Journal articles: 'Malicious insider'

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Nguyen, Minh-Duong, Ngoc-Tu Chau, Seungwook Jung, and Souhwan Jung. "A Demonstration of Malicious Insider Attacks inside Cloud IaaS Vendor." International Journal of Information and Education Technology 4, no. 6 (2014): 483–86. http://dx.doi.org/10.7763/ijiet.2014.v4.455.

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Jones, Andy. "Catching the malicious insider." Information Security Technical Report 13, no. 4 (2008): 220–24. http://dx.doi.org/10.1016/j.istr.2008.10.008.

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Kim, Park, Kim, Cho, and Kang. "Insider Threat Detection Based on User Behavior Modeling and Anomaly Detection Algorithms." Applied Sciences 9, no. 19 (2019): 4018. http://dx.doi.org/10.3390/app9194018.

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Insider threats are malicious activities by authorized users, such as theft of intellectual property or security information, fraud, and sabotage. Although the number of insider threats is much lower than external network attacks, insider threats can cause extensive damage. As insiders are very familiar with an organization’s system, it is very difficult to detect their malicious behavior. Traditional insider-threat detection methods focus on rule-based approaches built by domain experts, but they are neither flexible nor robust. In this paper, we propose insider-threat detection methods based on user behavior modeling and anomaly detection algorithms. Based on user log data, we constructed three types of datasets: user’s daily activity summary, e-mail contents topic distribution, and user’s weekly e-mail communication history. Then, we applied four anomaly detection algorithms and their combinations to detect malicious activities. Experimental results indicate that the proposed framework can work well for imbalanced datasets in which there are only a few insider threats and where no domain experts’ knowledge is provided.

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Glancy, Fletcher, David P. Biros, Nan Liang, and Andy Luse. "Classification of malicious insiders and the association of the forms of attacks." Journal of Criminal Psychology 10, no. 3 (2020): 233–47. http://dx.doi.org/10.1108/jcp-03-2020-0012.

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Purpose The authors argue that the current studies about malicious insiders confuse the fact that malicious attacks belong to two different categories, namely, those that launch instrumental attacks and expressive attacks. The authors collect malicious insider data from publicly available sources and use text-mining techniques to analyze the association between malicious insiders’ characteristics and the different types of attack. Design/methodology/approach The authors investigated the relationship between personality characteristics and different types of malicious attacks. For the personality characteristics, the authors use the same method as Liang et al. (2016), which extracted these characteristics based on a keyword-characteristic dictionary. For different types of malicious attacks, two raters rated each case based on criteria modified from criminology research to determine the degree of expressiveness and instrumentality. Findings The results show that malicious insiders who are manipulative or seeking personal gain tend to carry out instrumental attacks. Malicious insiders who are arrogant tend to conduct expressive attacks. Research limitations/implications This study uses third party articles to identify the personality characteristics of known malicious insiders. As such, not all personality characteristics may have been reported. Data availability was an issue. Practical implications Understanding if different personality characteristics lead different types of attacks can help managers identify employees who exhibit them and mitigate an attack before it occurs. Social implications Malicious insider attacks can have devastating results on businesses and employees. Help to identify potential malicious insiders before they act, may prevent undue harm. Originality/value This study used 132 cases of none malicious insiders to examine their attack objectives. No other study that the authors know of used that many cases.

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Clarke, Karla, Yair Levy, Laurie Dringus, and Shonda Brown. "How workplace satisfaction affects insider threat detection as a vital variable for the mitigation of malicious cyber insiders." Online Journal of Applied Knowledge Management 7, no. 1 (2019): 40–52. http://dx.doi.org/10.36965/ojakm.2019.7(1)40-52.

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Insider threat mitigation is a growing challenge within organizations. The development of a novel alert visualization dashboard for the identification of potentially malicious cyber insider threats was identified as necessary to alleviate this challenge. This research developed a cyber insider threat dashboard visualization prototype for detecting potentially malicious cyber insider activities QUICK.v™. This study utilized Subject Matter Experts (SMEs) by applying the Delphi Method to identify the most critical cyber visualization variables and ranking. This paper contains the detailed results of a survey based experimental research study that identified the critical cybersecurity variables also referred to as cybersecurity vital signs. The identified vital signs will aid cybersecurity analysts with triage for potentially malicious insider threats. From a total of 45 analytic variables assessed by 42 cybersecurity SMEs, the top six variables were identified using a comprehensive data collection process. The results indicated that workplace satisfaction is one of the top critical cyber visualization variables that should be measured and visualized to aid cybersecurity analysts in the detection of potentially malicious cyber insider threat activities. The process of the data collection to identify and rank critical cyber visualization variables are described.

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Kim, Jung Hwan, Chul Min Kim, and Man-Sung Yim. "An Investigation of Insider Threat Mitigation Based on EEG Signal Classification." Sensors 20, no. 21 (2020): 6365. http://dx.doi.org/10.3390/s20216365.

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This study proposes a scheme to identify insider threats in nuclear facilities through the detection of malicious intentions of potential insiders using subject-wise classification. Based on electroencephalography (EEG) signals, a classification model was developed to identify whether a subject has a malicious intention under scenarios of being forced to become an insider threat. The model also distinguishes insider threat scenarios from everyday conflict scenarios. To support model development, 21-channel EEG signals were measured on 25 healthy subjects, and sets of features were extracted from the time, time–frequency, frequency and nonlinear domains. To select the best use of the available features, automatic selection was performed by random-forest-based algorithms. The k-nearest neighbor, support vector machine with radial kernel, naïve Bayes, and multilayer perceptron algorithms were applied for the classification. By using EEG signals obtained while contemplating becoming an insider threat, the subject-wise model identified malicious intentions with 78.57% accuracy. The model also distinguished insider threat scenarios from everyday conflict scenarios with 93.47% accuracy. These findings could be utilized to support the development of insider threat mitigation systems along with existing trustworthiness assessments in the nuclear industry.

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Adikari, Swini. "Countering the Threat of Nuclear Terrorism Arising from Malicious Insiders." Global Affairs Review 1, no. 2 (2021): 1–18. http://dx.doi.org/10.51330/gar.0020214.

