175 research outputs found

    Machine Learning based Attacks Detection and Countermeasures in IoT

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    While the IoT offers important benefits and opportunities for users, the technology raises various security issues and threats. These threats may include spreading IoT botnets through IoT devices which are the common and most malicious security threat in the world of internet. Protecting the IoT devices against these threats and attacks requires efficient detection. While we need to take into consideration IoT devices memory capacity limitation and low power processors. In this paper, we will focus in proposing low power consumption Machine Learning (ML) techniques for detecting IoT botnet attacks using Random forest as ML-based detection method and describing IoT common attacks with its countermeasures. The experimental result of our proposed solution shows higher accuracy. From the results, we conclude that IoT botnet detection is possible; achieving a higher accuracy rate as an experimental result indicates an accuracy rate of over 99.99% where the true positive rate is 1.000 and the false-negative rate is 0.000

    Unsupervised detection of botnet activities using frequent pattern tree mining

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    A botnet is a network of remotely-controlled infected computers that can send spam, spread viruses, or stage denial-of-serviceattacks, without the consent of the computer owners. Since the beginning of the 21st century, botnet activities have steadilyincreased, becoming one of the major concerns for Internet security. In fact, botnet activities are becoming more and moredifficult to be detected, because they make use of Peer-to-Peer protocols (eMule, Torrent, Frostwire, Vuze, Skype and manyothers). To improve the detectability of botnet activities, this paper introduces the idea of association analysis in the field ofdata mining, and proposes a system to detect botnets based on the FP-growth (Frequent Pattern Tree) frequent item miningalgorithm. The detection system is composed of three parts: packet collection processing, rule mining, and statistical analysisof rules. Its characteristic feature is the rule-based classification of different botnet behaviors in a fast and unsupervisedfashion. The effectiveness of the approach is validated in a scenario with 11 Peer-to-Peer host PCs, 42063 Non-Peer-to-Peerhost PCs, and 17 host PCs with three different botnet activities (Storm, Waledac and Zeus). The recognition accuracy of theproposed architecture is shown to be above 94%. The proposed method is shown to improve the results reported in literature

    From Intrusion Detection to Attacker Attribution: A Comprehensive Survey of Unsupervised Methods

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    Over the last five years there has been an increase in the frequency and diversity of network attacks. This holds true, as more and more organisations admit compromises on a daily basis. Many misuse and anomaly based Intrusion Detection Systems (IDSs) that rely on either signatures, supervised or statistical methods have been proposed in the literature, but their trustworthiness is debatable. Moreover, as this work uncovers, the current IDSs are based on obsolete attack classes that do not reflect the current attack trends. For these reasons, this paper provides a comprehensive overview of unsupervised and hybrid methods for intrusion detection, discussing their potential in the domain. We also present and highlight the importance of feature engineering techniques that have been proposed for intrusion detection. Furthermore, we discuss that current IDSs should evolve from simple detection to correlation and attribution. We descant how IDS data could be used to reconstruct and correlate attacks to identify attackers, with the use of advanced data analytics techniques. Finally, we argue how the present IDS attack classes can be extended to match the modern attacks and propose three new classes regarding the outgoing network communicatio

    A Survey of Using Machine Learning in IoT Security and the Challenges Faced by Researchers

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    The Internet of Things (IoT) has become more popular in the last 15 years as it has significantly improved and gained control in multiple fields. We are nowadays surrounded by billions of IoT devices that directly integrate with our lives, some of them are at the center of our homes, and others control sensitive data such as military fields, healthcare, and datacenters, among others. This popularity makes factories and companies compete to produce and develop many types of those devices without caring about how secure they are. On the other hand, IoT is considered a good insecure environment for cyber thefts. Machine Learning (ML) and Deep Learning (DL) also gained more importance in the last 15 years; they achieved success in the networking security field too. IoT has some similar security requirements such as traditional networks, but with some differences according to its characteristics, some specific security features, and environmental limitations, some differences are made such as low energy resources, limited computational capability, and small memory. These limitations inspire some researchers to search for the perfect and lightweight security ways which strike a balance between performance and security. This survey provides a comprehensive discussion about using machine learning and deep learning in IoT devices within the last five years. It also lists the challenges faced by each model and algorithm. In addition, this survey shows some of the current solutions and other future directions and suggestions. It also focuses on the research that took the IoT environment limitations into consideration

    Reduction of False Positives in Intrusion Detection Based on Extreme Learning Machine with Situation Awareness

