As cyber-attacks increase in unencrypted communication environments such as the traditional Internet, protected communication channels based on cryptographic protocols, such as transport layer security (TLS), have been introduced to the Internet. Accordingly, attackers have been carrying out cyber-attacks by hiding themselves in protected communication channels. However, the nature of channels protected by cryptographic protocols makes it difficult to distinguish between normal and malicious network traffic behaviors. This means that traditional anomaly detection models with features from packets extracted a deep packet inspection (DPI) have been neutralized. Recently, studies on anomaly detection using artificial intelligence (AI) and statistical characteristics of traffic have been proposed as an alternative. In this review, we provide a systematic review for AI-based anomaly detection techniques over encrypted traffic. We set several research questions on the review topic and collected research according to eligibility criteria. Through the screening process and quality assessment, 30 research articles were selected with high suitability to be included in the review from the collected literature. We reviewed the selected research in terms of dataset, feature extraction, feature selection, preprocessing, anomaly detection algorithm, and performance indicators. As a result of the literature review, it was confirmed that various techniques used for AI-based anomaly detection over encrypted traffic were used. Some techniques are similar to those used for AI-based anomaly detection over unencrypted traffic, but some technologies are different from those used for unencrypted traffic.

Rapid and tremendous advances in wireless technology, miniaturization, and Internet of things (IoT) technology have brought significant development to vehicular ad hoc networks (VANETs). VANETs and IoT together play a vital role in the current intelligent transport system (ITS). However, a VANET is highly vulnerable to various security attacks due to its highly dynamic, decentralized, open-access medium, and protocol-design-related concerns. Regarding security concerns, a black hole attack (BHA) is one such threat in which the control or data packets are dropped by the malicious vehicle, converting a safe path/link into a compromised one. Dropping data packets has a severe impact on a VANET’s performance and security and may cause road fatalities, accidents, and traffic jams. In this study, a novel solution called detection and prevention of a BHA (DPBHA) is proposed to secure and improve the overall security and performance of the VANETs by detecting BHA at an early stage of the route discovery process. The proposed solution is based on calculating a dynamic threshold value and generating a forged route request (RREQ) packet. The solution is implemented and evaluated in the NS-2 simulator and its performance and efficacy are compared with the benchmark schemes. The results showed that the proposed DPBHA outperformed the benchmark schemes in terms of increasing the packet delivery ratio (PDR) by 3.0%, increasing throughput by 6.15%, reducing the routing overhead by 3.69%, decreasing the end-to-end delay by 6.13%, and achieving a maximum detection rate of 94.66%.

The Internet of Things (IoT) utilizes algorithms to facilitate intelligent applications across cities in the form of smart-urban projects. As the majority of devices in IoT are battery operated, their applications should be facilitated with a low-power communication setup. Such facility is possible through the Low-Power Wide-Area Network (LPWAN), but at a constrained bit rate. For long-range communication over LPWAN, several approaches and protocols are adopted. One such protocol is the Long-Range Wide Area Network (LoRaWAN), which is a media access layer protocol for long-range communication between the devices and the application servers via LPWAN gateways. However, LoRaWAN comes with fewer security features as a much-secured protocol consumes more battery because of the exorbitant computational overheads. The standard protocol fails to support end-to-end security and perfect forward secrecy while being vulnerable to the replay attack that makes LoRaWAN limited in supporting applications where security (especially end-to-end security) is important. Motivated by this, an enhanced LoRaWAN security protocol is proposed, which not only provides the basic functions of connectivity between the application server and the end device, but additionally averts these listed security issues. The proposed protocol is developed with two options, the Default Option (DO) and the Security-Enhanced Option (SEO). The protocol is validated through Burrows–Abadi–Needham (BAN) logic and the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The proposed protocol is also analyzed for overheads through system-based and low-power device-based evaluations. Further, a case study on a smart factory-enabled parking system is considered for its practical application. The results, in terms of network latency with reliability fitting and signaling overheads, show paramount improvements and better performance for the proposed protocol compared with the two handshake options, Pre-Shared Key (PSK) and Elliptic Curve Cryptography (ECC), of Datagram Transport Layer Security (DTLS).

With information systems worldwide being attacked daily, analogies from traditional warfare are apt, and deception tactics have historically proven effective as both a strategy and a technique for Defense. Defensive Deception includes thinking like an attacker and determining the best strategy to counter common attack strategies. Defensive Deception tactics are beneficial at introducing uncertainty for adversaries, increasing their learning costs, and, as a result, lowering the likelihood of successful attacks. In cybersecurity, honeypots and honeytokens and camouflaging and moving target defense commonly employ Defensive Deception tactics. For a variety of purposes, deceptive and anti-deceptive technologies have been created. However, there is a critical need for a broad, comprehensive and quantitative framework that can help us deploy advanced deception technologies. Computational intelligence provides an appropriate set of tools for creating advanced deception frameworks. Computational intelligence comprises two significant families of artificial intelligence technologies: deep learning and machine learning. These strategies can be used in various situations in Defensive Deception technologies. This survey focuses on Defensive Deception tactics deployed using the help of deep learning and machine learning algorithms. Prior work has yielded insights, lessons, and limitations presented in this study. It culminates with a discussion about future directions, which helps address the important gaps in present Defensive Deception research.

Security is the protection from various kinds of threats and most organizations engage in the challenge of security especially cyber-attacks. The attacks are increasing rapidly, due to which cyber security does not now change on supervised and pattern-based detection algorithms which assure continuous security observing. There are many kinds of problems in vendor organizations like cyber theft, which is the most common attack in cyberspace. This research study is developing a Cyber Security Challenges Model (CSCM) that will facilitate vendors’ organizations to identify challenges of cyber security during the development of software in a vendor organization. To find cyber security issues/challenges, a Systematic Literature Review (SLR) is conducted on 44 relevant research publications by developing a search string based on research questions. As the final selected research publications were less in number and did not complete our aim, therefore, snow bowling technique is applied to 67 relevant research publications. This relevant data was comprised of different databases/sources e.g., Google Scholar, IEEE Explore, SpringerLink, ACM Digital Library, anFffid ScienceDirect. Furthermore, for the distinctive literature review, we’ve carried out all of the steps in SLR, for example, improvement of SLR protocol, initials, and a very last collection of the applicable information, data extraction, data quality assessment, and data synthesis. Thirteen (13) critical cyber security challenges are identified which are; “Security issues/Access of Cyberattacks”, “Lack of Right Knowledge”, “Framework”, “Lack of Technical Support”, “Disaster Issues”, “Cost Security issues”, “Lack of Confidentiality and Trust”, “Lack of Management”, “Unauthorized Access issues”, “Lack of Resources”, “Lack of Metrics”, “Administrative Mistakes during Development” and “Lack of Quality, Liability, and Reliability”. The findings of our analysis study signify the similarities and dissimilarities in the recognized cybersecurity challenges in different decades, companies/firms, continents, databases, and methodologies.