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Nuclear theft from malicious insiders is a significant threat to Pakistan’s nuclear weapons arsenal. Pakistan is a member of the Convention of the Physical Protection of Nuclear Material (CPPNM), which is an international agreement that adheres to the protection of nuclear materials and the recovery of stolen nuclear materials. However, this agreement does not specifically take into account the risk of security breaches arising from malicious insiders due to Pakistan’s rapidly growing nuclear arsenal. The purpose of this paper is to examine the heightened risk of insider threats in conjunction with Pakistan’s increasing nuclear force structure. The first section of the paper examines the history of the development of Pakistan’s nuclear weapons programme and discusses Pakistan’s current nuclear force structure. The second section examines the international and domestic policies that Pakistan follows to address the issue of insider threats to Pakistan’s nuclear facilities. The final section proposes two policy alternatives to address Pakistan’s growing insider threat risks and outlines how the Design Basis Threat assessment is the most effective solution for Pakistan’s growing insider threat.

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Park, Won, Youngin You, and Kyungho Lee. "Detecting Potential Insider Threat: Analyzing Insiders’ Sentiment Exposed in Social Media." Security and Communication Networks 2018 (July 18, 2018): 1–8. http://dx.doi.org/10.1155/2018/7243296.

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In the era of Internet of Things (IoT), impact of social media is increasing gradually. With the huge progress in the IoT device, insider threat is becoming much more dangerous. Trying to find what kind of people are in high risk for the organization, about one million of tweets were analyzed by sentiment analysis methodology. Dataset made by the web service “Sentiment140” was used to find possible malicious insider. Based on the analysis of the sentiment level, users with negative sentiments were classified by the criteria and then selected as possible malicious insiders according to the threat level. Machine learning algorithms in the open-sourced machine learning software “Weka (Waikato Environment for Knowledge Analysis)” were used to find the possible malicious insider. Decision Tree had the highest accuracy among supervised learning algorithms and K-Means had the highest accuracy among unsupervised learning. In addition, we extract the frequently used words from the topic modeling technique and then verified the analysis results by matching them to the information security compliance elements. These findings can contribute to achieve higher detection accuracy by combining individual’s characteristics to the previous studies such as analyzing system behavior.

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Polyanichko, M. "Methodology for Detecting Anomalous Interaction of Users with Information Assets to Identify Insider Activity." Proceedings of Telecommunication Universities 6, no. 1 (2020): 94–98. http://dx.doi.org/10.31854/1813-324x-2020-6-1-94-98.

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The article describe us that the identification of insiders and insider activities in the organization is an actual direction of ensuring information security, since the high level of software development and hardware information protection brings the malicious actions of legitimate users to the fore. This article discusses the methodology which allows to identify anomalies in the organization’s employees interaction with information assets and its applicability is assessed in the context of work to detect malicious activities of insiders.

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Saranjame, Rahul, and Manik Lal Das. "Securing Digital Image from Malicious Insider Attacks." International Journal of Computer Vision and Image Processing 8, no. 2 (2018): 49–58. http://dx.doi.org/10.4018/ijcvip.2018040103.

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Protection of digital images from malicious (and dishonest) insider entities is an important concern in modern digital space. A malicious entity can alter some important features of an image to mislead the target recipient of the image, which can cause harm in applications such as healthcare, insurance, product description and so on. In order to protect digital images, cryptographic primitives such as pseudo-random function and digital signature have been used in practice for image protection. In this article, the authors present a method of image protection from malicious image manipulation. The authors use the concept of secret sharing and public key primitives in the proposed method. This article discusses on attacks involving generation of fake shares to cheat honest user(s) and a demonstration of the proposed system employing a centralized server to generate shares and authenticate them on the basis of requests is made as a counter to the described attack.

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Liang, Nan (Peter), David P. Biros, and Andy Luse. "An Empirical Validation of Malicious Insider Characteristics." Journal of Management Information Systems 33, no. 2 (2016): 361–92. http://dx.doi.org/10.1080/07421222.2016.1205925.

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Gayathri, R. G., Atul Sajjanhar, and Yong Xiang. "Image-Based Feature Representation for Insider Threat Classification." Applied Sciences 10, no. 14 (2020): 4945. http://dx.doi.org/10.3390/app10144945.

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Cybersecurity attacks can arise from internal and external sources. The attacks perpetrated by internal sources are also referred to as insider threats. These are a cause of serious concern to organizations because of the significant damage that can be inflicted by malicious insiders. In this paper, we propose an approach for insider threat classification which is motivated by the effectiveness of pre-trained deep convolutional neural networks (DCNNs) for image classification. In the proposed approach, we extract features from usage patterns of insiders and represent these features as images. Hence, images are used to represent the resource access patterns of the employees within an organization. After construction of images, we use pre-trained DCNNs for anomaly detection, with the aim to identify malicious insiders. Random under sampling is used for reducing the class imbalance issue. The proposed approach is evaluated using the MobileNetV2, VGG19, and ResNet50 pre-trained models, and a benchmark dataset. Experimental results show that the proposed method is effective and outperforms other state-of-the-art methods.

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G S, Smrithy, Alfredo Cuzzocrea, and Ramadoss Balakrishnan. "Detecting Insider Malicious Activities in Cloud Collaboration Systems." Fundamenta Informaticae 161, no. 3 (2018): 299–316. http://dx.doi.org/10.3233/fi-2018-1704.

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Oberoi, Priya, Sumit Mittal, and Rajneesh Kumar Gujral. "ADRCN: A Framework to Detect and Mitigate Malicious Insider Attacks in Cloud-Based Environment on IaaS." International Journal of Mathematical, Engineering and Management Sciences 4, no. 3 (2019): 654–70. http://dx.doi.org/10.33889//ijmems.2019.4.3-052.

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Security is a critical factor for any of the computing platforms. Cloud computing is a new computing environment but still, its basic technology is the Internet. Thus, Cloud computing environment not only has the threats of its own but it is also prone to security issues of its underlying technology i.e. Internet. In this paper, the authors are proposing a secure routing framework viz. Authenticated Dynamic Routing in Cloud Networks (ADRCN) to mitigate the malicious insider attacks while maintaining the path integrity in the Clouds. Symmetric cryptography with hashing is used to maintain the integrity of the path between the source and destination. The purpose of ADRCN is to maintain the integrity of the path between the client and data center. If malicious insider tries to perform an attack between the client and the data center then it will be detected. This work aims to give a solution for detection and prevention of malicious insider attacks in Cloud-based environments.

