In this paper, a combination of channel, receiver frequency-dependent IQ imbalance, and carrier frequency offset estimation under short cyclic prefix length is considered in orthogonal frequency division multiplexing system. An adaptive algorithm based on the set-membership filtering algorithm is used to compensate for these impairments. In short CP length, per-tone equalization structure is used to avoid inter-symbol interference. Due to CFO impairment and IQ imbalance in the receiver, we will expand the PTEQ structure to a two-branch structure. This structure has high computational complexity, so using the set-membership filtering idea with variable step size while reducing the average computation of the system can also increase the convergence speed of the estimates. On the other hand, applying wavelet transform on each branch of this structure before applying adaptive filters will increase the estimation speed. The proposed algorithm will be named SMF-WP-NLMS-PTEQ. The results of the simulations show better performance than the usual adaptive algorithms. Besides, estimation and compensation of channel effects, receiver IQ imbalance and carrier frequency offset under short CP can be easily accomplished by this algorithm.
The emergence of industrial Cyberinfrastructures, the development of information communication technology in industrial fields, and the remote accessibility of automated Industrial Control Systems (ICS) lead to various cyberattacks on industrial networks and Supervisory Control and Data Acquisition (SCADA) networks. The development of ICS industry-specific cybersecurity mechanisms can reduce the vulnerability of systems to fire, explosion, human accidents, environmental damage, and financial loss. Given that vulnerabilities are the points of penetration into industrial systems, and using these weaknesses, threats are organized, and intrusion into industrial systems occurs. Thus, it is essential to continuously improve the security of the networks of industrial control facilities. Traditional intrusion detection systems have been shown to be sluggish and prone to false positives. As a result, these algorithms' performance and speed must be improved. This paper proposes a novel Honeypot enhanced industrial Early Intrusion Detection System (EIDS) incorporated with Machine Learning (ML) algorithms. The proposed scheme collects data from all sensors of Honeypot and industrial devices from the industrial control network, stores it in the database of EIDS, analyses it using expert ML algorithms. The designed system for early intrusion detection can protect industrial systems against vulnerabilities by alerting the shortest possible time using online data mining in the EIDS database. The results show that the proposed EIDS detects anomalous behavior of the data with a high detection rate, low false positives, and better classification accuracy.
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