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Stranger Danger: A Mother's Perspective
Priscilla Salas gives a mother's perspective on the stranger danger misconception.
Journal Article
The silhouette girl
Pru Dunning has everything she ever wanted: a successful boyfriend, a thriving nursing career, and a truly comfortable life. But then the strange voicemails start. Scarletta, the woman calls herself. She seems to know Pru, although Pru certainly doesn't know that name, nor does she recognize the glamorous voice leaving her poisonous messages. Is this the work of jealous revenge from someone at work? An old enemy she has forgotten about? Pru begins to investigate, but carefully--if anyone found out about these lewd, threatening messages, filled with details that no stranger could possibly know, they might suspect that she is something other than an innocent victim. But when she suddenly becomes a person of interest in a murder case, it feels like Scarletta's toxic voice, lashing out from the shadows, will silence all beauty in Pru's perfect life, once and for all.-- Amazon.
Book
Semi-Supervised Cyber Threat Identification in Dark Net Markets: A Transductive and Deep Learning Approach
Dark Net Marketplaces (DNMs), online selling platforms on the dark web, constitute a major component of the underground economy. Due to the anonymity and increasing accessibility of these platforms, they are rich sources of cyber threats such as hacking tools, data breaches, and personal account information. As the number of products offered on DNMs increases, researchers have begun to develop automated machine learning-based threat identification approaches. A major challenge in adopting such an approach is that the task typically requires manually labeled training data, which is expensive and impractical. We propose a novel semi-supervised labeling technique for leveraging unlabeled data based on the lexical and structural characteristics of DNMs using transductive learning. Empirical results show that the proposed approach leads to an approximately 3-5% increase in classification performance measured by F
1
-score, while increasing both precision and recall. To further improve the identification performance, we adopt Long Short-Term Memory (LSTM) as a deep learning structure on top of the proposed labeling method. The results are evaluated against a large collection of 79K product listings obtained from the most popular DNMs. Our method outperforms the state-of-the-art methods in threat identification and is considered as an important step toward lowering the human supervision cost in realizing automated threat detection within cyber threat intelligence organizations.
Journal Article
Roots of war : wanting power, seeing threat, justifying force
\"Roots of War presents systematic archival, experimental, and survey research on three psychological factors leading to war--desire for power, exaggerated perception of threat, and justification for force -- set in comparative historical accounts of the unexpected 1914 escalation to world war and the peacefully - resolved 1962 Cuban Missile Crisis.\"--Provided by publisher.
Book
This is how D.C. looks like the day before Biden’s inauguration
On the eve of the presidential inauguration, downtown Washington, D.C., looked nearly barren with the exception of a few protestors and military security.
Streaming Video
The Paris diversion : a novel
\"American expat Kate Moore drops her kids at the international school, makes her rounds of chores, and meets her husband Dexter at their regular cafâe. Across the Seine, tech CEO Hunter Forsyth stands on his balcony, wondering why his police escort just departed, and frustrated that his cell service has cut out. And on the nearby rue de Rivoli, Mahmoud Khalid climbs out of a van, elbows his way into the museum courtyard, sets down his metal briefcase, and removes his windbreaker. Mahmoud is planning to die today...and he won't be the only one.\"--Provided by publisher.
Book
PRIORITI: scoring and categorization-based threat prioritization
The threat alert fatigue or alert overload problem has become critical in recent years. In practice, the volume of threat alerts is higher than the volume of alerts that SOC analysts can investigate. In this paper, we propose “Threat Inspection and Prioritization (PRIORITI),” a threat inspection mechanism that derives threat intelligence from the threat alert for prioritizing investigation. PRIORITI works in three phases, the first phase computes MITRE techniques, which act as a base layer for threat scoring and categorization. The second phase of PRIORITI maps the threat technique to CAPEC attack patterns and derives the scoring metrics. We further propose a novel threat scoring mechanism based on the derived metrics for threat score computation. The third phase of PRIORITI maps each MITRE technique to a single category from Microsoft’s STRIDE framework. Finally, threat score and category are used to prioritize the threat alerts. We evaluated PRIORITI on 7.6 million alerts from the DARPA dataset. It maps these alerts to 21 unique MITRE techniques and computes the threat scores and categories. From the aforementioned results, PRIORITI prioritizes 1.27% (i.e., 96703 out of 7.6 million) of captured alerts as critical by processing an average of 1 million alerts within ≈ 20 s. In addition, PRIORITI provides additional insights to the SOC analysts to investigate the threat alerts, which improves the time taken to respond to threats after detection. Through this effort, PRIORITI improves the productivity of the SOC analysts and provides a significant contribution to handle the “threat alert fatigue.”
Journal Article
The scarlet slipper mystery
Nancy Drew comes to the aid of the owners of a local dancing school when they receive an anonymous note threatening their lives.
Book
An overview of implementing security and privacy in federated learning
Federated learning has received a great deal of research attention recently,with privacy protection becoming a key factor in the development of artificial intelligence. Federated learning is a special kind of distributed learning framework, which allows multiple users to participate in model training while ensuring that their privacy is not compromised; however, this paradigm is still vulnerable to security and privacy threats from various attackers. This paper focuses on the security and privacy threats related to federated learning. First, we analyse the current research and development status of federated learning through use of the CiteSpace literature search tool. Next, we describe the basic concepts and threat models, and then analyse the security and privacy vulnerabilities within current federated learning architectures. Finally, the directions of development in this area are further discussed in the context of current advanced defence solutions, for which we provide a summary and comparison.
Journal Article