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Ankayarkanni, B. "Alleviating Malicious Insider in Cloud Through Offensive Decoy Technology." International Journal on Information Sciences and Computing 8, no. 2 (2014): 1–6. http://dx.doi.org/10.18000/ijisac.50145.

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Khan, Ahmed Yar, Rabia Latif, Seemab Latif, Shahzaib Tahir, Gohar Batool, and Tanzila Saba. "Malicious Insider Attack Detection in IoTs Using Data Analytics." IEEE Access 8 (2020): 11743–53. http://dx.doi.org/10.1109/access.2019.2959047.

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Ahmad, Mir Shahnawaz, and Shahid Mehraj Shah. "Mitigating Malicious Insider Attacks in the Internet of Things using Supervised Machine Learning Techniques." Scalable Computing: Practice and Experience 22, no. 1 (2021): 13–28. http://dx.doi.org/10.12694/scpe.v22i1.1818.

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The interconnection of large number of smart devices and sensors for critical information gathering and analysis over the internet has given rise to the Internet of Things (IoT) network. In recent times, IoT has emerged as a prime field for solving diverse real-life problems by providing a smart and affordable solutions. The IoT network has various constraints like: limited computational capacity of sensors, heterogeneity of devices, limited energy resource and bandwidth etc. These constraints restrict the use of high-end security mechanisms, thus making these type of networks more vulnerable to various security attacks including malicious insider attacks. Also, it is very difficult to detect such malicious insiders in the network due to their unpredictable behaviour and the ubiquitous nature of IoT network makes the task more difficult. To solve such problems machine learning techniques can be used as they have the ability to learn the behaviour of the system and predict the particular anomaly in the system. So, in this paper we have discussed various security requirements and challenges in the IoT network. We have also applied various supervised machine learning techniques on available IoT dataset to deduce which among them is best suited to detect the malicious insider attacks in the IoT network.

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Gunasekhar, T., K. Thirupathi Rao, V. Krishna Reddy, P. Sai Kiran, and B. Thirumala Rao. "Mitigation of Insider Attacks through Multi-Cloud." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 1 (2015): 136. http://dx.doi.org/10.11591/ijece.v5i1.pp136-141.

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The malicious insider can be an employees, user and/or third party business partner. In cloud environment, clients may store sensitive data about their organization in cloud data centers. The cloud service provider should ensure integrity, security, access control and confidentiality about the stored data at cloud data centers. The malicious insiders can perform stealing on sensitive data at cloud storage and at organizations. Most of the organizations ignoring the insider attack because it is harder to detect and mitigate. This is a major emerging problem at the cloud data centers as well as in organizations. In this paper, we proposed a method that ensures security, integrity, access control and confidentiality on sensitive data of cloud clients by employing multi cloud service providers. The organization should encrypt the sensitive data with their security policy and procedures and store the encrypted data in trusted cloud. The keys which are used during encryption process are again encrypted and stored in another cloud area. So that organization contains only keys for keys of encrypted data. The Administrator of organization also does not know what data kept in cloud area and if he accesses the data, easily caught during the auditing. Hence, the only authorized used can access the data and use it and we can mitigate insider attacks by providing restricted privileges.

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Michael, Antonia, and Jan Eloff. "Discovering “Insider IT Sabotage” based on human behaviour." Information & Computer Security 28, no. 4 (2020): 575–89. http://dx.doi.org/10.1108/ics-12-2019-0141.

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Purpose Malicious activities conducted by disgruntled employees via an email platform can cause profound damage to an organization such as financial and reputational losses. This threat is known as an “Insider IT Sabotage” threat. This involves employees misusing their access rights to harm the organization. Events leading up to the attack are not technical but rather behavioural. The problem is that owing to the high volume and complexity of emails, the risk of insider IT sabotage cannot be diminished with rule-based approaches. Design/methodology/approach Malicious human behaviours that insiders within the insider IT sabotage category would possess are studied and mapped to phrases that would appear in email communications. A large email data set is classified according to behavioural characteristics of these employees. Machine learning algorithms are used to identify occurrences of this insider threat type. The accuracy of these approaches is measured. Findings It is shown in this paper that suspicious behaviour of disgruntled employees can be discovered, by means of machine intelligence techniques. The output of the machine learning classifier depends mainly on the depth and quality of the phrases and behaviour analysis, cleansing and number of email attributes examined. This process of labelling content in isolation could be improved if other attributes of the email data are included, such that a confidence score can be computed for each user. Originality/value This research presents a novel approach to show that the creation of a prototype that can automate the detection of insider IT sabotage within email systems to mitigate the risk within organizations.

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Al-Mhiqani, Mohammed Nasser, Rabiah Ahmad, Z. Zainal Abidin, et al. "A Review of Insider Threat Detection: Classification, Machine Learning Techniques, Datasets, Open Challenges, and Recommendations." Applied Sciences 10, no. 15 (2020): 5208. http://dx.doi.org/10.3390/app10155208.

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Insider threat has become a widely accepted issue and one of the major challenges in cybersecurity. This phenomenon indicates that threats require special detection systems, methods, and tools, which entail the ability to facilitate accurate and fast detection of a malicious insider. Several studies on insider threat detection and related areas in dealing with this issue have been proposed. Various studies aimed to deepen the conceptual understanding of insider threats. However, there are many limitations, such as a lack of real cases, biases in making conclusions, which are a major concern and remain unclear, and the lack of a study that surveys insider threats from many different perspectives and focuses on the theoretical, technical, and statistical aspects of insider threats. The survey aims to present a taxonomy of contemporary insider types, access, level, motivation, insider profiling, effect security property, and methods used by attackers to conduct attacks and a review of notable recent works on insider threat detection, which covers the analyzed behaviors, machine-learning techniques, dataset, detection methodology, and evaluation metrics. Several real cases of insider threats have been analyzed to provide statistical information about insiders. In addition, this survey highlights the challenges faced by other researchers and provides recommendations to minimize obstacles.

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Furnell, Steven. "Malicious or misinformed? Exploring a contributor to the insider threat." Computer Fraud & Security 2006, no. 9 (2006): 8–12. http://dx.doi.org/10.1016/s1361-3723(06)70419-5.

