The Benefits of Artificial Intelligence in Cybersecurity

Cyberthreats have increased extensively during the last decade. Cybercriminals have become more sophisticated. Current security controls are not enough to defend networks from the number of highly skilled cybercriminals. Cybercriminals have learned how to evade the most sophisticated tools, such as Intrusion Detection and Prevention Systems (IDPS), and botnets are almost invisible to current tools. Fortunately, the application of Artificial Intelligence (AI) may increase the detection rate of IDPS systems, and Machine Learning (ML) techniques are able to mine data to detect botnets’ sources. However, the implementation of AI may bring other risks, and cybersecurity experts need to find a balance between risk and benefits

Machine and deep learning techniques for detecting internet protocol version six attacks: a review

The rapid development of information and communication technologies has increased the demand for internet-facing devices that require publicly accessible internet protocol (IP) addresses, resulting in the depletion of internet protocol version 4 (IPv4) address space. As a result, internet protocol version 6 (IPv6) was designed to address this issue. However, IPv6 is still not widely used because of security concerns. An intrusion detection system (IDS) is one example of a security mechanism used to secure networks. Lately, the use of machine learning (ML) or deep learning (DL) detection models in IDSs is gaining popularity due to their ability to detect threats on IPv6 networks accurately. However, there is an apparent lack of studies that review ML and DL in IDS. Even the existing reviews of ML and DL fail to compare those techniques. Thus, this paper comprehensively elucidates ML and DL techniques and IPv6-based distributed denial of service (DDoS) attacks. Additionally, this paper includes a qualitative comparison with other related works. Moreover, this work also thoroughly reviews the existing ML and DL-based IDSs for detecting IPv6 and IPv4 attacks. Lastly, researchers could use this review as a guide in the future to improve their work on DL and ML-based IDS

Revisión sistemática para la construcción de una arquitectura con tecnologías emergentes IoT, técnicas de inteligencia artificial, monitoreo y almacenamiento de tráfico malicioso

This article presents a systematic review to determine the guidelines that allow the construction of an architecture based on emerging IoT technologies, artificial intelligence techniques, monitoring and storage of malicious traffic, in order to safeguard information, given that there are security flaws in IoT devices, which are intercepted by malicious systems that perform unwanted actions without the consent of the user, causing damage and theft of data, that is why three phases were established to carry out: in the first phase an exhaustive search of information was carried out in specialized databases, where they are selected and classified for the development of the guidelines, in the second phase the information collected was identified and analyzed to define an appropriate algorithm for the study, emerging technologies and key components of the cybersecurity system and finally in the third phase defined the necessary and pertinent guidelines for the struction of an architecture based on emerging technologies

Scalable and Efficient Network Anomaly Detection on Connection Data Streams

Everyday, security experts and analysts must deal with and face the huge increase of cyber security threats that are propagating very fast on the Internet and threatening the security of hundreds of millions of users worldwide. The detection of such threats and attacks is of paramount importance to these experts in order to prevent these threats and mitigate their effects in the future. Thus, the need for security solutions that can prevent, detect, and mitigate such threats is imminent and must be addressed with scalable and efficient solutions. To this end, we propose a scalable framework, called Daedalus, to analyze streams of NIDS (network-based intrusion detection system) logs in near real-time and to extract useful threat security intelligence. The proposed system pre-processes massive amounts of connections stream logs received from different participating organizations and applies an elaborated anomaly detection technique in order to distinguish between normal and abnormal or anomalous network behaviors. As such, Daedalus detects network traffic anomalies by extracting a set of significant pre-defined features from the connection logs and then applying a time series-based technique in order to detect abnormal behavior in near real-time. Moreover, we correlate IP blocks extracted from the logs with some external security signature-based feeds that detect factual malicious activities (e.g., malware families and hashes, ransomware distribution, and command and control centers) in order to validate the proposed approach. Performed experiments demonstrate that Daedalus accurately identifies the malicious activities with an average F_1 score of 92.88\%. We further compare our proposed approach with existing K-Means and deep learning (LSTMs) approaches and demonstrate the accuracy and efficiency of our system