is desirable. Organizations need to confirm that system user behavior is in compliance with the expected security needs. This is of the essence because non-compliance leads to exposure to the cyber threats and trained and technically able individuals are responsive to the threats and can initiate corrective mechanisms to prevent losses (Pirc et al, 2016).
In conclusion, the evolution of cyber threats has resulted in concern on the effectiveness of antiviruses and other preventive tools. There are numerous cyber threats attributed to hacking, phishing, DDoS, identity theft among other adverse methods used by malicious individuals. The deployment of predictive analysis tools has become an emerging trend in most organizations over the years. These include deep packet inspection software, big data analytics, data mining techniques, attack graphs among other innovative solutions. There is however, need to evaluate hacker behavior and deploy suitable tools based on organization risk exposure.
References
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- Ahmet Okutan, Gordon Werner, Shanchieh Jay Yang, & Katie McConky. (January 01, 2018). Forecasting cyber-attacks with incomplete, imbalanced, and insignificant data. Cybersecurity, 1, 1, 1-16.
- Hernandez-Suarez, A., Sanchez-Perez, G., Toscano-Medina, K., Martinez-Hernandez, V., Perez-Meana, H., Olivares-Mercado, J., & Sanchez, V. (April 29, 2018). Social Sentiment Sensor in Twitter for Predicting Cyber-Attacks Using ℓ1 Regularization. Sensors, 18, 5, 1380.
- Xing Fang, Maochao Xu, Shouhuai Xu, & Peng Zhao. (January 01, 2019). A deep learning framework for predicting cyber-attacks rates. Eurasip Journal on Information Security, 2019, 1, 1-11.
- Abdullah, F. M., & Abdullah, F. M. (January 01, 2019). Using big data analytics to predict and reduce cyber-crimes. International Journal of Mechanical Engineering and Technology, 10, 1, 1540-1546.
- Pirc, J., DeSanto, D., Davison, I., & Gragido, W. (2016). Threat forecasting: Leveraging big data for predictive analysis. Cambridge, MA: Syngress.
- Vacca, J. R. (2017). Computer and information security handbook. Cambridge, MA: Morgan Kaufmann Publishers.