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Walker, Terrence. "Practical management of malicious insider threat – An enterprise CSIRT perspective." Information Security Technical Report 13, no. 4 (2008): 225–34. http://dx.doi.org/10.1016/j.istr.2008.10.013.

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Stafford, Thomas, George Deitz, and Yaojie Li. "The role of internal audit and user training in information security policy compliance." Managerial Auditing Journal 33, no. 4 (2018): 410–24. http://dx.doi.org/10.1108/maj-07-2017-1596.

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Purpose The purpose of the study is to investigate the role of information security policy compliance and the role of information systems auditing in identifying non-compliance in the workplace, with specific focus on the role of non-malicious insiders who unknowingly or innocuously thwart corporate information security (IS) directives by engaging in unsafe computing practices. The ameliorative effects of auditor-identified training and motivational programs to emphasize pro-security behaviors are explored. Design/methodology/approach This study applies qualitative case analysis of technology user security perceptions combined with interpretive analysis of depth interviews with auditors to examine and explain the rubrics of non-malicious technology user behaviors in violation of cybersecurity directives, to determine the ways in which auditors can best assist management in overcoming the problems associated with security complacency among users. Findings Enterprise risk management benefits from audits that identify technology users who either feel invulnerable to cyber threats and exploits or feel that workplace exigencies augur for expedient workarounds of formal cybersecurity policies. Research limitations/implications Implications for consideration of CyberComplacency and Cybersecurity Loafing expand the insider threat perspective beyond the traditional malicious insider perspective. Practical implications Implications for consideration of CyberComplacency and Cybersecurity Loafing include broadened perspectives for the consultative role of IS audit in the firm. Social implications CyberComplacency is a practice that has great potential for harm in all walks of life. A better understanding of these potential harms is beneficial. Originality/value This study is the first to characterize CyberComplacency as computer users who feel they operate invulnerable platforms and are subsequently motivated to engage in less cybersecurity diligence than the company would desire. This study is also the first to characterize the notion of Cybersecurity Loafing to describe technically competent workers who take unauthorized but expedient steps around certain security polices in the name of workgroup efficiency.

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Shaw, Eric D. "The role of behavioral research and profiling in malicious cyber insider investigations." Digital Investigation 3, no. 1 (2006): 20–31. http://dx.doi.org/10.1016/j.diin.2006.01.006.

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PARVEEN, PALLABI, NATHAN MCDANIEL, ZACKARY WEGER, et al. "EVOLVING INSIDER THREAT DETECTION STREAM MINING PERSPECTIVE." International Journal on Artificial Intelligence Tools 22, no. 05 (2013): 1360013. http://dx.doi.org/10.1142/s0218213013600130.

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Evidence of malicious insider activity is often buried within large data streams, such as system logs accumulated over months or years. Ensemble-based stream mining leverages multiple classification models to achieve highly accurate anomaly detection in such streams, even when the stream is unbounded, evolving, and unlabeled. This makes the approach effective for identifying insider threats who attempt to conceal their activities by varying their behaviors over time. This paper applies ensemble-based stream mining, supervised and unsupervised learning, and graph-based anomaly detection to the problem of insider threat detection. It demonstrates that the ensemble-based approach is significantly more effective than traditional single-model methods, supervised learning outperforms unsupervised learning, and increasing the cost of false negatives correlates to higher accuracy. Future work will consider a wider range of tunable parameters in an effort to further reduce false positives, include a more sophisticated polling algorithm for weighting better models, and implement parallelization to lower runtimes to more rapidly detect emerging insider threats.

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Ahmad, Shahnawaz. "Alleviating Malicious Insider Attacks in MANET using a Multipath On-demand Security Mechanism." International Journal of Computer Network and Information Security 10, no. 6 (2018): 40–51. http://dx.doi.org/10.5815/ijcnis.2018.06.04.

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Kumar, Gulshan, Mritunjay Kumar Rai, Hye-jin Kim, and Rahul Saha. "A Secure Localization Approach Using Mutual Authentication and Insider Node Validation in Wireless Sensor Networks." Mobile Information Systems 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/3243570.

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Localization is a concerning issue in the applications of wireless sensor networks. Along with the accuracy of the location estimation of the sensor nodes, the security of the estimation is another priority. Wireless sensor networks often face various attacks where the attackers try to manipulate the estimated location or try to provide false beacons. In this paper, we have proposed a methodology that will address this problem of security aspects in localization of the sensor nodes. Moreover, we have considered the network environment with random node deployment and mobility as these two conditions are less addressed in previous research works. Further, our proposed algorithm provides low overhead due to the usage of less control messages in a limited transmission range. In addition, we have also proposed an algorithm to detect the malicious anchor nodes inside the network. The simulated results show that our proposed algorithm is efficient in terms of time consumption, localization accuracy, and localization ratio in the presence of malicious nodes.

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Al Sharah, Ashraf, Taiwo Oyedare, and Sachin Shetty. "Detecting and Mitigating Smart Insider Jamming Attacks in MANETs Using Reputation-Based Coalition Game." Journal of Computer Networks and Communications 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/4289176.

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Security in mobile ad hoc networks (MANETs) is challenging due to the ability of adversaries to gather necessary intelligence to launch insider jamming attacks. The solutions to prevent external attacks on MANET are not applicable for defense against insider jamming attacks. There is a need for a formal framework to characterize the information required by adversaries to launch insider jamming attacks. In this paper, we propose a novel reputation-based coalition game in MANETs to detect and mitigate insider jamming attacks. Since there is no centralized controller in MANETs, the nodes rely heavily on availability of transmission rates and a reputation for each individual node in the coalition to detect the presence of internal jamming node. The nodes will form a stable grand coalition in order to make a strategic security defense decision, maintain the grand coalition based on node reputation, and exclude any malicious node based on reputation value. Simulation results show that our approach provides a framework to quantify information needed by adversaries to launch insider attacks. The proposed approach will improve MANET’s defense against insider attacks, while also reducing incorrect classification of legitimate nodes as jammers.

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Kotenko, Igor, Andrey Krasov, Igor Ushakov, and Konstantin Izrailov. "An Approach for Stego-Insider Detection Based on a Hybrid NoSQL Database." Journal of Sensor and Actuator Networks 10, no. 2 (2021): 25. http://dx.doi.org/10.3390/jsan10020025.

