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Register a new userFrom Cyber Terrorism to Cyber Peacekeeping: Are we there yet?
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Added by
Leandros Maglaras A
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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In Cyberspace nowadays, there is a burst of information that everyone has access. However, apart from the advantages the Internet offers, it also hides numerous dangers for both people and nations. Cyberspace has a dark side, including terrorism, bullying, and other types of violence. Cyberwarfare is a kind of virtual war that causes the same destruction that a physical war would also do. In this article, we discuss what Cyberterrorism is and how it can lead to Cyberwarfare.
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Peacekeeping is a noble and essential activity, helping to bring peace to conflict torn areas and providing security to millions of people around the world. Peacekeepers operate in all domains of war: buffer zones on land, no fly zones in the air and ensuring free passage at sea. With the emergence of cyberspace as a domain of war, questions on the role of peacekeeping in this domain naturally arise. There is extensive research around the topic of cyber warfare, but surprisingly little on how to restore and maintain peace in its aftermath. This is a significant gap which needs addressing. We begin by providing an overview of peacekeeping, describing its overarching goals and principles, using the United Nations model as a reference. We then review existing literature on cyber peacekeeping. The paper progresses to discuss the question of whether cyber peacekeeping is needed, and if so, if it is a plausible concept. We explore some ideas on how cyber peacekeeping could be performed and the challenges cyber peacekeepers will face, before making suggestions on where future work should be focused.
Cyber peacekeeping is an emerging and multi-disciplinary field of research, touching upon technical, political and societal domains of thought. In this article we build upon previous works by developing the cyber peacekeeping activity of observation, monitoring and reporting. We take a practical approach: describing a scenario in which two countries request UN support in drawing up and overseeing a ceasefire which includes cyber terms. We explore how a cyber peacekeeping operation could start up and discuss the challenges it will face. The article makes a number of proposals, including the use of a virtual collaborative environment to bring multiple benefits. We conclude by summarising our findings, and describing where further work lies.
The United Nations conducts peace operations around the world, aiming tomaintain peace and security in conflict torn areas. Whilst early operations werelargely successful, the changing nature of warfare and conflict has often left peaceoperations strugglingto adapt. In this article, we make a contribution towardsefforts to plan for the next evolution in both intra and inter-state conflict: cyberwarfare. It is now widely accepted that cyber warfare will be a component offuture conflicts, and much researchhas been devoted to how governments andmilitaries can prepare for and fight in this new domain [1]. Despite the vastamount of research relating to cyber warfare, there has been less discussion onits impact towards successful peace operations. This is agap in knowledge thatis important to address, since the restoration of peace following conflict of anykind is of global importance. It is however a complex topic requiring discussionacross multiple domains. Input from the technical, political, governmental andsocietal domains are critical in forming the concept of cyber peacekeeping.Previous work on this topic has sought to define the concept of cyber peacekeeping[2, 3, 4]. We build upon this work by exploring the practicalities ofstarting up a cyber peacekeeping component and setting up a Cyber Buffer Zone (CBZ).
Embodied instruction following is a challenging problem requiring an agent to infer a sequence of primitive actions to achieve a goal environment state from complex language and visual inputs. Action Learning From Realistic Environments and Directives (ALFRED) is a recently proposed benchmark for this problem consisting of step-by-step natural language instructions to achieve subgoals which compose to an ultimate high-level goal. Key challenges for this task include localizing target locations and navigating to them through visual inputs, and grounding language instructions to visual appearance of objects. To address these challenges, in this study, we augment the agents field of view during navigation subgoals with multiple viewing angles, and train the agent to predict its relative spatial relation to the target location at each timestep. We also improve language grounding by introducing a pre-trained object detection module to the model pipeline. Empirical studies show that our approach exceeds the baseline model performance.
Automated detection of software vulnerabilities is a fundamental problem in software security. Existing program analysis techniques either suffer from high false positives or false negatives. Recent progress in Deep Learning (DL) has resulted in a surge of interest in applying DL for automated vulnerability detection. Several recent studies have demonstrated promising results achieving an accuracy of up to 95% at detecting vulnerabilities. In this paper, we ask, how well do the state-of-the-art DL-based techniques perform in a real-world vulnerability prediction scenario?. To our surprise, we find that their performance drops by more than 50%. A systematic investigation of what causes such precipitous performance drop reveals that existing DL-based vulnerability prediction approaches suffer from challenges with the training data (e.g., data duplication, unrealistic distribution of vulnerable classes, etc.) and with the model choices (e.g., simple token-based models). As a result, these approaches often do not learn features related to the actual cause of the vulnerabilities. Instead, they learn unrelated artifacts from the dataset (e.g., specific variable/function names, etc.). Leveraging these empirical findings, we demonstrate how a more principled approach to data collection and model design, based on realistic settings of vulnerability prediction, can lead to better solutions. The resulting tools perform significantly better than the studied baseline: up to 33.57% boost in precision and 128.38% boost in recall compared to the best performing model in the literature. Overall, this paper elucidates existing DL-based vulnerability prediction systems potential issues and draws a roadmap for future DL-based vulnerability prediction research. In that spirit, we make available all the artifacts supporting our results: https://git.io/Jf6IA.
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