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Securing Future Autonomous Applications Using Cyber-Physical Systems and the Internet of Things
Published in Amit Kumar Tyagi, Niladhuri Sreenath, Handbook of Research of Internet of Things and Cyber-Physical Systems, 2022
S. Sobana, S. Krishna Prabha, T. Seerangurayar, S. Sudha
An anomaly-based intrusion detection system (IDS) is used to detect both network and computer intrusions and misuse. Based on heuristics AIDS classifies the intrusion attack as either normal attack or anomalous attack. Thus, AIDS is capable of detecting any type of misuse, but SIDs is capable of detecting intrusions if and only if the signature has previously been created [127]. To detect intrusion AIDS performs two phase of operations such as training phase and testing phase. Though there are several ways to detect anomalies artificial neural networks (ANNs), strict anomaly detection method, data mining (DM) method, artificial immune system and grammar-based methods are widely used in recent autonomous applications [128].
A Review on Application of GANs in Cybersecurity Domain
Published in IETE Technical Review, 2022
Several new and robust intrusion detection techniques are being developed to tackle advanced attacks. These techniques are classified as signature-based and anomaly-based intrusion detection system. The signature-based IDS analyzes the event patterns and detects attacks by comparing them with the previously-stored attack signatures and patterns. The anomaly-based IDS record patterns of various attacks and can detect new variations to these old attacks through several techniques such as machine learning based detection, data-mining based detection, knowledge-based detection, and statistical anomaly based detection. Jain et al. [3] and Kumar and Venugopalan [4] describes current trends in IDS research and these techniques in great detail, including their pros and cons. (Figure 1) classifies and lists various IDS techniques based on detection methods.
An improved bio-inspired based intrusion detection model for a cyberspace
Published in Cogent Engineering, 2021
Samera Uga Otor, Bodunde Odunola Akinyemi, Temitope Adegboye Aladesanmi, Ganiyu Adesola Aderounmu, B. H. Kamagaté
Security measures such as Intrusion Detection System (IDS) and Intrusion Prevention Systems (IPS) among others are examples of cyber-security tools, concepts, and technologies used to minimize these threats. Intrusion Detection and Prevention Systems are systems used to monitor the activities occurring in a computer system or network and scrutinizing them for signs of likely violations or forthcoming threats of violation of computer security policies, acceptable use policies, or standard security practices and attempting to stop detected possible occurrences. These systems primarily focus on identifying possible instances, logging information about them, attempting to stop them, and reporting them to security administrators (Scarfone & Mell, 2012). Intrusion detection systems based on the techniques used to detect attacks (Liao et al., 2013), can be classified as Signature or Misuse-based and Anomaly-based. This paper focuses on Anomaly-based intrusion detection system in cyberspace.
Deep Ensemble Technique for Cyber Attack Detection in Big Data Environment
Published in Cybernetics and Systems, 2022
D. Raghunath Kumar Babu, A. Packialatha
In recent times, Baykara and Das (2018) studied the usage of honey pots on corporate networks. The researchers have proposed a strategy to substantially reduce the “cost of administration, maintenance, and configuration.” Selvakumar et al. (2019) have studied “Adaptive IDS based on Fuzzy Rough Sets for Allen’s interval algebra and attribute selection” on a network trace dataset for choosing an enormous count of attack data for effective attack forecasting in WSNs. Aldwairi, Perera, and Novotny (2018) have demonstrated how and when to use the RBM method to distinguish between anomalous and regular “Net Flow traffic.” Qu et al. (2018) have developed a “Knowledge-Based Intrusion Detection Strategy (KBIDS)” to make up the gap amongst balancing and detection. Furthermore, Hamed, Dara, and Kremer (2018) have adopted a Network Intrusion Detection System (NIDS) that was based on two schemes, namely the Recursive Feature Addition (RFA) and the bigram technique, and the scheme was created, implemented, and assessed as a result. Zhang et al. (2017) have proposed an NIDS in WSNs based on hierarchical trust and dynamic state context that was dependable and suited for continuously varying WSNs classified by perceptual environment changes. Zha and Li (2018) have created a Complex Matching Accelerator (CMA) aided by nonvolatile memory toward enhancing the energy efficiency of ID systems. In addition, Sedjelmaci, Senouci, and Ansari (2017) have introduced a tradeoff among the ID rate and overhead. Kumar, Gupta, and Tripathi (2021a) introduced an anomaly-based intrusion detection system by decentralizing the existing cloud-based security architecture to local fog nodes. Kumar, Tripathi, and P. Gupta (2021b) established a privacy-preservation based IDS for securing information and identifying the malicious assaults in a Software Defined Internet of Things-Fog (SDIoT-Fog) network.