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One of the reasons for the implementation of information security threats in organizations is the insider activity of its employees. There is a big challenge to detect stego-insiders-employees who create stego-channels to secretly receive malicious information and transfer confidential information across the organization’s perimeter. Especially presently, with great popularity of wireless sensor networks (WSNs) and Internet of Things (IoT) devices, there is a big variety of information that could be gathered and processed by stego-insiders. Consequently, the problem arises of identifying such intruders and their transmission channels. The paper proposes an approach to solving this problem. The paper provides a review of the related works in terms of insider models and methods of their identification, including techniques for handling insider attacks in WSN, as well methods of embedding and detection of stego-embeddings. This allows singling out the basic features of stego-insiders, which could be determined by their behavior in the network. In the interests of storing these attributes of user behavior, as well as storing such attributes from large-scale WSN, a hybrid NoSQL database is created based on graph and document-oriented approaches. The algorithms for determining each of the features using the NoSQL database are specified. The general scheme of stego-insider detection is also provided. To confirm the efficiency of the approach, an experiment was carried out on a real network. During the experiment, a database of user behavior was collected. Then, user behavior features were retrieved from the database using special SQL queries. The analysis of the results of SQL queries is carried out, and their applicability for determining the attribute is justified. Weak points of the approach and ways to improve them are indicated.

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Kong, Fan Yu, Lu Hong Diao, Jia Yu, Ya Li Jiang, and Da Shui Zhou. "Insider Forgery Cryptanalysis of Two Post-Quantum Multi-Signature Schemes." Applied Mechanics and Materials 437 (October 2013): 876–79. http://dx.doi.org/10.4028/www.scientific.net/amm.437.876.

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In 2010, M. Meziani and P.-L. Cayrel presented two post-quantum multi-signature schemes based on the syndrome decoding hard problem and error correcting codes. In this paper, we propose the insider forgery cryptanalysis of M. Meziani et al.s post-quantum multi-signature schemes. In M. Meziani et al.s schemes, the verifier only verifies the final multi-signature and does not check the validity of the partial signatures generated by other signers. Thus the malicious last signer can forge a valid multi-signature by himself/herself on behalf of the group of signers, which can pass the verification of the verifier. Therefore, M. Meziani et al.s post-quantum multi-signature schemes do not meet the security requirements of multi-signature schemes.

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Sheykhkanloo, Naghmeh Moradpoor, and Adam Hall. "Insider Threat Detection Using Supervised Machine Learning Algorithms on an Extremely Imbalanced Dataset." International Journal of Cyber Warfare and Terrorism 10, no. 2 (2020): 1–26. http://dx.doi.org/10.4018/ijcwt.2020040101.

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An insider threat can take on many forms and fall under different categories. This includes malicious insider, careless/unaware/uneducated/naïve employee, and the third-party contractor. Machine learning techniques have been studied in published literature as a promising solution for such threats. However, they can be biased and/or inaccurate when the associated dataset is hugely imbalanced. Therefore, this article addresses the insider threat detection on an extremely imbalanced dataset which includes employing a popular balancing technique known as spread subsample. The results show that although balancing the dataset using this technique did not improve performance metrics, it did improve the time taken to build the model and the time taken to test the model. Additionally, the authors realised that running the chosen classifiers with parameters other than the default ones has an impact on both balanced and imbalanced scenarios, but the impact is significantly stronger when using the imbalanced dataset.

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Li, Dongyang, Lin Yang, Hongguang Zhang, Xiaolei Wang, Linru Ma, and Junchao Xiao. "Image-Based Insider Threat Detection via Geometric Transformation." Security and Communication Networks 2021 (September 13, 2021): 1–18. http://dx.doi.org/10.1155/2021/1777536.

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Insider threat detection has been a challenging task over decades; existing approaches generally employ the traditional generative unsupervised learning methods to produce normal user behavior model and detect significant deviations as anomalies. However, such approaches are insufficient in precision and computational complexity. In this paper, we propose a novel insider threat detection method, Image-based Insider Threat Detector via Geometric Transformation (IGT), which converts the unsupervised anomaly detection into supervised image classification task, and therefore the performance can be boosted via computer vision techniques. To illustrate, our IGT uses a novel image-based feature representation of user behavior by transforming audit logs into grayscale images. By applying multiple geometric transformations on these behavior grayscale images, IGT constructs a self-labelled dataset and then trains a behavior classifier to detect anomaly in a self-supervised manner. The motivation behind our proposed method is that images converted from normal behavior data may contain unique latent features which remain unchanged after geometric transformation, while malicious ones cannot. Experimental results on CERT dataset show that IGT outperforms the classical autoencoder-based unsupervised insider threat detection approaches, and improves the instance and user based Area under the Receiver Operating Characteristic Curve (AUROC) by 4% and 2%, respectively.

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Berman, Daniel, Anna Buczak, Jeffrey Chavis, and Cherita Corbett. "A Survey of Deep Learning Methods for Cyber Security." Information 10, no. 4 (2019): 122. http://dx.doi.org/10.3390/info10040122.

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This survey paper describes a literature review of deep learning (DL) methods for cyber security applications. A short tutorial-style description of each DL method is provided, including deep autoencoders, restricted Boltzmann machines, recurrent neural networks, generative adversarial networks, and several others. Then we discuss how each of the DL methods is used for security applications. We cover a broad array of attack types including malware, spam, insider threats, network intrusions, false data injection, and malicious domain names used by botnets.

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Maestre Vidal, Jorge, and Marco Antonio Sotelo Monge. "Obfuscation of Malicious Behaviors for Thwarting Masquerade Detection Systems Based on Locality Features." Sensors 20, no. 7 (2020): 2084. http://dx.doi.org/10.3390/s20072084.

