False data injection attacks and the insider threat in smart systems

Gönen, Serkan; Sayan, H. Hüseyin; Yılmaz, Ercan Nurcan; Üstünsoy, Furkan; Karacayılmaz, Gökçe

doi:10.1016/j.cose.2020.101955

Cited by 18 publications

(11 citation statements)

References 15 publications

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“…The role and importance of Artificial Intelligence (AI) and big data technologies for FDIA detection were also highlighted [9,[12][13][14]. The financial impact of FDIAs was demonstrated in [8]. The authors assumed an insider attack and simulated an injection attack by changing the value of the memory location of the Programmable Logic Controller (PLC).…”

Section: Related Workmentioning

confidence: 99%

“…The FDIA is one of the most common attacks and can be launched on any critical infrastructure by penetrating the communication sessions between different devices. FDIAs can damage physical components, induce huge economic losses, and even create life-threatening scenarios [8,9]. Therefore, it is essential to prevent and detect FDIAs in any critical infrastructure.…”

Section: Introductionmentioning

confidence: 99%

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Improving Detection of False Data Injection Attacks Using Machine Learning with Feature Selection and Oversampling

et al. 2021

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Critical infrastructures have recently been integrated with digital controls to support intelligent decision making. Although this integration provides various benefits and improvements, it also exposes the system to new cyberattacks. In particular, the injection of false data and commands into communication is one of the most common and fatal cyberattacks in critical infrastructures. Hence, in this paper, we investigate the effectiveness of machine-learning algorithms in detecting False Data Injection Attacks (FDIAs). In particular, we focus on two of the most widely used critical infrastructures, namely power systems and water treatment plants. This study focuses on tackling two key technical issues: (1) finding the set of best features under a different combination of techniques and (2) resolving the class imbalance problem using oversampling methods. We evaluate the performance of each algorithm in terms of time complexity and detection accuracy to meet the time-critical requirements of critical infrastructures. Moreover, we address the inherent skewed distribution problem and the data imbalance problem commonly found in many critical infrastructure datasets. Our results show that the considered minority oversampling techniques can improve the Area Under Curve (AUC) of GradientBoosting, AdaBoost, and kNN by 10–12%.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving Detection of False Data Injection Attacks Using Machine Learning with Feature Selection and Oversampling

et al. 2021

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“…The threat of a successful FDIA is critical and alarming, with the fast transition of the traditional grid to the smart grid. In recent work, Serkan et al [5] have demonstrated the impact of FDIA through the insider attack model by modifying the memory address of Programmable Logic Controller (PLC). The authors demonstrated that change in PLC could directly affect bill management software and the final bill.…”

Section: Related Workmentioning

confidence: 99%

“…It is a critical and most occurring attack from the set of known attacks to the smart grid. Depending on the type of successful FDIAs, it can cause damage to physical infrastructure or result in economical loss [5] and, importantly, in many cases, it can be life threatening [6]. So, considering the scale of damage detecting and preventing FDIA in the power system is of high importance.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithm for Detection of False Data Injection Attack in Power System

Kumar

Saxena

Choi

2021

2021 International Conference on Information Networking (ICOIN)

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Electric grids are becoming smart due to the integration of Information and Communication Technology (ICT) with the traditional grid. However, it can also attract various kinds of Cyber-attacks to the grid infrastructure. The False Data Injection Attack (FDIA) is one of the lethal and most occurring attacks possible in both the physical and cyber part of the smart grid. This paper proposed an approach by applying machine learning algorithms to detect FDIAs in the power system. Several feature selection techniques are explored to investigate the most suitable features to achieve high accuracy. Various machine learning algorithms are tested to follow the most suitable method for building a detection system against such attacks. Also, the dataset has a skewed distribution between the two classes, and hence data imbalance issue is addressed during the experiments. Moreover, because the response time is critical in a smart grid, each experiment is also evaluated in terms of time complexity.

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“…The present innovative encryption methods like attribute‐based encryption (ABE) are ineffective to solve this problem because of costly computation. The authentication key management, secure storage, data encryption, efficient user revocation, access control, and so on are remains resolved and addressed (Gönen et al, 2020; Uthayakumar et al, 2019). This study analyses the present privacy preserving methods, drawbacks, strengths, investigation problems and refers to a novel paradigm assisted by blockchain technique, which could offset specific drawbacks however guarantees an architecture to provide effective privacy preserving and security in e‐health data.…”

Section: Introductionmentioning

confidence: 99%

Optimal multiple key‐based homomorphic encryption with deep neural networks to secure medical data transmission and diagnosis

Alzubi

Beseiso

et al. 2021

Expert Systems

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Medical database classification problems can be considered as complex optimization problems to assure the diagnosis support precisely. In healthcare, several computer researchers have employed different deep learning (DL) approaches to enhance the classification performance. Besides, encryption is an effective way to offer secure transmission of medical data over public network. With this motivation, this paper presents new privacy‐preserving encryption with DL based medical data transmission and classification (PPEDL‐MDTC) model. The presented model derives multiple key‐based homomorphic encryption (MHE) technique with sailfish optimization (SFO), called MHE‐SFO algorithm‐based encryption process. In addition, the cross‐entropy based artificial butterfly optimization‐based feature selection technique and optimal deep neural network (ODNN) based classification is carried out. In ODNN model, the hyperparameter optimization of the DNN model is carried out utilizing the use of chemical reaction optimization (CRO) algorithm. The proposed method has been simulated utilizing Python 3.6.5 tool, which is tested using activity recognition and sleep stage dataset. A detailed comparative outcomes analysis makes sure the higher efficiency of the PPEDL‐MDTC on the state of art techniques with the detection accuracy of 0.9813 and 0.9650 on the applied activity recognition and University College Dublin Sleep Stage dataset.

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False data injection attacks and the insider threat in smart systems

Cited by 18 publications

References 15 publications

Improving Detection of False Data Injection Attacks Using Machine Learning with Feature Selection and Oversampling

Improving Detection of False Data Injection Attacks Using Machine Learning with Feature Selection and Oversampling

Machine Learning Algorithm for Detection of False Data Injection Attack in Power System

Optimal multiple key‐based homomorphic encryption with deep neural networks to secure medical data transmission and diagnosis

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