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    Protecting computer networks from intrusions is more important than ever for our privacy, economy, and national security. Seemingly a month does not pass without news of a major data breach involving sensitive personal identity, financial, medical, trade secret, or national security data. Democratic processes can now be potentially compromised through breaches of electronic voting systems. As ever more devices, including medical machines, automobiles, and control systems for critical infrastructure are increasingly networked, human life is also more at risk from cyber-attacks. Research into Intrusion Detection Systems (IDSs) began several decades ago and IDSs are still a mainstay of computer and network protection and continue to evolve. However, detecting previously unseen, or zero-day, threats is still an elusive goal. Many commercial IDS deployments still use misuse detection based on known threat signatures. Systems utilizing anomaly detection have shown great promise to detect previously unseen threats in academic research. But their success has been limited in large part due to the excessive number of false positives that they produce. This research demonstrates that false positives can be better minimized, while maintaining detection accuracy, by combining Extreme Learning Machine (ELM) and Hidden Markov Models (HMM) as classifiers within the context of a situation awareness framework. This research was performed using the University of New South Wales - Network Based 2015 (UNSW-NB15) data set which is more representative of contemporary cyber-attack and normal network traffic than older data sets typically used in IDS research. It is shown that this approach provides better results than either HMM or ELM alone and with a lower False Positive Rate (FPR) than other comparable approaches that also used the UNSW-NB15 data set

    Detecting Abnormal Social Robot Behavior through Emotion Recognition

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    Sharing characteristics with both the Internet of Things and the Cyber Physical Systems categories, a new type of device has arrived to claim a third category and raise its very own privacy concerns. Social robots are in the market asking consumers to become part of their daily routine and interactions. Ranging in the level and method of communication with the users, all social robots are able to collect, share and analyze a great variety and large volume of personal data.In this thesis, we focus the community’s attention to this emerging area of interest for privacy and security research. We discuss the likely privacy issues, comment on current defense mechanisms that are applicable to this new category of devices, outline new forms of attack that are made possible through social robots, highlight paths that research on consumer perceptions could follow, and propose a system for detecting abnormal social robot behavior based on emotion detection

    Umělá inteligence v kybernetické bezpečnosti

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    Artifcial intelligence (AI) and machine learning (ML) have grown rapidly in recent years, and their applications in practice can be seen in many felds, ranging from facial recognition to image analysis. Recent developments in Artificial intelligence have a vast transformative potential for both cybersecurity defenders and cybercriminals. Anti-malware solutions adopt intelligent techniques to detect and prevent threats to the digital space. In contrast, cybercriminals are aware of the new prospects too and likely to adapt AI techniques to their operations. This thesis presents advances made so far in the field of applying AI techniques in cybersecurity for combating against cyber threats, to demonstrate how this promising technology can be a useful tool for detection and prevention of cyberattacks. Furthermore, the research examines how transnational criminal organizations and cybercriminals may leverage developing AI technology to conduct more sophisticated criminal activities. Next, the research outlines the possible dynamic new kind of malware, called X-Ware and X-sWarm, which simulates the swarm system behaviour and integrates the neural network to operate more efficiently as a background for the forthcoming anti-malware solution. This research proposes how to record and visualize the behaviour of these type of malware when it propagates through the file system, computer network (virus process is known) or by observed data analysis (virus process is not known and we observe only the data from the system). Finally, a paradigm of an anti-malware solution, named Multi agent antivirus system has been proposed in the thesis that gives the insight to develop a more robust, adaptive and flexible defence system.Význam umělé inteligence (AI) a strojového učení (ML) v posledních letech rychle rostl a na jejich aplikacích lze vidět, že v mnoha oblastech, od rozpoznávání obličeje až po analýzu obrazu, byl učiněn velký pokrok. Poslední vývoj v oblasti umělé inteligence má obrovský potenciál jak pro obránce v oblasti kybernetické bezpečnosti, tak pro ůtočníky. AI se stává řešením v otázce obrany proti modernímu malware a hraje tak důležitou roli v detekci a prevenci hrozeb v digitálním prostoru. Naproti tomu kyberzločinci jsou si vědomi nových vyhlídek ve spojení s AI a pravděpodobně přizpůsobí tyto techniky novým generacím malware, vektorům útoku a celkově jejich operacím. Tato práce představuje dosavadní pokroky aplikace technik AI v oblasti kybernetické bezpečnosti. V této oblasti tzn. v boji proti kybernetickým hrozbám se ukázuje jako slibná technologie a užitečný nástroj pro detekci a prevenci kybernetických útoků. V práci si rovněž pokládme otázku, jak mohou nadnárodní zločinecké organizace a počítačoví zločinci využít vyvíjející se technologii umělé inteligence k provádění sofistikovanějších trestných činností. Konečně, výzkum nastíní možný nový druh malware, nazvaný X-Ware, který simuluje chování hejnového systému a integruje neuronovou síť tak, aby fungovala efektivněji a tak se celý X-Ware a X-sWarm dal použít nejen jako kybernetická zbraň na útok, ale i jako antivirové obranné řešení. Tento výzkum navrhuje, jak zaznamenat a vizualizovat chování X-Ware, když se šíří prostřednictvím systému souborů, sítí a to jak analýzou jeho dynamiky (proces je znám), tak analýzou dat (proces není znám, pozorujeme jen data). Nakonec bylo v disertační práci navrženo paradigma řešení proti malwaru, jež bylo nazváno „Multi agent antivirus system“. Tato práce tedy poskytuje pohled na vývoj robustnějšího, adaptivnějšího a flexibilnějšího obranného systému.460 - Katedra informatikyvyhově
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