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In recent years, dynamic user verification has become one of the basic pillars for insider threat detection. From these threats, the research presented in this paper focuses on masquerader attacks, a category of insiders characterized by being intentionally conducted by persons outside the organization that somehow were able to impersonate legitimate users. Consequently, it is assumed that masqueraders are unaware of the protected environment within the targeted organization, so it is expected that they move in a more erratic manner than legitimate users along the compromised systems. This feature makes them susceptible to being discovered by dynamic user verification methods based on user profiling and anomaly-based intrusion detection. However, these approaches are susceptible to evasion through the imitation of the normal legitimate usage of the protected system (mimicry), which is being widely exploited by intruders. In order to contribute to their understanding, as well as anticipating their evolution, the conducted research focuses on the study of mimicry from the standpoint of an uncharted terrain: the masquerade detection based on analyzing locality traits. With this purpose, the problem is widely stated, and a pair of novel obfuscation methods are introduced: locality-based mimicry by action pruning and locality-based mimicry by noise generation. Their modus operandi, effectiveness, and impact are evaluated by a collection of well-known classifiers typically implemented for masquerade detection. The simplicity and effectiveness demonstrated suggest that they entail attack vectors that should be taken into consideration for the proper hardening of real organizations.

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Oberoi, Priya, Sumit Mittal, and Rajneesh Kumar Gujral. "ARCN: Authenticated Routing on Cloud Network to Mitigate Insider Attacks on Infrastructure as a Service." Journal of Computational and Theoretical Nanoscience 16, no. 9 (2019): 3945–54. http://dx.doi.org/10.1166/jctn.2019.8275.

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Cloud security is one of the major issues which are affecting the acceptance of Cloud computing (CC). In spite of the number of benefits offered by CC, it also suffers from a myriad of security vulnerabilities. Security is the prime concern while we are accessing the resources from the Cloud through the Internet. This research work is primarily on IAAS (Infrastructure as a service). Here a protocol called Authenticated Routing on Cloud Network (ARCN) has been proposed, to secure the communication route between the client and service provider. The proposed protocol ARCN uses the public key cryptography to mitigate the various security attacks like malicious insider attacks, Spoofing, Falsified routes, DoS, etc.

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Rao, O. Srinivasa, and K. Madhuri. "Path Reconstruction and Effective Routing with Advanced Congestion Diversity in Wireless Adhoc Networks." International Journal of Emerging Research in Management and Technology 6, no. 9 (2018): 125. http://dx.doi.org/10.23956/ijermt.v6i9.96.

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Link error and malicious packet dropping are two sources for packet losses in multi-hop wireless ad hoc network. In this paper, while observing a sequence of packet losses in the network, we are interested in determining whether the losses are caused by link errors only, or by the combined effect of link errors and malicious drop. We are especially interested in the insider-attack case, whereby malicious nodes that are part of the route exploit their knowledge of the communication context to selectively drop a small amount of packets critical to the network performance. Because the packet dropping rate in this case is comparable to the channel error rate, conventional algorithms that are based on detecting the packet loss rate cannot achieve satisfactory detection accuracy. To improve the detection accuracy, we propose to exploit the correlations between lost packets. Furthermore, to ensure truthful calculation of these correlations, we develop a homomorphic linear authenticator (HLA) based public auditing architecture that allows the detector to verify the truthfulness of the packet loss information reported by nodes. This construction is privacy preserving, collusion proof, and incurs low communication and storage overheads. To reduce the computation overhead of the baseline scheme, a packet-block based mechanism is also proposed, which allows one to trade detection accuracy for lower computation complexity. Through extensive simulations, we verify that the proposed mechanisms achieve significantly better detection accuracy than conventional methods such as a maximum-likelihood based detection.

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Gupta, Radhika, Dr Sahil Verma, Dr Kavita, and Anup Ial Yadav. "A Comparative Analysis of Trust Based Applications in Wireless Sensor Networks." International Journal of Engineering & Technology 7, no. 4.12 (2018): 73. http://dx.doi.org/10.14419/ijet.v7i4.12.20996.

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The wireless sensor network is a type of ad hoc network which is vulnerable to security attacks, specifically insider attacks. In spite of the fact that confidentiality, integrity and authentication helps in forestalling the particular sort of attacks, but they come at an expense. A traditional and evergreen concept of the trust evaluation and management, among the nodes of a network, for communication is a good and effective security measure. Overseeing trust in a distributed wireless sensor network is a challenging task when coordinated effort or participation is must in accomplishing mission and framework objectives. The paper represents a survey of various trust applications which are very helpful for carrying out a secure data transmission in a sensor network. The analysed trust applications malicious attack detection, secure data aggregation, secure node selection and secure routing.

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Jayashree, Devasagayam, V. Uma Rani, and K. Soma Sundaram. "Trust Based Misbehavior Detection in Wireless Sensor Networks." Applied Mechanics and Materials 622 (August 2014): 191–98. http://dx.doi.org/10.4028/www.scientific.net/amm.622.191.

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Due to emerging technology Wireless Sensor Network (WSN), it is necessary to monitor the behavior of sensor nodes and establish the secure communication in network. Security is a challenging task in wireless environment. Several encryption mechanisms are available to prevent outsider attacks, but no mechanism available for insider attacks. A trust model is a collection of rules used to establish co-operation or collaboration among nodes as well as monitoring misbehavior of wireless sensor networks. Trust model is necessary to enhance secure localization, communication or routing, aggregation, collaboration among nodes. In this paper, proposed a behavior based distributed trust model for wireless sensor network to effectively deal with self-ish or malicious nodes. Here, take multidimensional trust attributes derived from communications and networks to evaluate the overall trust of sensor nodes. It monitors the behavior of nodes and establishes secure communication among networks.

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Singh, Nagendra Kumar, and Sandeep Kumar Nayak. "The Threat Detection Framework for Securing Semantic Web Services." Journal of Computational and Theoretical Nanoscience 16, no. 12 (2019): 5099–104. http://dx.doi.org/10.1166/jctn.2019.8569.

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Security is a necessary aspect of modern life for organizations and individuals who use the semantic web to provide various services. Semantic web applications are being used as a portal to communicate with back-end database systems and to support business processes. The confidential and personal information of any organization is stored on these systems. Access Control ensures that the requesting user has to meet certain criteria to access these systems. In most cases, it has been observed that access control only provides protection against external threats. There is no provision for detecting internal attacks. Therefore, there is a need for a mechanism that can be able to detect the malicious behaviour of previously authorized users. This paper proposes two algorithms to detect anomalous behaviour performed by the legitimate insider. During training phase, the first algorithm will create the query signature of each incoming query submitted by the legitimate insider. It also estimates the amount of data that can be extracted by the submitted query and includes in the query signature. The second algorithm will detect incongruous data extraction from the database by comparing the current query signature with the previous signature. If both signatures are identical, the query is considered safe for execution. Otherwise, the query will be considered as threat. In this paper, efforts are being made to give details of the security structure on the semantic web service.

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Y. F. Al_Sahlani, Ahmed, and Songfeng Lu. "Lightweight Communication Overhead Authentication Scheme Using Smart Card." Indonesian Journal of Electrical Engineering and Computer Science 1, no. 3 (2016): 597. http://dx.doi.org/10.11591/ijeecs.v1.i3.pp597-606.

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<p>Authentication takes its place to grant authorized user a remote access to certain online resources. As well, prevent unauthorized user from accessing that resources. Unforyunatly most of authentication schemes consider only security factors without taking in consideration the communication resources required. Recently, Li et. al. proposed an enhanced smart card based remote user password authentication scheme. We analyzed their scheme and we pointed out that, their scheme required high communication overhead. Furthermore, their scheme suffer from forgery, user impersonation and server impersonation attacks. Through this paper to address aforesaid weaknesses, we propose a Lightweight communication overhead authentication scheme using smart card. The security and performance analysis shows that, our proposed scheme is lightweight communication and computation cost as well secure and can withstand wide spectrum of malicious attacks, like forgery, insider, replay and stolen smart card attack. Besides, our scheme encompasses desired security attributes. Therefore, it is suitable for practical use compared to other related scheme. </p>

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Brahma, Anitarani, and Suvasini Panigrahi. "Role-Based Profiling Using Fuzzy Adaptive Resonance Theory for Securing Database Systems." International Journal of Applied Metaheuristic Computing 12, no. 2 (2021): 36–48. http://dx.doi.org/10.4018/ijamc.2021040103.

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Very large amounts of time and effort have been invested by the research community working on database security to achieve high assurance of security and privacy. An important component of a secure database system is intrusion detection system which has the ability to successfully detect anomalous behavior caused by applications and users. However, modeling the normal behavior of a large number of users in a huge organization is quite infeasible and inefficient. The main purpose of this research investigation is thus to model the behavior of roles instead of users by applying adaptive resonance theory neural network. The observed behavior which deviates from any of the established role profiles is treated as malicious. The proposed model has the advantage of identifying insider threat and is applicable for large organizations as it is based on role profiling instead of user profiling. The proposed system is capable of detecting intrusion with high accuracy along with minimized false alarms.

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Siddiqui, Sarah Ali, Adnan Mahmood, Quan Z. Sheng, Hajime Suzuki, and Wei Ni. "A Survey of Trust Management in the Internet of Vehicles." Electronics 10, no. 18 (2021): 2223. http://dx.doi.org/10.3390/electronics10182223.

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Over the past decade, the groundbreaking technological advancements in the Internet of Vehicles (IoV) coupled with the notion of trust have attracted increasing attention from researchers and experts in intelligent transportation systems (ITS), wherein vehicles establish a belief towards their peers in the pursuit of ensuring safe and efficacious traffic flows. Diverse domains have been taking advantage of trust management models in the quest of alleviating diverse insider attacks, wherein messages generated by legitimate users are altered or counterfeited by malicious entities, subsequently, endangering the lives of drivers, passengers, and vulnerable pedestrians. In the course of vehicles forming perceptions towards other participating vehicles, a range of contributing parameters regarding the interactions among these vehicles are accumulated to establish a final opinion towards a target vehicle. The significance of these contributing parameters is typically represented by associating a weighting factor to each contributing attribute. The values assigned to these weighting factors are often set manually, i.e., these values are predefined and do not take into consideration any affecting parameters. Furthermore, a threshold is specified manually that classifies the vehicles into honest and dishonest vehicles relying on the computed trust. Moreover, adversary models as an extension to trust management models in order to tackle the variants of insider attacks are being extensively emphasized in the literature. This paper, therefore, reviews the state of the art in the vehicular trust management focusing on the aforementioned factors such as quantification of weights, quantification of threshold, misbehavior detection, etc. Moreover, an overarching IoV architecture, constituents within the notion of trust, and attacks relating to the IoV have also been presented in addition to open research challenges in the subject domain.

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Panda, Niranjan, and Binod Kumar Pattanayak. "Energy aware detection and prevention of black hole attack in MANET." International Journal of Engineering & Technology 7, no. 2.6 (2018): 135. http://dx.doi.org/10.14419/ijet.v7i2.6.10139.

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Energy and security are the two vital components of Mobile Ad-hoc Networks (MANETs). During routing finding an optimal path from sender to receiver sense of path length (number of hops), longevity (battery life) and security becomes an essential requirement. Variety of schemes are proposed by the researchers for finding the shortest path along with energy saving and protecting from attacks Black-hole and gray-hole attacks are some of the most harmful attacks against MANET communication and needs attention. These attacks may cause by insider or outsider malicious node(s) who may drop packets or misroute the information during communication from sender node to receiver node. In our study we proposed an intelligent routing protocol based on Ant Colony Optimization (ACO) technique that finds shortest path from source to destination, applies the concept of power aware techniques to save energy increasing the longevity of the link avoiding link failure and also uses the concept of digital signatures, watchdog and path rater for detection and avoidance ofblack-hole and gray-hole attacks. Simulation study of the proposed scheme is made over some network parameters and found to be efficient in comparison to the basic AODV routing protocol.

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Bukkawar, Mrunali, and Mohd Shafi Pathan. "Web-Proxy-Based Authentication and Authorization Mechanism Against Client-Based HTTP Attacks." International Journal of Synthetic Emotions 8, no. 1 (2017): 60–72. http://dx.doi.org/10.4018/ijse.2017010105.

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There has been a huge development in how to read a data from sensor device such as infrared (IR) device, temperature device, etc. Sensor data collection has wide issues of information security. Information security is also the current topic of discussion due to its use in application in various fields. There are number of users having different user roles with smart devices. These personnel use devices for various purposes like access the information from various devices such as wireless sensors so that a secure and efficient mutual authentication and authorization scheme is used in the smart grid network to prevent various insider and outsider attacks on information or data. Therefore, proposed work design novel approach to overcome that attack, malicious user and device by authentication and authorization. The technique of authenticate authenticates each user role dynamically using a signature based access control and verifies the identity of user together with the device. Access control mechanism not only prevents unauthorized access but also prevent misuse of data. Existing system generates shared key for each session but it generates huge overhead and not suitable for the real-world applications so in proposed system we used public key cryptography to reduce the overhead.

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Cui, Jianming, Xiaojun Zhang, Ning Cao, Dexue Zhang, Jianrui Ding, and Guofu Li. "An improved authentication protocol–based dynamic identity for multi-server environments." International Journal of Distributed Sensor Networks 14, no. 5 (2018): 155014771877765. http://dx.doi.org/10.1177/1550147718777654.

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The age of Internet of things gives rise to more challenges to various secure demands when designing the protocols, such as object identification and tracking, and privacy control. In many of the current protocols, a malicious server may cheat users as if it was a legal server, making it vital to verify the legality of both users and servers with the help of a trusted third-party, such as a registration center. Li et al. proposed an authentication protocol based on dynamic identity for multi-server environment, which is still susceptible to password-guessing attack, eavesdropping attack, masquerade attack, and insider attack etc. Besides, their protocol does not provide the anonymity of users, which is an essential request to protect users’ privacy. In this article, we present an improved authentication protocol, depending on the registration center in multi-server environments to remedy these security flaws. Different from the previous protocols, registration center in our proposed protocol is one of parties in authentication phase to verify the legality of the users and the servers, thus can effectively avoid the server spoofing attack. Our protocol only uses nonce, exclusive-OR operation, and one-way hash function in its implementation. Formal analysis has been performed using the Burrows–Abadi–Needham logic to show its security.

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Chen, Dr Joy Iong Zong, and Dr Smys S. "Social Multimedia Security and Suspicious Activity Detection in SDN using Hybrid Deep Learning Technique." June 2020 2, no. 2 (2020): 108–15. http://dx.doi.org/10.36548/jitdw.2020.2.004.

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Social multimedia traffic is growing exponentially with the increased usage and continuous development of services and applications based on multimedia. Quality of Service (QoS), Quality of Information (QoI), scalability, reliability and such factors that are essential for social multimedia networks are realized by secure data transmission. For delivering actionable and timely insights in order to meet the growing demands of the user, multimedia analytics is performed by means of a trust-based paradigm. Efficient management and control of the network is facilitated by limiting certain capabilities such as energy-aware networking and runtime security in Software Defined Networks. In social multimedia context, suspicious flow detection is performed by a hybrid deep learning based anomaly detection scheme in order to enhance the SDN reliability. The entire process is divided into two modules namely – Abnormal activities detection using support vector machine based on Gradient descent and improved restricted Boltzmann machine which facilitates the anomaly detection module, and satisfying the strict requirements of QoS like low latency and high bandwidth in SDN using end-to-end data delivery module. In social multimedia, data delivery and anomaly detection services are essential in order to improve the efficiency and effectiveness of the system. For this purpose, we use benchmark datasets as well as real time evaluation to experimentally evaluate the proposed scheme. Detection of malicious events like confidential data collection, profile cloning and identity theft are performed to analyze the performance of the system using CMU-based insider threat dataset for large scale analysis.

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Omar, Marwan, Derek Mohammed, and Van Nguyen. "Defending against malicious insiders: a conceptual framework for predicting, detecting, and deterring malicious insiders." International Journal of Business Process Integration and Management 8, no. 2 (2017): 114. http://dx.doi.org/10.1504/ijbpim.2017.083794.

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Omar, Marwan, Derek Mohammed, and Van Nguyen. "Defending against malicious insiders: a conceptual framework for predicting, detecting, and deterring malicious insiders." International Journal of Business Process Integration and Management 8, no. 2 (2017): 114. http://dx.doi.org/10.1504/ijbpim.2017.10004538.

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Talukdar, Md Ibrahim, Rosilah Hassan, Md Sharif Hossen, Khaleel Ahmad, Faizan Qamar, and Amjed Sid Ahmed. "Performance Improvements of AODV by Black Hole Attack Detection Using IDS and Digital Signature." Wireless Communications and Mobile Computing 2021 (March 2, 2021): 1–13. http://dx.doi.org/10.1155/2021/6693316.

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In mobile ad hoc networks (MANETs), mobile devices connect with other devices wirelessly, where there is no central administration. They are prone to different types of attacks such as the black hole, insider, gray hole, wormhole, faulty node, and packet drop, which considerably interrupt to perform secure communication. This paper has implemented the denial-of-service attacks like black hole attacks on general-purpose ad hoc on-demand distance vector (AODV) protocol. It uses three approaches: normal AODV, black hole AODV (BH_AODV), and detected black hole AODV (D_BH_AODV), wherein we observe that black holes acutely degrade the performance of networks. We have detected the black hole attacks within the networks using two techniques: (1) intrusion detection system (IDS) and (2) encryption technique (digital signature) with the concept of prevention. Moreover, normal AODV, BH_AODV, and D_BH_AODV protocols are investigated for various quality of service (QoS) parameters, i.e., packet delivery ratio (PDR), delay, and overhead with varying the number of nodes, packet sizes, and simulation times. The NS2 software has been used as a simulation tool to simulate existing network topologies, but it does not contain any mechanism to simulate malicious protocols by itself; therefore, we have developed and implemented a D_BH_AODV routing protocol. The outcomes show that the proposed D_BH_AODV approach for the PDR value delivers around 40 to 50% for varying nodes and packets. In contrast, the delay decreases from 300 to 100 ms and 150 to 50 ms with an increase in the number of nodes and packets, respectively. Furthermore, the overhead changes from 1 to 3 for various nodes and packet values. The outcome of this research proves that the black hole attack degrades the overall performance of the network, while the D_BH_AODV enhances the QoS performance since it detects the black hole nodes and avoids them to establish the communication between nodes.

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Badhani, Shikha, and Sunil K. Muttoo. "Evading android anti-malware by hiding malicious application inside images." International Journal of System Assurance Engineering and Management 9, no. 2 (2017): 482–93. http://dx.doi.org/10.1007/s13198-017-0692-7.

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Journal articles on the topic 'Malicious insider'

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